20181026 Portfolio and Y123 feature 사용.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio Generate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('raw_data_20181008.pickle', 'rb') as handle:\n",
    "    raw_data = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['AK홀딩스', 'BGF', 'BGF리테일', 'BNK금융지주', 'CJ', 'CJ CGV', 'CJ대한통운', 'CJ제일제당', 'DB손해보험', 'DB하이텍', 'GKL', 'GS', 'GS건설', 'GS리테일', 'HDC', 'HDC현대산업개발', 'JW중외제약', 'JW홀딩스', 'KB금융', 'KCC', 'KT', 'KT&G', 'LF', 'LG', 'LG디스플레이', 'LG상사', 'LG생활건강', 'LG유플러스', 'LG이노텍', 'LG전자', 'LG하우시스', 'LG화학', 'LIG넥스원', 'LS', 'LS산전', 'NAVER', 'NH투자증권', 'OCI', 'POSCO', 'S&T모티브', 'S-Oil', 'SBS', 'SK', 'SKC', 'SK네트웍스', 'SK디스커버리', 'SK이노베이션', 'SK케미칼', 'SK텔레콤', 'SK하이닉스', 'SPC삼립', '강원랜드', '고려아연', '고려제강', '금호석유', '금호타이어', '기아차', '기업은행', '남양유업', '남해화학', '넥센타이어', '넷마블', '녹십자', '녹십자홀딩스', '농심', '대교', '대덕전자', '대림산업', '대상', '대우건설', '대우조선해양', '대웅제약', '대한유화', '대한항공', '동국제강', '동서', '동아쏘시오홀딩스', '동아에스티', '동양', '동원F&B', '동원시스템즈', '두산', '두산밥캣', '두산인프라코어', '두산중공업', '락앤락', '롯데쇼핑', '롯데정밀화학', '롯데지주', '롯데칠성', '롯데케미칼', '롯데푸드', '롯데하이마트', '만도', '메리츠종금증권', '무학', '미래에셋대우', '보령제약', '부광약품', '빙그레', '삼성SDI', '삼성물산', '삼성바이오로직스', '삼성생명', '삼성에스디에스', '삼성엔지니어링', '삼성전기', '삼성전자', '삼성중공업', '삼성증권', '삼성카드', '삼성화재', '삼양사', '삼양홀딩스', '세방전지', '세아베스틸', '세아제강', '셀트리온', '신세계', '신한지주', '쌍용양회', '쌍용차', '아모레G', '아모레퍼시픽', '아이에스동서', '에스엘', '에스원', '엔씨소프트', '영원무역', '영진약품', '영풍', '오뚜기', '오리온', '오리온홀딩스', '우리은행', '유니드', '유한양행', '이노션', '이마트', '일양약품', '일진머티리얼즈', '제일기획', '종근당', '카카오', '코스맥스', '코오롱인더', '코웨이', '쿠쿠홀딩스', '태광산업', '팜스코', '팬오션', '포스코대우', '풍산', '하나금융지주', '하이트진로', '한국가스공사', '한국금융지주', '한국단자', '한국쉘석유', '한국전력', '한국콜마', '한국타이어', '한국타이어월드와이드', '한국항공우주', '한라홀딩스', '한미사이언스', '한미약품', '한샘', '한섬', '한세실업', '한솔케미칼', '한온시스템', '한올바이오파마', '한일시멘트', '한전KPS', '한전기술', '한진중공업', '한화', '한화생명', '한화에어로스페이스', '한화케미칼', '현대건설', '현대그린푸드', '현대글로비스', '현대로템', '현대리바트', '현대모비스', '현대미포조선', '현대백화점', '현대엘리베이', '현대위아', '현대제철', '현대중공업', '현대중공업지주', '현대차', '현대해상', '현대홈쇼핑', '호텔신라', '효성', '효성중공업', '후성', '휴켐스'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BGF\n",
      "BGF리테일\n",
      "HDC현대산업개발\n",
      "LIG넥스원\n",
      "SK케미칼\n",
      "넷마블\n",
      "동아에스티\n",
      "두산밥캣\n",
      "만도\n",
      "삼성물산\n",
      "삼성바이오로직스\n",
      "삼성에스디에스\n",
      "오리온\n",
      "이노션\n",
      "종근당\n",
      "코스맥스\n",
      "쿠쿠홀딩스\n",
      "현대로템\n",
      "현대중공업지주\n",
      "효성중공업\n"
     ]
    }
   ],
   "source": [
    "copy_keys = list(raw_data.keys())\n",
    "for stock in copy_keys:\n",
    "    if len(raw_data[stock].loc[:'2013-01-01']) < 1:\n",
    "        del raw_data[stock]\n",
    "        print(stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2010-01-04</th>\n",
       "      <td>16060.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16180.0</td>\n",
       "      <td>5.953252</td>\n",
       "      <td>11950800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>16520.0</td>\n",
       "      <td>16580.0</td>\n",
       "      <td>16300.0</td>\n",
       "      <td>16440.0</td>\n",
       "      <td>6.048919</td>\n",
       "      <td>27925850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>16580.0</td>\n",
       "      <td>16820.0</td>\n",
       "      <td>16520.0</td>\n",
       "      <td>16820.0</td>\n",
       "      <td>6.188735</td>\n",
       "      <td>22948850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>16820.0</td>\n",
       "      <td>16820.0</td>\n",
       "      <td>16260.0</td>\n",
       "      <td>16260.0</td>\n",
       "      <td>5.982689</td>\n",
       "      <td>22107950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>16400.0</td>\n",
       "      <td>16420.0</td>\n",
       "      <td>16120.0</td>\n",
       "      <td>16420.0</td>\n",
       "      <td>6.041558</td>\n",
       "      <td>14777550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2010-01-04  16060.0  16180.0  16000.0  16180.0   5.953252  11950800\n",
       "2010-01-05  16520.0  16580.0  16300.0  16440.0   6.048919  27925850\n",
       "2010-01-06  16580.0  16820.0  16520.0  16820.0   6.188735  22948850\n",
       "2010-01-07  16820.0  16820.0  16260.0  16260.0   5.982689  22107950\n",
       "2010-01-08  16400.0  16420.0  16120.0  16420.0   6.041558  14777550"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['삼성전자'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stock에서 Volume==0인 index, stock에서 제외\n",
    "for key, df in raw_data.items():\n",
    "    raw_data[key] = df.drop(df.index[np.where(df.Volume==0)[0]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature 생성\n",
    "for key, df in raw_data.items():\n",
    "    df[\"pct_change\"] = df.Close.pct_change()\n",
    "    for i in range(3, 31, 2):\n",
    "        df[\"close_ma_{}\".format(i)] = df.Close.rolling(window = i).mean()\n",
    "        df[\"volume_ma_{}\".format(i)] = df.Volume.rolling(window = i).mean()\n",
    "    for i in [7, 21, 30, 60, 90, 91, 100, 120]:\n",
    "        df[\"his_vol_{}\".format(i)] = df[\"pct_change\"].rolling(window = i).std()*(252**0.5)\n",
    "    for i in [1, 3, 5, 7, 10, 12, 14]:\n",
    "        df['returnsCC{}'.format(i) ]= np.log(df.Open / df.Close.shift(i))\n",
    "        df['returnsOO{}'.format(i) ] = np.log(df.Open / df.Open.shift(i))\n",
    "        df['returnsCC{}'.format(i) ]= np.log(df.Close / df.Close.shift(i))\n",
    "        df['returnsOC{}'.format(i) ] = np.log(df.Close / df.Open.shift(i))\n",
    "    for i in [1, 3, 5, 7, 10]:\n",
    "        df['Y3_{}'.format(i)] = np.where(df.Close.rolling(i).mean() - df.Close.shift(1).rolling(i).mean()>=0,1,0)\n",
    "        for shift in [1, 2, 3, 4, 5]:\n",
    "            df['sh{}_Y3_{}'.format(shift, i)] = df['Y3_{}'.format(i)].shift(-1*shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={}\n",
    "validation_={}\n",
    "for stock, df in raw_data.items():\n",
    "    data[stock] = df.loc['2013-07-01':'2018-07-01']\n",
    "    validation_[stock] = df.loc['2018-06-20':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>close_ma_3</th>\n",
       "      <th>volume_ma_3</th>\n",
       "      <th>close_ma_5</th>\n",
       "      <th>...</th>\n",
       "      <th>sh2_Y3_7</th>\n",
       "      <th>sh3_Y3_7</th>\n",
       "      <th>sh4_Y3_7</th>\n",
       "      <th>sh5_Y3_7</th>\n",
       "      <th>Y3_10</th>\n",
       "      <th>sh1_Y3_10</th>\n",
       "      <th>sh2_Y3_10</th>\n",
       "      <th>sh3_Y3_10</th>\n",
       "      <th>sh4_Y3_10</th>\n",
       "      <th>sh5_Y3_10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>26680.0</td>\n",
       "      <td>26920.0</td>\n",
       "      <td>26440.0</td>\n",
       "      <td>26520.0</td>\n",
       "      <td>36.487026</td>\n",
       "      <td>12825950</td>\n",
       "      <td>-0.011923</td>\n",
       "      <td>26713.333333</td>\n",
       "      <td>1.984778e+07</td>\n",
       "      <td>26260.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-02</th>\n",
       "      <td>26800.0</td>\n",
       "      <td>27040.0</td>\n",
       "      <td>26600.0</td>\n",
       "      <td>26700.0</td>\n",
       "      <td>36.734673</td>\n",
       "      <td>10214450</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>26686.666667</td>\n",
       "      <td>1.466722e+07</td>\n",
       "      <td>26412.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-03</th>\n",
       "      <td>26680.0</td>\n",
       "      <td>26780.0</td>\n",
       "      <td>25900.0</td>\n",
       "      <td>26020.0</td>\n",
       "      <td>35.799110</td>\n",
       "      <td>12822050</td>\n",
       "      <td>-0.025468</td>\n",
       "      <td>26413.333333</td>\n",
       "      <td>1.195415e+07</td>\n",
       "      <td>26572.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-04</th>\n",
       "      <td>26040.0</td>\n",
       "      <td>26340.0</td>\n",
       "      <td>25920.0</td>\n",
       "      <td>26340.0</td>\n",
       "      <td>36.239376</td>\n",
       "      <td>9042650</td>\n",
       "      <td>0.012298</td>\n",
       "      <td>26353.333333</td>\n",
       "      <td>1.069305e+07</td>\n",
       "      <td>26484.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2013-07-05</th>\n",
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       "               Open     High      Low    Close  Adj Close    Volume  \\\n",
       "Date                                                                  \n",
       "2013-07-01  26680.0  26920.0  26440.0  26520.0  36.487026  12825950   \n",
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       "2013-07-05  25720.0  26300.0  25200.0  25340.0  34.863544  30382800   \n",
       "\n",
       "            pct_change    close_ma_3   volume_ma_3  close_ma_5    ...      \\\n",
       "Date                                                              ...       \n",
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       "2013-07-04    0.012298  26353.333333  1.069305e+07     26484.0    ...       \n",
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       "            sh2_Y3_7  sh3_Y3_7  sh4_Y3_7  sh5_Y3_7  Y3_10  sh1_Y3_10  \\\n",
       "Date                                                                   \n",
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       "[5 rows x 94 columns]"
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       "      <th>2018-06-26</th>\n",
       "      <td>45900.0</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>45900.0</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>46653.011719</td>\n",
       "      <td>11223166</td>\n",
       "      <td>0.007503</td>\n",
       "      <td>46966.666667</td>\n",
       "      <td>1.070296e+07</td>\n",
       "      <td>46990.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close     Adj Close    Volume  \\\n",
       "Date                                                                     \n",
       "2018-06-20  47450.0  47600.0  46850.0  47000.0  46653.011719  12959316   \n",
       "2018-06-21  47900.0  47900.0  47050.0  47050.0  46702.644531  10015541   \n",
       "2018-06-22  47000.0  47250.0  46200.0  47250.0  46901.167969  10298006   \n",
       "2018-06-25  47050.0  47050.0  46150.0  46650.0  46305.597656  10587711   \n",
       "2018-06-26  45900.0  47300.0  45900.0  47000.0  46653.011719  11223166   \n",
       "\n",
       "            pct_change    close_ma_3   volume_ma_3  close_ma_5    ...      \\\n",
       "Date                                                              ...       \n",
       "2018-06-20    0.000000  46866.666667  1.494639e+07     47290.0    ...       \n",
       "2018-06-21    0.001064  47016.666667  1.273094e+07     47060.0    ...       \n",
       "2018-06-22    0.004251  47100.000000  1.109095e+07     46980.0    ...       \n",
       "2018-06-25   -0.012698  46983.333333  1.030042e+07     46990.0    ...       \n",
       "2018-06-26    0.007503  46966.666667  1.070296e+07     46990.0    ...       \n",
       "\n",
       "            sh2_Y3_7  sh3_Y3_7  sh4_Y3_7  sh5_Y3_7  Y3_10  sh1_Y3_10  \\\n",
       "Date                                                                   \n",
       "2018-06-20       0.0       0.0       0.0       1.0      0        0.0   \n",
       "2018-06-21       0.0       0.0       1.0       0.0      0        0.0   \n",
       "2018-06-22       0.0       1.0       0.0       0.0      0        0.0   \n",
       "2018-06-25       1.0       0.0       0.0       0.0      0        0.0   \n",
       "2018-06-26       0.0       0.0       0.0       0.0      0        0.0   \n",
       "\n",
       "            sh2_Y3_10  sh3_Y3_10  sh4_Y3_10  sh5_Y3_10  \n",
       "Date                                                    \n",
       "2018-06-20        0.0        0.0        0.0        0.0  \n",
       "2018-06-21        0.0        0.0        0.0        0.0  \n",
       "2018-06-22        0.0        0.0        0.0        0.0  \n",
       "2018-06-25        0.0        0.0        0.0        0.0  \n",
       "2018-06-26        0.0        0.0        0.0        0.0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_['삼성전자'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run moon_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = multi_train_test_split(data, 0.8, dtrain=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>close_ma_5</th>\n",
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       "      <th>2017-06-13</th>\n",
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       "      <th>2017-06-14</th>\n",
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       "    <tr>\n",
       "      <th>2017-06-15</th>\n",
       "      <td>45680.0</td>\n",
       "      <td>45920.0</td>\n",
       "      <td>45180.0</td>\n",
       "      <td>45680.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-16</th>\n",
       "      <td>45500.0</td>\n",
       "      <td>45940.0</td>\n",
       "      <td>45460.0</td>\n",
       "      <td>45580.0</td>\n",
       "      <td>11768.463867</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-19</th>\n",
       "      <td>45580.0</td>\n",
       "      <td>46560.0</td>\n",
       "      <td>45560.0</td>\n",
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       "<p>5 rows × 94 columns</p>\n",
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       "               Open     High      Low    Close     Adj Close    Volume  \\\n",
       "Date                                                                     \n",
       "2017-06-13  45140.0  45620.0  45140.0  45400.0  11721.989258   8624900   \n",
       "2017-06-14  45800.0  46060.0  45240.0  45360.0  11711.661133  10166700   \n",
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       "2017-06-19  45580.0  46560.0  45560.0  46560.0  12021.494141  11303900   \n",
       "\n",
       "            pct_change    close_ma_3   volume_ma_3  close_ma_5    ...      \\\n",
       "Date                                                              ...       \n",
       "2017-06-13    0.000441  45626.666667  1.043735e+07     45468.0    ...       \n",
       "2017-06-14   -0.000881  45380.000000  9.915300e+06     45480.0    ...       \n",
       "2017-06-15    0.007055  45480.000000  9.482867e+06     45584.0    ...       \n",
       "2017-06-16   -0.002189  45540.000000  1.240905e+07     45480.0    ...       \n",
       "2017-06-19    0.021501  45940.000000  1.278812e+07     45716.0    ...       \n",
       "\n",
       "            sh2_Y3_7  sh3_Y3_7  sh4_Y3_7  sh5_Y3_7  Y3_10  sh1_Y3_10  \\\n",
       "Date                                                                   \n",
       "2017-06-13       0.0       1.0       1.0       1.0      0        1.0   \n",
       "2017-06-14       1.0       1.0       1.0       1.0      1        1.0   \n",
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       "Date                                                    \n",
       "2017-06-13        1.0        1.0        1.0        1.0  \n",
       "2017-06-14        1.0        1.0        1.0        1.0  \n",
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       "2017-06-19        1.0        1.0        1.0        1.0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['삼성전자'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-06-20</th>\n",
       "      <td>47240.0</td>\n",
       "      <td>48140.0</td>\n",
       "      <td>47220.0</td>\n",
       "      <td>48140.0</td>\n",
       "      <td>12429.440430</td>\n",
       "      <td>15045000</td>\n",
       "      <td>0.033935</td>\n",
       "      <td>46760.000000</td>\n",
       "      <td>1.458412e+07</td>\n",
       "      <td>46264.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>2017-06-21</th>\n",
       "      <td>47740.0</td>\n",
       "      <td>48120.0</td>\n",
       "      <td>47480.0</td>\n",
       "      <td>47480.0</td>\n",
       "      <td>12259.031250</td>\n",
       "      <td>9973650</td>\n",
       "      <td>-0.013710</td>\n",
       "      <td>47393.333333</td>\n",
       "      <td>1.210752e+07</td>\n",
       "      <td>46688.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-22</th>\n",
       "      <td>47960.0</td>\n",
       "      <td>48080.0</td>\n",
       "      <td>47720.0</td>\n",
       "      <td>47960.0</td>\n",
       "      <td>12382.965820</td>\n",
       "      <td>11455800</td>\n",
       "      <td>0.010110</td>\n",
       "      <td>47860.000000</td>\n",
       "      <td>1.215815e+07</td>\n",
       "      <td>47144.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-23</th>\n",
       "      <td>47600.0</td>\n",
       "      <td>47780.0</td>\n",
       "      <td>47420.0</td>\n",
       "      <td>47620.0</td>\n",
       "      <td>12295.179688</td>\n",
       "      <td>9515100</td>\n",
       "      <td>-0.007089</td>\n",
       "      <td>47686.666667</td>\n",
       "      <td>1.031485e+07</td>\n",
       "      <td>47552.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-26</th>\n",
       "      <td>47520.0</td>\n",
       "      <td>48360.0</td>\n",
       "      <td>47520.0</td>\n",
       "      <td>48280.0</td>\n",
       "      <td>12465.586914</td>\n",
       "      <td>8552800</td>\n",
       "      <td>0.013860</td>\n",
       "      <td>47953.333333</td>\n",
       "      <td>9.841233e+06</td>\n",
       "      <td>47896.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close     Adj Close    Volume  \\\n",
       "Date                                                                     \n",
       "2017-06-20  47240.0  48140.0  47220.0  48140.0  12429.440430  15045000   \n",
       "2017-06-21  47740.0  48120.0  47480.0  47480.0  12259.031250   9973650   \n",
       "2017-06-22  47960.0  48080.0  47720.0  47960.0  12382.965820  11455800   \n",
       "2017-06-23  47600.0  47780.0  47420.0  47620.0  12295.179688   9515100   \n",
       "2017-06-26  47520.0  48360.0  47520.0  48280.0  12465.586914   8552800   \n",
       "\n",
       "            pct_change    close_ma_3   volume_ma_3  close_ma_5    ...      \\\n",
       "Date                                                              ...       \n",
       "2017-06-20    0.033935  46760.000000  1.458412e+07     46264.0    ...       \n",
       "2017-06-21   -0.013710  47393.333333  1.210752e+07     46688.0    ...       \n",
       "2017-06-22    0.010110  47860.000000  1.215815e+07     47144.0    ...       \n",
       "2017-06-23   -0.007089  47686.666667  1.031485e+07     47552.0    ...       \n",
       "2017-06-26    0.013860  47953.333333  9.841233e+06     47896.0    ...       \n",
       "\n",
       "            sh2_Y3_7  sh3_Y3_7  sh4_Y3_7  sh5_Y3_7  Y3_10  sh1_Y3_10  \\\n",
       "Date                                                                   \n",
       "2017-06-20       1.0       1.0       1.0       1.0      1        1.0   \n",
       "2017-06-21       1.0       1.0       1.0       1.0      1        1.0   \n",
       "2017-06-22       1.0       1.0       1.0       0.0      1        1.0   \n",
       "2017-06-23       1.0       1.0       0.0       1.0      1        1.0   \n",
       "2017-06-26       1.0       0.0       1.0       0.0      1        1.0   \n",
       "\n",
       "            sh2_Y3_10  sh3_Y3_10  sh4_Y3_10  sh5_Y3_10  \n",
       "Date                                                    \n",
       "2017-06-20        1.0        1.0        1.0        1.0  \n",
       "2017-06-21        1.0        1.0        1.0        1.0  \n",
       "2017-06-22        1.0        1.0        1.0        1.0  \n",
       "2017-06-23        1.0        1.0        1.0        1.0  \n",
       "2017-06-26        1.0        1.0        1.0        1.0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['삼성전자'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "alg = XGBClassifier(\n",
    " learning_rate =0.15,\n",
    " n_estimators=1000,\n",
    " max_depth=5,\n",
    " min_child_weight=2,\n",
    " gamma=0,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'binary:logistic',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_li = [i for i in data['삼성전자'].columns if i.find('sh') != -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sh1_Y3_1', 'sh2_Y3_1', 'sh3_Y3_1', 'sh4_Y3_1', 'sh5_Y3_1', 'sh1_Y3_3', 'sh2_Y3_3', 'sh3_Y3_3', 'sh4_Y3_3', 'sh5_Y3_3', 'sh1_Y3_5', 'sh2_Y3_5', 'sh3_Y3_5', 'sh4_Y3_5', 'sh5_Y3_5', 'sh1_Y3_7', 'sh2_Y3_7', 'sh3_Y3_7', 'sh4_Y3_7', 'sh5_Y3_7', 'sh1_Y3_10', 'sh2_Y3_10', 'sh3_Y3_10', 'sh4_Y3_10', 'sh5_Y3_10']\n"
     ]
    }
   ],
   "source": [
    "print(y_li)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run xgb_modelfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "\n",
    "def modelfit(alg, train, test, X, y):\n",
    "    alg = copy.deepcopy(alg)\n",
    "    xgb_params = alg.get_xgb_params()\n",
    "    xgtrain = xgb.DMatrix(train[X].values, label=train[y].values)\n",
    "    cvresult=xgb.cv(xgb_params, xgtrain,\n",
    "                    num_boost_round=alg.get_params()['n_estimators'],\n",
    "                    nfold=5, early_stopping_rounds=50,\n",
    "                    show_stdv=False)\n",
    "    alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    alg.fit(train[X], train[y], eval_metric='auc')\n",
    "    dtrain_predictions = alg.predict(train[X])\n",
    "    dtrain_predprob = alg.predict_proba(train[X])[:,1]\n",
    "    dtest_predictions = alg.predict(test[X])\n",
    "    dtest_predprob = alg.predict_proba(test[X])[:,1]\n",
    "    print(\"Train Accuracy : {:.4f}\".format(metrics.accuracy_score(\n",
    "                            train[y].values, dtrain_predictions)))\n",
    "    print(\"Train AUC Score : {:.4f}\".format(metrics.roc_auc_score(\n",
    "                            train[y].values, dtrain_predprob)))\n",
    "    print(\"Test  Accuracy : {:.4f}\".format(metrics.accuracy_score(\n",
    "                            test[y].values, dtest_predictions)))\n",
    "    print(\"Test  AUC Score : {:.4f}\".format(metrics.roc_auc_score(\n",
    "                            test[y].values, dtest_predprob)))\n",
    "    return alg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " AK홀딩스\n",
      "Train Accuracy : 0.9224\n",
      "Train AUC Score : 0.9821\n",
      "Test  Accuracy : 0.8571\n",
      "Test  AUC Score : 0.9298\n",
      "\n",
      " BNK금융지주\n",
      "Train Accuracy : 0.9040\n",
      "Train AUC Score : 0.9750\n",
      "Test  Accuracy : 0.8531\n",
      "Test  AUC Score : 0.9247\n",
      "\n",
      " CJ\n",
      "Train Accuracy : 0.9193\n",
      "Train AUC Score : 0.9766\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.9164\n",
      "\n",
      " CJ CGV\n",
      "Train Accuracy : 0.9316\n",
      "Train AUC Score : 0.9856\n",
      "Test  Accuracy : 0.8571\n",
      "Test  AUC Score : 0.9192\n",
      "\n",
      " CJ대한통운\n",
      "Train Accuracy : 0.9459\n",
      "Train AUC Score : 0.9912\n",
      "Test  Accuracy : 0.8612\n",
      "Test  AUC Score : 0.9278\n",
      "\n",
      " CJ제일제당\n",
      "Train Accuracy : 0.9694\n",
      "Train AUC Score : 0.9982\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9165\n",
      "\n",
      " DB손해보험\n",
      "Train Accuracy : 0.9571\n",
      "Train AUC Score : 0.9927\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.9028\n",
      "\n",
      " DB하이텍\n",
      "Train Accuracy : 0.9234\n",
      "Train AUC Score : 0.9766\n",
      "Test  Accuracy : 0.8408\n",
      "Test  AUC Score : 0.9241\n",
      "\n",
      " GKL\n",
      "Train Accuracy : 0.9336\n",
      "Train AUC Score : 0.9822\n",
      "Test  Accuracy : 0.8612\n",
      "Test  AUC Score : 0.9410\n",
      "\n",
      " GS\n",
      "Train Accuracy : 0.9734\n",
      "Train AUC Score : 0.9978\n",
      "Test  Accuracy : 0.7592\n",
      "Test  AUC Score : 0.8653\n",
      "\n",
      " GS건설\n",
      "Train Accuracy : 0.9091\n",
      "Train AUC Score : 0.9697\n",
      "Test  Accuracy : 0.8449\n",
      "Test  AUC Score : 0.9199\n",
      "\n",
      " GS리테일\n",
      "Train Accuracy : 0.9203\n",
      "Train AUC Score : 0.9817\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.8860\n",
      "\n",
      " HDC\n",
      "Train Accuracy : 0.9529\n",
      "Train AUC Score : 0.9870\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.9078\n",
      "\n",
      " JW중외제약\n",
      "Train Accuracy : 0.8825\n",
      "Train AUC Score : 0.9508\n",
      "Test  Accuracy : 0.8735\n",
      "Test  AUC Score : 0.9368\n",
      "\n",
      " JW홀딩스\n",
      "Train Accuracy : 0.9417\n",
      "Train AUC Score : 0.9880\n",
      "Test  Accuracy : 0.8653\n",
      "Test  AUC Score : 0.9377\n",
      "\n",
      " KB금융\n",
      "Train Accuracy : 0.9009\n",
      "Train AUC Score : 0.9709\n",
      "Test  Accuracy : 0.7796\n",
      "Test  AUC Score : 0.8682\n",
      "\n",
      " KCC\n",
      "Train Accuracy : 0.9224\n",
      "Train AUC Score : 0.9773\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.9089\n",
      "\n",
      " KT\n",
      "Train Accuracy : 0.9489\n",
      "Train AUC Score : 0.9896\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8659\n",
      "\n",
      " KT&G\n",
      "Train Accuracy : 0.8662\n",
      "Train AUC Score : 0.9420\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.9168\n",
      "\n",
      " LF\n",
      "Train Accuracy : 0.8897\n",
      "Train AUC Score : 0.9608\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.8790\n",
      "\n",
      " LG\n",
      "Train Accuracy : 0.9898\n",
      "Train AUC Score : 0.9989\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.8796\n",
      "\n",
      " LG디스플레이\n",
      "Train Accuracy : 1.0000\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8855\n",
      "\n",
      " LG상사\n",
      "Train Accuracy : 0.9540\n",
      "Train AUC Score : 0.9908\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.8861\n",
      "\n",
      " LG생활건강\n",
      "Train Accuracy : 0.9132\n",
      "Train AUC Score : 0.9741\n",
      "Test  Accuracy : 0.7592\n",
      "Test  AUC Score : 0.8831\n",
      "\n",
      " LG유플러스\n",
      "Train Accuracy : 0.9081\n",
      "Train AUC Score : 0.9664\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8808\n",
      "\n",
      " LG이노텍\n",
      "Train Accuracy : 0.8846\n",
      "Train AUC Score : 0.9545\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8690\n",
      "\n",
      " LG전자\n",
      "Train Accuracy : 0.9070\n",
      "Train AUC Score : 0.9618\n",
      "Test  Accuracy : 0.7755\n",
      "Test  AUC Score : 0.8804\n",
      "\n",
      " LG하우시스\n",
      "Train Accuracy : 0.9377\n",
      "Train AUC Score : 0.9874\n",
      "Test  Accuracy : 0.7469\n",
      "Test  AUC Score : 0.8556\n",
      "\n",
      " LG화학\n",
      "Train Accuracy : 0.9173\n",
      "Train AUC Score : 0.9781\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.9304\n",
      "\n",
      " LS\n",
      "Train Accuracy : 0.9122\n",
      "Train AUC Score : 0.9722\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.8887\n",
      "\n",
      " LS산전\n",
      "Train Accuracy : 0.8784\n",
      "Train AUC Score : 0.9460\n",
      "Test  Accuracy : 0.7714\n",
      "Test  AUC Score : 0.8819\n",
      "\n",
      " NAVER\n",
      "Train Accuracy : 0.8940\n",
      "Train AUC Score : 0.9694\n",
      "Test  Accuracy : 0.8382\n",
      "Test  AUC Score : 0.9095\n",
      "\n",
      " NH투자증권\n",
      "Train Accuracy : 0.9530\n",
      "Train AUC Score : 0.9914\n",
      "Test  Accuracy : 0.7755\n",
      "Test  AUC Score : 0.8921\n",
      "\n",
      " OCI\n",
      "Train Accuracy : 0.9111\n",
      "Train AUC Score : 0.9739\n",
      "Test  Accuracy : 0.8041\n",
      "Test  AUC Score : 0.8892\n",
      "\n",
      " POSCO\n",
      "Train Accuracy : 0.9285\n",
      "Train AUC Score : 0.9774\n",
      "Test  Accuracy : 0.7959\n",
      "Test  AUC Score : 0.8889\n",
      "\n",
      " S&T모티브\n",
      "Train Accuracy : 0.9040\n",
      "Train AUC Score : 0.9687\n",
      "Test  Accuracy : 0.8490\n",
      "Test  AUC Score : 0.9110\n",
      "\n",
      " S-Oil\n",
      "Train Accuracy : 0.9745\n",
      "Train AUC Score : 0.9969\n",
      "Test  Accuracy : 0.7918\n",
      "Test  AUC Score : 0.8871\n",
      "\n",
      " SBS\n",
      "Train Accuracy : 0.9980\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8041\n",
      "Test  AUC Score : 0.9034\n",
      "\n",
      " SK\n",
      "Train Accuracy : 0.9939\n",
      "Train AUC Score : 0.9999\n",
      "Test  Accuracy : 0.7673\n",
      "Test  AUC Score : 0.8757\n",
      "\n",
      " SKC\n",
      "Train Accuracy : 0.9265\n",
      "Train AUC Score : 0.9807\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8652\n",
      "\n",
      " SK네트웍스\n",
      "Train Accuracy : 0.9173\n",
      "Train AUC Score : 0.9737\n",
      "Test  Accuracy : 0.8041\n",
      "Test  AUC Score : 0.8844\n",
      "\n",
      " SK디스커버리\n",
      "Train Accuracy : 0.9458\n",
      "Train AUC Score : 0.9904\n",
      "Test  Accuracy : 0.8506\n",
      "Test  AUC Score : 0.9178\n",
      "\n",
      " SK이노베이션\n",
      "Train Accuracy : 0.8836\n",
      "Train AUC Score : 0.9647\n",
      "Test  Accuracy : 0.7469\n",
      "Test  AUC Score : 0.8790\n",
      "\n",
      " SK텔레콤\n",
      "Train Accuracy : 0.9428\n",
      "Train AUC Score : 0.9889\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8724\n",
      "\n",
      " SK하이닉스\n",
      "Train Accuracy : 0.9336\n",
      "Train AUC Score : 0.9846\n",
      "Test  Accuracy : 0.7673\n",
      "Test  AUC Score : 0.8757\n",
      "\n",
      " SPC삼립\n",
      "Train Accuracy : 0.9316\n",
      "Train AUC Score : 0.9871\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.9080\n",
      "\n",
      " 강원랜드\n",
      "Train Accuracy : 0.9408\n",
      "Train AUC Score : 0.9852\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8800\n",
      "\n",
      " 고려아연\n",
      "Train Accuracy : 0.9101\n",
      "Train AUC Score : 0.9746\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.9022\n",
      "\n",
      " 고려제강\n",
      "Train Accuracy : 0.9397\n",
      "Train AUC Score : 0.9841\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.9087\n",
      "\n",
      " 금호석유\n",
      "Train Accuracy : 0.9234\n",
      "Train AUC Score : 0.9758\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.8791\n",
      "\n",
      " 금호타이어\n",
      "Train Accuracy : 0.8846\n",
      "Train AUC Score : 0.9630\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.9017\n",
      "\n",
      " 기아차\n",
      "Train Accuracy : 0.9224\n",
      "Train AUC Score : 0.9765\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8953\n",
      "\n",
      " 기업은행\n",
      "Train Accuracy : 0.9775\n",
      "Train AUC Score : 0.9975\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.9178\n",
      "\n",
      " 남양유업\n",
      "Train Accuracy : 0.9469\n",
      "Train AUC Score : 0.9875\n",
      "Test  Accuracy : 0.7837\n",
      "Test  AUC Score : 0.8858\n",
      "\n",
      " 남해화학\n",
      "Train Accuracy : 0.9081\n",
      "Train AUC Score : 0.9712\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8815\n",
      "\n",
      " 넥센타이어\n",
      "Train Accuracy : 0.9387\n",
      "Train AUC Score : 0.9872\n",
      "Test  Accuracy : 0.7755\n",
      "Test  AUC Score : 0.8550\n",
      "\n",
      " 녹십자\n",
      "Train Accuracy : 0.9030\n",
      "Train AUC Score : 0.9721\n",
      "Test  Accuracy : 0.8653\n",
      "Test  AUC Score : 0.9114\n",
      "\n",
      " 녹십자홀딩스\n",
      "Train Accuracy : 0.9377\n",
      "Train AUC Score : 0.9852\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8931\n",
      "\n",
      " 농심\n",
      "Train Accuracy : 0.9122\n",
      "Train AUC Score : 0.9704\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.9219\n",
      "\n",
      " 대교\n",
      "Train Accuracy : 0.9551\n",
      "Train AUC Score : 0.9947\n",
      "Test  Accuracy : 0.7878\n",
      "Test  AUC Score : 0.8683\n",
      "\n",
      " 대덕전자\n",
      "Train Accuracy : 0.9091\n",
      "Train AUC Score : 0.9700\n",
      "Test  Accuracy : 0.8041\n",
      "Test  AUC Score : 0.8823\n",
      "\n",
      " 대림산업\n",
      "Train Accuracy : 0.8744\n",
      "Train AUC Score : 0.9495\n",
      "Test  Accuracy : 0.7878\n",
      "Test  AUC Score : 0.8621\n",
      "\n",
      " 대상\n",
      "Train Accuracy : 0.9030\n",
      "Train AUC Score : 0.9672\n",
      "Test  Accuracy : 0.8735\n",
      "Test  AUC Score : 0.9332\n",
      "\n",
      " 대우건설\n",
      "Train Accuracy : 0.9132\n",
      "Train AUC Score : 0.9774\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.9050\n",
      "\n",
      " 대우조선해양\n",
      "Train Accuracy : 0.9409\n",
      "Train AUC Score : 0.9863\n",
      "Test  Accuracy : 0.8142\n",
      "Test  AUC Score : 0.9050\n",
      "\n",
      " 대웅제약\n",
      "Train Accuracy : 0.8856\n",
      "Train AUC Score : 0.9647\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.9159\n",
      "\n",
      " 대한유화\n",
      "Train Accuracy : 0.9244\n",
      "Train AUC Score : 0.9825\n",
      "Test  Accuracy : 0.7714\n",
      "Test  AUC Score : 0.8595\n",
      "\n",
      " 대한항공\n",
      "Train Accuracy : 0.9065\n",
      "Train AUC Score : 0.9657\n",
      "Test  Accuracy : 0.7950\n",
      "Test  AUC Score : 0.8700\n",
      "\n",
      " 동국제강\n",
      "Train Accuracy : 0.9285\n",
      "Train AUC Score : 0.9851\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.8780\n",
      "\n",
      " 동서\n",
      "Train Accuracy : 0.9479\n",
      "Train AUC Score : 0.9891\n",
      "Test  Accuracy : 0.7878\n",
      "Test  AUC Score : 0.8864\n",
      "\n",
      " 동아쏘시오홀딩스\n",
      "Train Accuracy : 0.9421\n",
      "Train AUC Score : 0.9842\n",
      "Test  Accuracy : 0.8107\n",
      "Test  AUC Score : 0.9015\n",
      "\n",
      " 동양\n",
      "Train Accuracy : 0.9888\n",
      "Train AUC Score : 0.9995\n",
      "Test  Accuracy : 0.7946\n",
      "Test  AUC Score : 0.8905\n",
      "\n",
      " 동원F&B\n",
      "Train Accuracy : 0.9070\n",
      "Train AUC Score : 0.9704\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.8914\n",
      "\n",
      " 동원시스템즈\n",
      "Train Accuracy : 0.9990\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9089\n",
      "\n",
      " 두산\n",
      "Train Accuracy : 0.9438\n",
      "Train AUC Score : 0.9835\n",
      "Test  Accuracy : 0.8041\n",
      "Test  AUC Score : 0.9144\n",
      "\n",
      " 두산인프라코어\n",
      "Train Accuracy : 0.9244\n",
      "Train AUC Score : 0.9807\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.8946\n",
      "\n",
      " 두산중공업\n",
      "Train Accuracy : 0.9193\n",
      "Train AUC Score : 0.9812\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.9080\n",
      "\n",
      " 락앤락\n",
      "Train Accuracy : 0.8979\n",
      "Train AUC Score : 0.9627\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8807\n",
      "\n",
      " 롯데쇼핑\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9317\n",
      "Train AUC Score : 0.9809\n",
      "Test  Accuracy : 0.8058\n",
      "Test  AUC Score : 0.9044\n",
      "\n",
      " 롯데정밀화학\n",
      "Train Accuracy : 0.9265\n",
      "Train AUC Score : 0.9847\n",
      "Test  Accuracy : 0.7796\n",
      "Test  AUC Score : 0.8575\n",
      "\n",
      " 롯데지주\n",
      "Train Accuracy : 0.9205\n",
      "Train AUC Score : 0.9787\n",
      "Test  Accuracy : 0.8292\n",
      "Test  AUC Score : 0.8976\n",
      "\n",
      " 롯데칠성\n",
      "Train Accuracy : 1.0000\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.7810\n",
      "Test  AUC Score : 0.8602\n",
      "\n",
      " 롯데케미칼\n",
      "Train Accuracy : 0.9857\n",
      "Train AUC Score : 0.9989\n",
      "Test  Accuracy : 0.7837\n",
      "Test  AUC Score : 0.8541\n",
      "\n",
      " 롯데푸드\n",
      "Train Accuracy : 0.9275\n",
      "Train AUC Score : 0.9805\n",
      "Test  Accuracy : 0.8182\n",
      "Test  AUC Score : 0.9028\n",
      "\n",
      " 롯데하이마트\n",
      "Train Accuracy : 0.9734\n",
      "Train AUC Score : 0.9978\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.9078\n",
      "\n",
      " 메리츠종금증권\n",
      "Train Accuracy : 0.9990\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.9055\n",
      "\n",
      " 무학\n",
      "Train Accuracy : 0.9387\n",
      "Train AUC Score : 0.9874\n",
      "Test  Accuracy : 0.7673\n",
      "Test  AUC Score : 0.8755\n",
      "\n",
      " 미래에셋대우\n",
      "Train Accuracy : 0.8999\n",
      "Train AUC Score : 0.9647\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.8982\n",
      "\n",
      " 보령제약\n",
      "Train Accuracy : 0.9078\n",
      "Train AUC Score : 0.9674\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.8863\n",
      "\n",
      " 부광약품\n",
      "Train Accuracy : 1.0000\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.9122\n",
      "\n",
      " 빙그레\n",
      "Train Accuracy : 0.9704\n",
      "Train AUC Score : 0.9963\n",
      "Test  Accuracy : 0.7918\n",
      "Test  AUC Score : 0.8693\n",
      "\n",
      " 삼성SDI\n",
      "Train Accuracy : 0.9183\n",
      "Train AUC Score : 0.9777\n",
      "Test  Accuracy : 0.8490\n",
      "Test  AUC Score : 0.9265\n",
      "\n",
      " 삼성생명\n",
      "Train Accuracy : 0.8836\n",
      "Train AUC Score : 0.9581\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8859\n",
      "\n",
      " 삼성엔지니어링\n",
      "Train Accuracy : 0.9207\n",
      "Train AUC Score : 0.9793\n",
      "Test  Accuracy : 0.8272\n",
      "Test  AUC Score : 0.9001\n",
      "\n",
      " 삼성전기\n",
      "Train Accuracy : 0.9081\n",
      "Train AUC Score : 0.9697\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.8993\n",
      "\n",
      " 삼성전자\n",
      "Train Accuracy : 0.9826\n",
      "Train AUC Score : 0.9987\n",
      "Test  Accuracy : 0.8653\n",
      "Test  AUC Score : 0.9361\n",
      "\n",
      " 삼성중공업\n",
      "Train Accuracy : 0.9857\n",
      "Train AUC Score : 0.9988\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9193\n",
      "\n",
      " 삼성증권\n",
      "Train Accuracy : 0.9499\n",
      "Train AUC Score : 0.9938\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.9072\n",
      "\n",
      " 삼성카드\n",
      "Train Accuracy : 0.9408\n",
      "Train AUC Score : 0.9888\n",
      "Test  Accuracy : 0.8408\n",
      "Test  AUC Score : 0.9125\n",
      "\n",
      " 삼성화재\n",
      "Train Accuracy : 1.0000\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.7673\n",
      "Test  AUC Score : 0.8707\n",
      "\n",
      " 삼양사\n",
      "Train Accuracy : 0.9019\n",
      "Train AUC Score : 0.9772\n",
      "Test  Accuracy : 0.7959\n",
      "Test  AUC Score : 0.8910\n",
      "\n",
      " 삼양홀딩스\n",
      "Train Accuracy : 0.9305\n",
      "Train AUC Score : 0.9806\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.9120\n",
      "\n",
      " 세방전지\n",
      "Train Accuracy : 0.9173\n",
      "Train AUC Score : 0.9777\n",
      "Test  Accuracy : 0.7673\n",
      "Test  AUC Score : 0.8600\n",
      "\n",
      " 세아베스틸\n",
      "Train Accuracy : 0.9745\n",
      "Train AUC Score : 0.9972\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.9240\n",
      "\n",
      " 세아제강\n",
      "Train Accuracy : 0.9162\n",
      "Train AUC Score : 0.9770\n",
      "Test  Accuracy : 0.8653\n",
      "Test  AUC Score : 0.9274\n",
      "\n",
      " 셀트리온\n",
      "Train Accuracy : 0.9520\n",
      "Train AUC Score : 0.9910\n",
      "Test  Accuracy : 0.8449\n",
      "Test  AUC Score : 0.9152\n",
      "\n",
      " 신세계\n",
      "Train Accuracy : 0.9244\n",
      "Train AUC Score : 0.9754\n",
      "Test  Accuracy : 0.8245\n",
      "Test  AUC Score : 0.9014\n",
      "\n",
      " 신한지주\n",
      "Train Accuracy : 0.8641\n",
      "Train AUC Score : 0.9430\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8982\n",
      "\n",
      " 쌍용양회\n",
      "Train Accuracy : 0.9254\n",
      "Train AUC Score : 0.9823\n",
      "Test  Accuracy : 0.8408\n",
      "Test  AUC Score : 0.9000\n",
      "\n",
      " 쌍용차\n",
      "Train Accuracy : 0.9132\n",
      "Train AUC Score : 0.9706\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.8724\n",
      "\n",
      " 아모레G\n",
      "Train Accuracy : 0.9949\n",
      "Train AUC Score : 0.9999\n",
      "Test  Accuracy : 0.8025\n",
      "Test  AUC Score : 0.9050\n",
      "\n",
      " 아모레퍼시픽\n",
      "Train Accuracy : 0.9464\n",
      "Train AUC Score : 0.9893\n",
      "Test  Accuracy : 0.7901\n",
      "Test  AUC Score : 0.9010\n",
      "\n",
      " 아이에스동서\n",
      "Train Accuracy : 0.9724\n",
      "Train AUC Score : 0.9955\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8877\n",
      "\n",
      " 에스엘\n",
      "Train Accuracy : 0.9499\n",
      "Train AUC Score : 0.9882\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.9099\n",
      "\n",
      " 에스원\n",
      "Train Accuracy : 0.8713\n",
      "Train AUC Score : 0.9559\n",
      "Test  Accuracy : 0.7224\n",
      "Test  AUC Score : 0.8020\n",
      "\n",
      " 엔씨소프트\n",
      "Train Accuracy : 0.9806\n",
      "Train AUC Score : 0.9982\n",
      "Test  Accuracy : 0.7592\n",
      "Test  AUC Score : 0.8451\n",
      "\n",
      " 영원무역\n",
      "Train Accuracy : 0.9193\n",
      "Train AUC Score : 0.9781\n",
      "Test  Accuracy : 0.8612\n",
      "Test  AUC Score : 0.9137\n",
      "\n",
      " 영진약품\n",
      "Train Accuracy : 0.9173\n",
      "Train AUC Score : 0.9754\n",
      "Test  Accuracy : 0.8490\n",
      "Test  AUC Score : 0.9330\n",
      "\n",
      " 영풍\n",
      "Train Accuracy : 0.9173\n",
      "Train AUC Score : 0.9721\n",
      "Test  Accuracy : 0.8571\n",
      "Test  AUC Score : 0.9358\n",
      "\n",
      " 오뚜기\n",
      "Train Accuracy : 0.9244\n",
      "Train AUC Score : 0.9805\n",
      "Test  Accuracy : 0.7796\n",
      "Test  AUC Score : 0.8721\n",
      "\n",
      " 오리온홀딩스\n",
      "Train Accuracy : 0.9195\n",
      "Train AUC Score : 0.9797\n",
      "Test  Accuracy : 0.7917\n",
      "Test  AUC Score : 0.8748\n",
      "\n",
      " 우리은행\n",
      "Train Accuracy : 1.0000\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8125\n",
      "Test  AUC Score : 0.8887\n",
      "\n",
      " 유니드\n",
      "Train Accuracy : 0.9877\n",
      "Train AUC Score : 0.9994\n",
      "Test  Accuracy : 0.7878\n",
      "Test  AUC Score : 0.8815\n",
      "\n",
      " 유한양행\n",
      "Train Accuracy : 0.9060\n",
      "Train AUC Score : 0.9707\n",
      "Test  Accuracy : 0.7796\n",
      "Test  AUC Score : 0.8918\n",
      "\n",
      " 이마트\n",
      "Train Accuracy : 0.8938\n",
      "Train AUC Score : 0.9586\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.8917\n",
      "\n",
      " 일양약품\n",
      "Train Accuracy : 0.9050\n",
      "Train AUC Score : 0.9743\n",
      "Test  Accuracy : 0.8245\n",
      "Test  AUC Score : 0.8864\n",
      "\n",
      " 일진머티리얼즈\n",
      "Train Accuracy : 0.9408\n",
      "Train AUC Score : 0.9867\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.9137\n",
      "\n",
      " 제일기획\n",
      "Train Accuracy : 0.9122\n",
      "Train AUC Score : 0.9746\n",
      "Test  Accuracy : 0.7918\n",
      "Test  AUC Score : 0.8861\n",
      "\n",
      " 카카오\n",
      "Train Accuracy : 0.9223\n",
      "Train AUC Score : 0.9769\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.9113\n",
      "\n",
      " 코오롱인더\n",
      "Train Accuracy : 0.9724\n",
      "Train AUC Score : 0.9951\n",
      "Test  Accuracy : 0.7878\n",
      "Test  AUC Score : 0.8974\n",
      "\n",
      " 코웨이\n",
      "Train Accuracy : 0.9632\n",
      "Train AUC Score : 0.9940\n",
      "Test  Accuracy : 0.7061\n",
      "Test  AUC Score : 0.8148\n",
      "\n",
      " 태광산업\n",
      "Train Accuracy : 0.8958\n",
      "Train AUC Score : 0.9584\n",
      "Test  Accuracy : 0.7959\n",
      "Test  AUC Score : 0.9019\n",
      "\n",
      " 팜스코\n",
      "Train Accuracy : 0.9234\n",
      "Train AUC Score : 0.9839\n",
      "Test  Accuracy : 0.7714\n",
      "Test  AUC Score : 0.8645\n",
      "\n",
      " 팬오션\n",
      "Train Accuracy : 0.9545\n",
      "Train AUC Score : 0.9924\n",
      "Test  Accuracy : 0.8664\n",
      "Test  AUC Score : 0.9374\n",
      "\n",
      " 포스코대우\n",
      "Train Accuracy : 0.9408\n",
      "Train AUC Score : 0.9865\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.8931\n",
      "\n",
      " 풍산\n",
      "Train Accuracy : 0.9990\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.8694\n",
      "Test  AUC Score : 0.9235\n",
      "\n",
      " 하나금융지주\n",
      "Train Accuracy : 0.9081\n",
      "Train AUC Score : 0.9715\n",
      "Test  Accuracy : 0.7918\n",
      "Test  AUC Score : 0.8455\n",
      "\n",
      " 하이트진로\n",
      "Train Accuracy : 0.9193\n",
      "Train AUC Score : 0.9792\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.9017\n",
      "\n",
      " 한국가스공사\n",
      "Train Accuracy : 0.9132\n",
      "Train AUC Score : 0.9768\n",
      "Test  Accuracy : 0.7796\n",
      "Test  AUC Score : 0.8612\n",
      "\n",
      " 한국금융지주\n",
      "Train Accuracy : 0.9561\n",
      "Train AUC Score : 0.9938\n",
      "Test  Accuracy : 0.8082\n",
      "Test  AUC Score : 0.8880\n",
      "\n",
      " 한국단자\n",
      "Train Accuracy : 0.9265\n",
      "Train AUC Score : 0.9850\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.8580\n",
      "\n",
      " 한국쉘석유\n",
      "Train Accuracy : 0.9213\n",
      "Train AUC Score : 0.9805\n",
      "Test  Accuracy : 0.7837\n",
      "Test  AUC Score : 0.8358\n",
      "\n",
      " 한국전력\n",
      "Train Accuracy : 0.9132\n",
      "Train AUC Score : 0.9754\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9286\n",
      "\n",
      " 한국콜마\n",
      "Train Accuracy : 0.9408\n",
      "Train AUC Score : 0.9867\n",
      "Test  Accuracy : 0.8531\n",
      "Test  AUC Score : 0.9343\n",
      "\n",
      " 한국타이어\n",
      "Train Accuracy : 0.9642\n",
      "Train AUC Score : 0.9946\n",
      "Test  Accuracy : 0.7265\n",
      "Test  AUC Score : 0.8230\n",
      "\n",
      " 한국타이어월드와이드\n",
      "Train Accuracy : 0.9877\n",
      "Train AUC Score : 0.9992\n",
      "Test  Accuracy : 0.7143\n",
      "Test  AUC Score : 0.8298\n",
      "\n",
      " 한국항공우주\n",
      "Train Accuracy : 0.9035\n",
      "Train AUC Score : 0.9603\n",
      "Test  Accuracy : 0.8074\n",
      "Test  AUC Score : 0.8837\n",
      "\n",
      " 한라홀딩스\n",
      "Train Accuracy : 1.0000\n",
      "Train AUC Score : 1.0000\n",
      "Test  Accuracy : 0.7552\n",
      "Test  AUC Score : 0.8545\n",
      "\n",
      " 한미사이언스\n",
      "Train Accuracy : 0.9580\n",
      "Train AUC Score : 0.9912\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9137\n",
      "\n",
      " 한미약품\n",
      "Train Accuracy : 0.9224\n",
      "Train AUC Score : 0.9786\n",
      "Test  Accuracy : 0.8245\n",
      "Test  AUC Score : 0.9175\n",
      "\n",
      " 한샘\n",
      "Train Accuracy : 0.9295\n",
      "Train AUC Score : 0.9813\n",
      "Test  Accuracy : 0.7714\n",
      "Test  AUC Score : 0.8774\n",
      "\n",
      " 한섬\n",
      "Train Accuracy : 0.9152\n",
      "Train AUC Score : 0.9759\n",
      "Test  Accuracy : 0.8449\n",
      "Test  AUC Score : 0.9280\n",
      "\n",
      " 한세실업\n",
      "Train Accuracy : 0.9152\n",
      "Train AUC Score : 0.9732\n",
      "Test  Accuracy : 0.8490\n",
      "Test  AUC Score : 0.9233\n",
      "\n",
      " 한솔케미칼\n",
      "Train Accuracy : 0.9316\n",
      "Train AUC Score : 0.9818\n",
      "Test  Accuracy : 0.7796\n",
      "Test  AUC Score : 0.8908\n",
      "\n",
      " 한온시스템\n",
      "Train Accuracy : 0.8812\n",
      "Train AUC Score : 0.9545\n",
      "Test  Accuracy : 0.8182\n",
      "Test  AUC Score : 0.8750\n",
      "\n",
      " 한올바이오파마\n",
      "Train Accuracy : 0.9367\n",
      "Train AUC Score : 0.9859\n",
      "Test  Accuracy : 0.8408\n",
      "Test  AUC Score : 0.9344\n",
      "\n",
      " 한일시멘트\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy : 0.9161\n",
      "Train AUC Score : 0.9765\n",
      "Test  Accuracy : 0.8286\n",
      "Test  AUC Score : 0.9015\n",
      "\n",
      " 한전KPS\n",
      "Train Accuracy : 0.9745\n",
      "Train AUC Score : 0.9971\n",
      "Test  Accuracy : 0.8204\n",
      "Test  AUC Score : 0.8894\n",
      "\n",
      " 한전기술\n",
      "Train Accuracy : 0.8999\n",
      "Train AUC Score : 0.9688\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.9044\n",
      "\n",
      " 한진중공업\n",
      "Train Accuracy : 0.9581\n",
      "Train AUC Score : 0.9925\n",
      "Test  Accuracy : 0.8122\n",
      "Test  AUC Score : 0.9006\n",
      "\n",
      " 한화\n",
      "Train Accuracy : 0.9775\n",
      "Train AUC Score : 0.9977\n",
      "Test  Accuracy : 0.8490\n",
      "Test  AUC Score : 0.9210\n",
      "\n",
      " 한화생명\n",
      "Train Accuracy : 0.8866\n",
      "Train AUC Score : 0.9583\n",
      "Test  Accuracy : 0.8408\n",
      "Test  AUC Score : 0.9104\n",
      "\n",
      " 한화에어로스페이스\n",
      "Train Accuracy : 0.9101\n",
      "Train AUC Score : 0.9532\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.8734\n",
      "\n",
      " 한화케미칼\n",
      "Train Accuracy : 0.9826\n",
      "Train AUC Score : 0.9988\n",
      "Test  Accuracy : 0.7510\n",
      "Test  AUC Score : 0.8432\n",
      "\n",
      " 현대건설\n",
      "Train Accuracy : 0.9275\n",
      "Train AUC Score : 0.9746\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9039\n",
      "\n",
      " 현대그린푸드\n",
      "Train Accuracy : 0.9694\n",
      "Train AUC Score : 0.9976\n",
      "Test  Accuracy : 0.7959\n",
      "Test  AUC Score : 0.8993\n",
      "\n",
      " 현대글로비스\n",
      "Train Accuracy : 0.9939\n",
      "Train AUC Score : 0.9999\n",
      "Test  Accuracy : 0.8245\n",
      "Test  AUC Score : 0.8769\n",
      "\n",
      " 현대리바트\n",
      "Train Accuracy : 0.9367\n",
      "Train AUC Score : 0.9861\n",
      "Test  Accuracy : 0.7755\n",
      "Test  AUC Score : 0.8934\n",
      "\n",
      " 현대모비스\n",
      "Train Accuracy : 0.9101\n",
      "Train AUC Score : 0.9707\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.8879\n",
      "\n",
      " 현대미포조선\n",
      "Train Accuracy : 0.9132\n",
      "Train AUC Score : 0.9770\n",
      "Test  Accuracy : 0.8000\n",
      "Test  AUC Score : 0.8807\n",
      "\n",
      " 현대백화점\n",
      "Train Accuracy : 0.8897\n",
      "Train AUC Score : 0.9482\n",
      "Test  Accuracy : 0.8449\n",
      "Test  AUC Score : 0.9167\n",
      "\n",
      " 현대엘리베이\n",
      "Train Accuracy : 0.9101\n",
      "Train AUC Score : 0.9630\n",
      "Test  Accuracy : 0.8367\n",
      "Test  AUC Score : 0.9055\n",
      "\n",
      " 현대위아\n",
      "Train Accuracy : 0.9377\n",
      "Train AUC Score : 0.9871\n",
      "Test  Accuracy : 0.8327\n",
      "Test  AUC Score : 0.9102\n",
      "\n",
      " 현대제철\n",
      "Train Accuracy : 0.8876\n",
      "Train AUC Score : 0.9543\n",
      "Test  Accuracy : 0.7878\n",
      "Test  AUC Score : 0.8805\n",
      "\n",
      " 현대중공업\n",
      "Train Accuracy : 0.9051\n",
      "Train AUC Score : 0.9642\n",
      "Test  Accuracy : 0.8208\n",
      "Test  AUC Score : 0.8750\n",
      "\n",
      " 현대차\n",
      "Train Accuracy : 0.9683\n",
      "Train AUC Score : 0.9959\n",
      "Test  Accuracy : 0.8163\n",
      "Test  AUC Score : 0.9093\n",
      "\n",
      " 현대해상\n",
      "Train Accuracy : 0.9816\n",
      "Train AUC Score : 0.9991\n",
      "Test  Accuracy : 0.7918\n",
      "Test  AUC Score : 0.8744\n",
      "\n",
      " 현대홈쇼핑\n",
      "Train Accuracy : 0.8968\n",
      "Train AUC Score : 0.9642\n",
      "Test  Accuracy : 0.7429\n",
      "Test  AUC Score : 0.8392\n",
      "\n",
      " 호텔신라\n",
      "Train Accuracy : 0.9765\n",
      "Train AUC Score : 0.9980\n",
      "Test  Accuracy : 0.8245\n",
      "Test  AUC Score : 0.9026\n",
      "\n",
      " 효성\n",
      "Train Accuracy : 0.8711\n",
      "Train AUC Score : 0.9493\n",
      "Test  Accuracy : 0.7884\n",
      "Test  AUC Score : 0.8713\n",
      "\n",
      " 후성\n",
      "Train Accuracy : 0.9152\n",
      "Train AUC Score : 0.9747\n",
      "Test  Accuracy : 0.7837\n",
      "Test  AUC Score : 0.8609\n",
      "\n",
      " 휴켐스\n",
      "Train Accuracy : 0.9030\n",
      "Train AUC Score : 0.9701\n",
      "Test  Accuracy : 0.8490\n",
      "Test  AUC Score : 0.8884\n"
     ]
    }
   ],
   "source": [
    "model_set = {}\n",
    "for stock, df in train.items():\n",
    "    print('\\n',stock)\n",
    "    model = modelfit(alg, df, test[stock], X, 'sh1_Y3_5')\n",
    "    model_set[stock] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AK홀딩스   12\n",
      "BNK금융지주   17\n",
      "CJ   12\n",
      "CJ CGV   20\n",
      "CJ대한통운   26\n",
      "CJ제일제당   43\n",
      "DB손해보험   32\n",
      "DB하이텍   12\n",
      "GKL   22\n",
      "GS   41\n",
      "GS건설   4\n",
      "GS리테일   15\n",
      "HDC   22\n",
      "JW중외제약   2\n",
      "JW홀딩스   26\n",
      "KB금융   11\n",
      "KCC   13\n",
      "KT   21\n",
      "KT&G   6\n",
      "LF   4\n",
      "LG   47\n",
      "LG디스플레이   171\n",
      "LG상사   26\n",
      "LG생활건강   12\n",
      "LG유플러스   6\n",
      "LG이노텍   3\n",
      "LG전자   4\n",
      "LG하우시스   22\n",
      "LG화학   6\n",
      "LS   12\n",
      "LS산전   2\n",
      "NAVER   5\n",
      "NH투자증권   32\n",
      "OCI   7\n",
      "POSCO   15\n",
      "S&T모티브   12\n",
      "S-Oil   37\n",
      "SBS   72\n",
      "SK   49\n",
      "SKC   12\n",
      "SK네트웍스   12\n",
      "SK디스커버리   25\n",
      "SK이노베이션   10\n",
      "SK텔레콤   25\n",
      "SK하이닉스   16\n",
      "SPC삼립   16\n",
      "강원랜드   29\n",
      "고려아연   14\n",
      "고려제강   20\n",
      "금호석유   11\n",
      "금호타이어   6\n",
      "기아차   9\n",
      "기업은행   43\n",
      "남양유업   19\n",
      "남해화학   8\n",
      "넥센타이어   22\n",
      "녹십자   10\n",
      "녹십자홀딩스   14\n",
      "농심   10\n",
      "대교   39\n",
      "대덕전자   11\n",
      "대림산업   3\n",
      "대상   7\n",
      "대우건설   15\n",
      "대우조선해양   10\n",
      "대웅제약   6\n",
      "대한유화   20\n",
      "대한항공   5\n",
      "동국제강   20\n",
      "동서   25\n",
      "동아쏘시오홀딩스   15\n",
      "동양   52\n",
      "동원F&B   6\n",
      "동원시스템즈   74\n",
      "두산   18\n",
      "두산인프라코어   13\n",
      "두산중공업   14\n",
      "락앤락   6\n",
      "롯데쇼핑   16\n",
      "롯데정밀화학   24\n",
      "롯데지주   12\n",
      "롯데칠성   88\n",
      "롯데케미칼   47\n",
      "롯데푸드   13\n",
      "롯데하이마트   41\n",
      "메리츠종금증권   66\n",
      "무학   21\n",
      "미래에셋대우   4\n",
      "보령제약   5\n",
      "부광약품   178\n",
      "빙그레   38\n",
      "삼성SDI   18\n",
      "삼성생명   12\n",
      "삼성엔지니어링   12\n",
      "삼성전기   9\n",
      "삼성전자   43\n",
      "삼성중공업   48\n",
      "삼성증권   30\n",
      "삼성카드   25\n",
      "삼성화재   80\n",
      "삼양사   12\n",
      "삼양홀딩스   11\n",
      "세방전지   15\n",
      "세아베스틸   40\n",
      "세아제강   13\n",
      "셀트리온   22\n",
      "신세계   8\n",
      "신한지주   5\n",
      "쌍용양회   17\n",
      "쌍용차   7\n",
      "아모레G   66\n",
      "아모레퍼시픽   25\n",
      "아이에스동서   25\n",
      "에스엘   26\n",
      "에스원   4\n",
      "엔씨소프트   47\n",
      "영원무역   11\n",
      "영진약품   12\n",
      "영풍   7\n",
      "오뚜기   14\n",
      "오리온홀딩스   15\n",
      "우리은행   88\n",
      "유니드   53\n",
      "유한양행   10\n",
      "이마트   7\n",
      "일양약품   12\n",
      "일진머티리얼즈   20\n",
      "제일기획   17\n",
      "카카오   12\n",
      "코오롱인더   32\n",
      "코웨이   33\n",
      "태광산업   3\n",
      "팜스코   13\n",
      "팬오션   27\n",
      "포스코대우   18\n",
      "풍산   82\n",
      "하나금융지주   10\n",
      "하이트진로   10\n",
      "한국가스공사   10\n",
      "한국금융지주   29\n",
      "한국단자   16\n",
      "한국쉘석유   17\n",
      "한국전력   14\n",
      "한국콜마   18\n",
      "한국타이어   37\n",
      "한국타이어월드와이드   42\n",
      "한국항공우주   5\n",
      "한라홀딩스   73\n",
      "한미사이언스   28\n",
      "한미약품   8\n",
      "한샘   13\n",
      "한섬   15\n",
      "한세실업   11\n",
      "한솔케미칼   17\n",
      "한온시스템   7\n",
      "한올바이오파마   17\n",
      "한일시멘트   11\n",
      "한전KPS   38\n",
      "한전기술   4\n",
      "한진중공업   25\n",
      "한화   41\n",
      "한화생명   5\n",
      "한화에어로스페이스   2\n",
      "한화케미칼   40\n",
      "현대건설   8\n",
      "현대그린푸드   47\n",
      "현대글로비스   59\n",
      "현대리바트   13\n",
      "현대모비스   10\n",
      "현대미포조선   9\n",
      "현대백화점   2\n",
      "현대엘리베이   6\n",
      "현대위아   23\n",
      "현대제철   4\n",
      "현대중공업   3\n",
      "현대차   32\n",
      "현대해상   43\n",
      "현대홈쇼핑   7\n",
      "호텔신라   41\n",
      "효성   2\n",
      "후성   15\n",
      "휴켐스   10\n"
     ]
    }
   ],
   "source": [
    "for stock, model in model_set.items():\n",
    "    print(stock, ' ', model.get_params()['n_estimators'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>close_ma_3</th>\n",
       "      <th>volume_ma_3</th>\n",
       "      <th>close_ma_5</th>\n",
       "      <th>...</th>\n",
       "      <th>returnsOO12</th>\n",
       "      <th>returnsOC12</th>\n",
       "      <th>returnsCC14</th>\n",
       "      <th>returnsOO14</th>\n",
       "      <th>returnsOC14</th>\n",
       "      <th>Y3_1</th>\n",
       "      <th>Y3_3</th>\n",
       "      <th>Y3_5</th>\n",
       "      <th>Y3_7</th>\n",
       "      <th>Y3_10</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",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-20</th>\n",
       "      <td>30450.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>195667</td>\n",
       "      <td>-0.067103</td>\n",
       "      <td>29650.000000</td>\n",
       "      <td>242168.333333</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>-0.056281</td>\n",
       "      <td>-0.064539</td>\n",
       "      <td>-0.019513</td>\n",
       "      <td>-0.085695</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-21</th>\n",
       "      <td>28600.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>28300.0</td>\n",
       "      <td>28450.0</td>\n",
       "      <td>28450.0</td>\n",
       "      <td>78562</td>\n",
       "      <td>-0.001754</td>\n",
       "      <td>29166.666667</td>\n",
       "      <td>205759.666667</td>\n",
       "      <td>29310.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056089</td>\n",
       "      <td>-0.061348</td>\n",
       "      <td>-0.056377</td>\n",
       "      <td>-0.059390</td>\n",
       "      <td>-0.064648</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-22</th>\n",
       "      <td>28100.0</td>\n",
       "      <td>28500.0</td>\n",
       "      <td>27850.0</td>\n",
       "      <td>28400.0</td>\n",
       "      <td>28400.0</td>\n",
       "      <td>71601</td>\n",
       "      <td>-0.001757</td>\n",
       "      <td>28450.000000</td>\n",
       "      <td>115276.666667</td>\n",
       "      <td>29160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043523</td>\n",
       "      <td>-0.032903</td>\n",
       "      <td>-0.063107</td>\n",
       "      <td>-0.070415</td>\n",
       "      <td>-0.059796</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>28050.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>27850.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>59640</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>28550.000000</td>\n",
       "      <td>69934.333333</td>\n",
       "      <td>28940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.010363</td>\n",
       "      <td>-0.075508</td>\n",
       "      <td>-0.049121</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>28800.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>28650.0</td>\n",
       "      <td>29350.0</td>\n",
       "      <td>29350.0</td>\n",
       "      <td>73446</td>\n",
       "      <td>0.019097</td>\n",
       "      <td>28850.000000</td>\n",
       "      <td>68229.000000</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006969</td>\n",
       "      <td>0.025886</td>\n",
       "      <td>0.015451</td>\n",
       "      <td>-0.018917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>29050.0</td>\n",
       "      <td>29850.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>116276</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>29250.000000</td>\n",
       "      <td>83120.666667</td>\n",
       "      <td>28920.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001723</td>\n",
       "      <td>0.020479</td>\n",
       "      <td>0.029137</td>\n",
       "      <td>0.008643</td>\n",
       "      <td>0.027399</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>29950.0</td>\n",
       "      <td>30350.0</td>\n",
       "      <td>29550.0</td>\n",
       "      <td>29750.0</td>\n",
       "      <td>29750.0</td>\n",
       "      <td>100100</td>\n",
       "      <td>0.005068</td>\n",
       "      <td>29566.666667</td>\n",
       "      <td>96607.333333</td>\n",
       "      <td>29180.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033959</td>\n",
       "      <td>0.027259</td>\n",
       "      <td>0.025533</td>\n",
       "      <td>0.046123</td>\n",
       "      <td>0.039422</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>29900.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>29250.0</td>\n",
       "      <td>29250.0</td>\n",
       "      <td>96992</td>\n",
       "      <td>-0.016807</td>\n",
       "      <td>29533.333333</td>\n",
       "      <td>104456.000000</td>\n",
       "      <td>29350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015165</td>\n",
       "      <td>-0.006814</td>\n",
       "      <td>0.020726</td>\n",
       "      <td>0.030563</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>29200.0</td>\n",
       "      <td>30150.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>29500.0</td>\n",
       "      <td>29500.0</td>\n",
       "      <td>97549</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>29500.000000</td>\n",
       "      <td>98213.666667</td>\n",
       "      <td>29490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011915</td>\n",
       "      <td>-0.001693</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.008599</td>\n",
       "      <td>0.018820</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-03</th>\n",
       "      <td>29600.0</td>\n",
       "      <td>29800.0</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>29150.0</td>\n",
       "      <td>29150.0</td>\n",
       "      <td>55533</td>\n",
       "      <td>-0.011864</td>\n",
       "      <td>29300.000000</td>\n",
       "      <td>83358.000000</td>\n",
       "      <td>29450.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025022</td>\n",
       "      <td>-0.040342</td>\n",
       "      <td>-0.023730</td>\n",
       "      <td>0.005080</td>\n",
       "      <td>-0.010239</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-04</th>\n",
       "      <td>29000.0</td>\n",
       "      <td>29150.0</td>\n",
       "      <td>28550.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>47422</td>\n",
       "      <td>-0.012007</td>\n",
       "      <td>29150.000000</td>\n",
       "      <td>66834.666667</td>\n",
       "      <td>29290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008584</td>\n",
       "      <td>-0.015504</td>\n",
       "      <td>-0.054067</td>\n",
       "      <td>-0.018788</td>\n",
       "      <td>-0.025708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-05</th>\n",
       "      <td>28850.0</td>\n",
       "      <td>29850.0</td>\n",
       "      <td>28550.0</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>99122</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>29183.333333</td>\n",
       "      <td>67359.000000</td>\n",
       "      <td>29260.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010345</td>\n",
       "      <td>0.015319</td>\n",
       "      <td>0.015319</td>\n",
       "      <td>-0.050687</td>\n",
       "      <td>-0.025022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-06</th>\n",
       "      <td>29450.0</td>\n",
       "      <td>31150.0</td>\n",
       "      <td>29450.0</td>\n",
       "      <td>31150.0</td>\n",
       "      <td>31150.0</td>\n",
       "      <td>208599</td>\n",
       "      <td>0.052365</td>\n",
       "      <td>29850.000000</td>\n",
       "      <td>118381.000000</td>\n",
       "      <td>29640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033392</td>\n",
       "      <td>0.022728</td>\n",
       "      <td>0.040956</td>\n",
       "      <td>0.006814</td>\n",
       "      <td>0.062935</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-09</th>\n",
       "      <td>30700.0</td>\n",
       "      <td>31750.0</td>\n",
       "      <td>30700.0</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>232037</td>\n",
       "      <td>0.001605</td>\n",
       "      <td>30650.000000</td>\n",
       "      <td>179919.333333</td>\n",
       "      <td>29980.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070856</td>\n",
       "      <td>0.087011</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.051808</td>\n",
       "      <td>0.067963</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-10</th>\n",
       "      <td>31250.0</td>\n",
       "      <td>31400.0</td>\n",
       "      <td>30600.0</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>97089</td>\n",
       "      <td>-0.004808</td>\n",
       "      <td>31133.333333</td>\n",
       "      <td>179241.666667</td>\n",
       "      <td>30360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106250</td>\n",
       "      <td>0.099829</td>\n",
       "      <td>0.085695</td>\n",
       "      <td>0.025933</td>\n",
       "      <td>0.019513</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-11</th>\n",
       "      <td>30900.0</td>\n",
       "      <td>31150.0</td>\n",
       "      <td>30150.0</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>93460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31100.000000</td>\n",
       "      <td>140862.000000</td>\n",
       "      <td>30810.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096768</td>\n",
       "      <td>0.101610</td>\n",
       "      <td>0.087451</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.082192</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-12</th>\n",
       "      <td>31200.0</td>\n",
       "      <td>31250.0</td>\n",
       "      <td>30500.0</td>\n",
       "      <td>30650.0</td>\n",
       "      <td>30650.0</td>\n",
       "      <td>72367</td>\n",
       "      <td>-0.012882</td>\n",
       "      <td>30916.666667</td>\n",
       "      <td>87638.666667</td>\n",
       "      <td>31020.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080043</td>\n",
       "      <td>0.062257</td>\n",
       "      <td>0.076244</td>\n",
       "      <td>0.104649</td>\n",
       "      <td>0.086863</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-13</th>\n",
       "      <td>30700.0</td>\n",
       "      <td>31400.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>31200.0</td>\n",
       "      <td>87289</td>\n",
       "      <td>0.017945</td>\n",
       "      <td>30966.666667</td>\n",
       "      <td>84372.000000</td>\n",
       "      <td>31030.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055244</td>\n",
       "      <td>0.071400</td>\n",
       "      <td>0.080043</td>\n",
       "      <td>0.090274</td>\n",
       "      <td>0.106429</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-16</th>\n",
       "      <td>31300.0</td>\n",
       "      <td>31950.0</td>\n",
       "      <td>30600.0</td>\n",
       "      <td>31950.0</td>\n",
       "      <td>31950.0</td>\n",
       "      <td>134413</td>\n",
       "      <td>0.024038</td>\n",
       "      <td>31266.666667</td>\n",
       "      <td>98023.000000</td>\n",
       "      <td>31180.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044089</td>\n",
       "      <td>0.064643</td>\n",
       "      <td>0.084880</td>\n",
       "      <td>0.083243</td>\n",
       "      <td>0.103797</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-17</th>\n",
       "      <td>31750.0</td>\n",
       "      <td>31800.0</td>\n",
       "      <td>29550.0</td>\n",
       "      <td>29850.0</td>\n",
       "      <td>29850.0</td>\n",
       "      <td>180941</td>\n",
       "      <td>-0.065728</td>\n",
       "      <td>31000.000000</td>\n",
       "      <td>134214.333333</td>\n",
       "      <td>30940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060034</td>\n",
       "      <td>-0.001674</td>\n",
       "      <td>0.008410</td>\n",
       "      <td>0.088874</td>\n",
       "      <td>0.027166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>30100.0</td>\n",
       "      <td>29650.0</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>29700.0</td>\n",
       "      <td>63211</td>\n",
       "      <td>-0.005025</td>\n",
       "      <td>30500.000000</td>\n",
       "      <td>126188.333333</td>\n",
       "      <td>30670.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027029</td>\n",
       "      <td>0.016978</td>\n",
       "      <td>-0.001682</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>-0.008382</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19</th>\n",
       "      <td>30300.0</td>\n",
       "      <td>30300.0</td>\n",
       "      <td>29300.0</td>\n",
       "      <td>29500.0</td>\n",
       "      <td>29500.0</td>\n",
       "      <td>65571</td>\n",
       "      <td>-0.006734</td>\n",
       "      <td>29683.333333</td>\n",
       "      <td>103241.000000</td>\n",
       "      <td>30440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023373</td>\n",
       "      <td>-0.003384</td>\n",
       "      <td>0.008511</td>\n",
       "      <td>0.013289</td>\n",
       "      <td>-0.013468</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20</th>\n",
       "      <td>29500.0</td>\n",
       "      <td>30200.0</td>\n",
       "      <td>29500.0</td>\n",
       "      <td>30100.0</td>\n",
       "      <td>30100.0</td>\n",
       "      <td>71695</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>29766.666667</td>\n",
       "      <td>66825.666667</td>\n",
       "      <td>30220.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017094</td>\n",
       "      <td>0.037229</td>\n",
       "      <td>0.020135</td>\n",
       "      <td>0.010222</td>\n",
       "      <td>0.030356</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23</th>\n",
       "      <td>30200.0</td>\n",
       "      <td>30350.0</td>\n",
       "      <td>29600.0</td>\n",
       "      <td>29800.0</td>\n",
       "      <td>29800.0</td>\n",
       "      <td>72314</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>29800.000000</td>\n",
       "      <td>69860.000000</td>\n",
       "      <td>29790.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045732</td>\n",
       "      <td>0.032398</td>\n",
       "      <td>0.022053</td>\n",
       "      <td>0.020068</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-24</th>\n",
       "      <td>29800.0</td>\n",
       "      <td>30250.0</td>\n",
       "      <td>29750.0</td>\n",
       "      <td>30250.0</td>\n",
       "      <td>30250.0</td>\n",
       "      <td>50545</td>\n",
       "      <td>0.015101</td>\n",
       "      <td>30050.000000</td>\n",
       "      <td>64851.333333</td>\n",
       "      <td>29870.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011814</td>\n",
       "      <td>0.026802</td>\n",
       "      <td>0.049121</td>\n",
       "      <td>0.027213</td>\n",
       "      <td>0.042200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-25</th>\n",
       "      <td>30550.0</td>\n",
       "      <td>31100.0</td>\n",
       "      <td>30300.0</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>113381</td>\n",
       "      <td>0.024793</td>\n",
       "      <td>30350.000000</td>\n",
       "      <td>78746.666667</td>\n",
       "      <td>30130.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004898</td>\n",
       "      <td>0.009725</td>\n",
       "      <td>0.046213</td>\n",
       "      <td>0.057255</td>\n",
       "      <td>0.071877</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-26</th>\n",
       "      <td>31250.0</td>\n",
       "      <td>31750.0</td>\n",
       "      <td>30850.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>173370</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>30983.333333</td>\n",
       "      <td>112432.000000</td>\n",
       "      <td>30570.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014297</td>\n",
       "      <td>0.017502</td>\n",
       "      <td>0.059325</td>\n",
       "      <td>0.073623</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-27</th>\n",
       "      <td>31850.0</td>\n",
       "      <td>31950.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>31850.0</td>\n",
       "      <td>31850.0</td>\n",
       "      <td>112933</td>\n",
       "      <td>0.004732</td>\n",
       "      <td>31516.666667</td>\n",
       "      <td>133228.000000</td>\n",
       "      <td>30920.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030281</td>\n",
       "      <td>0.030281</td>\n",
       "      <td>0.020619</td>\n",
       "      <td>0.036775</td>\n",
       "      <td>0.036775</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-30</th>\n",
       "      <td>31600.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>31050.0</td>\n",
       "      <td>54871</td>\n",
       "      <td>-0.025118</td>\n",
       "      <td>31533.333333</td>\n",
       "      <td>113724.666667</td>\n",
       "      <td>31170.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>-0.004819</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011138</td>\n",
       "      <td>-0.006421</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>31350.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>31150.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>89955</td>\n",
       "      <td>0.020934</td>\n",
       "      <td>31533.333333</td>\n",
       "      <td>85919.666667</td>\n",
       "      <td>31460.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020952</td>\n",
       "      <td>0.032054</td>\n",
       "      <td>0.020718</td>\n",
       "      <td>0.014458</td>\n",
       "      <td>0.025560</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-16</th>\n",
       "      <td>26600.0</td>\n",
       "      <td>27100.0</td>\n",
       "      <td>26400.0</td>\n",
       "      <td>27050.0</td>\n",
       "      <td>27050.0</td>\n",
       "      <td>137544</td>\n",
       "      <td>0.024621</td>\n",
       "      <td>26650.000000</td>\n",
       "      <td>136187.666667</td>\n",
       "      <td>26740.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.172246</td>\n",
       "      <td>-0.155470</td>\n",
       "      <td>-0.158630</td>\n",
       "      <td>-0.161108</td>\n",
       "      <td>-0.144332</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>27050.0</td>\n",
       "      <td>27100.0</td>\n",
       "      <td>26450.0</td>\n",
       "      <td>26450.0</td>\n",
       "      <td>26450.0</td>\n",
       "      <td>104486</td>\n",
       "      <td>-0.022181</td>\n",
       "      <td>26633.333333</td>\n",
       "      <td>122734.333333</td>\n",
       "      <td>26660.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.147527</td>\n",
       "      <td>-0.169958</td>\n",
       "      <td>-0.185781</td>\n",
       "      <td>-0.163350</td>\n",
       "      <td>-0.185781</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-20</th>\n",
       "      <td>26700.0</td>\n",
       "      <td>26700.0</td>\n",
       "      <td>25650.0</td>\n",
       "      <td>25750.0</td>\n",
       "      <td>25750.0</td>\n",
       "      <td>146836</td>\n",
       "      <td>-0.026465</td>\n",
       "      <td>26416.666667</td>\n",
       "      <td>129622.000000</td>\n",
       "      <td>26430.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.173229</td>\n",
       "      <td>-0.209458</td>\n",
       "      <td>-0.187164</td>\n",
       "      <td>-0.168494</td>\n",
       "      <td>-0.204722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-21</th>\n",
       "      <td>26100.0</td>\n",
       "      <td>26750.0</td>\n",
       "      <td>25850.0</td>\n",
       "      <td>26500.0</td>\n",
       "      <td>26500.0</td>\n",
       "      <td>122967</td>\n",
       "      <td>0.029126</td>\n",
       "      <td>26233.333333</td>\n",
       "      <td>124763.000000</td>\n",
       "      <td>26430.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208477</td>\n",
       "      <td>-0.193268</td>\n",
       "      <td>-0.179172</td>\n",
       "      <td>-0.183279</td>\n",
       "      <td>-0.168070</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-22</th>\n",
       "      <td>26350.0</td>\n",
       "      <td>26400.0</td>\n",
       "      <td>25250.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>190156</td>\n",
       "      <td>-0.041509</td>\n",
       "      <td>25883.333333</td>\n",
       "      <td>153319.666667</td>\n",
       "      <td>26230.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.181689</td>\n",
       "      <td>-0.218408</td>\n",
       "      <td>-0.235663</td>\n",
       "      <td>-0.186424</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>25400.0</td>\n",
       "      <td>25850.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>154319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25766.666667</td>\n",
       "      <td>155814.000000</td>\n",
       "      <td>25900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>-0.223144</td>\n",
       "      <td>-0.229423</td>\n",
       "      <td>-0.235663</td>\n",
       "      <td>-0.235663</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-24</th>\n",
       "      <td>25450.0</td>\n",
       "      <td>25650.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>69122</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>25433.333333</td>\n",
       "      <td>137865.666667</td>\n",
       "      <td>25710.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.185917</td>\n",
       "      <td>-0.183954</td>\n",
       "      <td>-0.219214</td>\n",
       "      <td>-0.216441</td>\n",
       "      <td>-0.214479</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>25500.0</td>\n",
       "      <td>25800.0</td>\n",
       "      <td>25150.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>73029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25466.666667</td>\n",
       "      <td>98823.333333</td>\n",
       "      <td>25660.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.200136</td>\n",
       "      <td>-0.200136</td>\n",
       "      <td>-0.192078</td>\n",
       "      <td>-0.219214</td>\n",
       "      <td>-0.219214</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-28</th>\n",
       "      <td>25600.0</td>\n",
       "      <td>25800.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>25700.0</td>\n",
       "      <td>25700.0</td>\n",
       "      <td>71651</td>\n",
       "      <td>0.007843</td>\n",
       "      <td>25566.666667</td>\n",
       "      <td>71267.333333</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080643</td>\n",
       "      <td>-0.076745</td>\n",
       "      <td>-0.184265</td>\n",
       "      <td>-0.180040</td>\n",
       "      <td>-0.176142</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-29</th>\n",
       "      <td>25800.0</td>\n",
       "      <td>25800.0</td>\n",
       "      <td>25300.0</td>\n",
       "      <td>25450.0</td>\n",
       "      <td>25450.0</td>\n",
       "      <td>73901</td>\n",
       "      <td>-0.009728</td>\n",
       "      <td>25550.000000</td>\n",
       "      <td>72860.333333</td>\n",
       "      <td>25510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039891</td>\n",
       "      <td>-0.053550</td>\n",
       "      <td>-0.086520</td>\n",
       "      <td>-0.188440</td>\n",
       "      <td>-0.202099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-30</th>\n",
       "      <td>25500.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>24850.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>144481</td>\n",
       "      <td>-0.015717</td>\n",
       "      <td>25400.000000</td>\n",
       "      <td>96677.666667</td>\n",
       "      <td>25440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045985</td>\n",
       "      <td>-0.063790</td>\n",
       "      <td>-0.069392</td>\n",
       "      <td>-0.084557</td>\n",
       "      <td>-0.102362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>25150.0</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>94962</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>25283.333333</td>\n",
       "      <td>104448.000000</td>\n",
       "      <td>25410.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052287</td>\n",
       "      <td>-0.044366</td>\n",
       "      <td>-0.059348</td>\n",
       "      <td>-0.065408</td>\n",
       "      <td>-0.057487</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-03</th>\n",
       "      <td>25350.0</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>71629</td>\n",
       "      <td>-0.005917</td>\n",
       "      <td>25200.000000</td>\n",
       "      <td>103690.666667</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048132</td>\n",
       "      <td>-0.054067</td>\n",
       "      <td>-0.050301</td>\n",
       "      <td>-0.051885</td>\n",
       "      <td>-0.057820</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-04</th>\n",
       "      <td>25200.0</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>61542</td>\n",
       "      <td>-0.007937</td>\n",
       "      <td>25183.333333</td>\n",
       "      <td>76044.333333</td>\n",
       "      <td>25210.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070843</td>\n",
       "      <td>-0.078811</td>\n",
       "      <td>-0.054488</td>\n",
       "      <td>-0.050301</td>\n",
       "      <td>-0.058269</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-05</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>25250.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>51860</td>\n",
       "      <td>-0.002000</td>\n",
       "      <td>25050.000000</td>\n",
       "      <td>61677.000000</td>\n",
       "      <td>25110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.065788</td>\n",
       "      <td>-0.067790</td>\n",
       "      <td>-0.080813</td>\n",
       "      <td>-0.062035</td>\n",
       "      <td>-0.064037</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-06</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>25150.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>56991</td>\n",
       "      <td>0.004008</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>56797.666667</td>\n",
       "      <td>25110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043059</td>\n",
       "      <td>-0.041061</td>\n",
       "      <td>-0.054382</td>\n",
       "      <td>-0.078811</td>\n",
       "      <td>-0.076813</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-07</th>\n",
       "      <td>25100.0</td>\n",
       "      <td>25350.0</td>\n",
       "      <td>24850.0</td>\n",
       "      <td>25100.0</td>\n",
       "      <td>25100.0</td>\n",
       "      <td>46751</td>\n",
       "      <td>0.001996</td>\n",
       "      <td>25033.333333</td>\n",
       "      <td>51867.333333</td>\n",
       "      <td>25060.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048600</td>\n",
       "      <td>-0.048600</td>\n",
       "      <td>-0.025567</td>\n",
       "      <td>-0.061796</td>\n",
       "      <td>-0.061796</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-10</th>\n",
       "      <td>24950.0</td>\n",
       "      <td>25150.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>63619</td>\n",
       "      <td>-0.013944</td>\n",
       "      <td>24966.666667</td>\n",
       "      <td>55787.000000</td>\n",
       "      <td>24970.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017875</td>\n",
       "      <td>-0.025924</td>\n",
       "      <td>-0.068319</td>\n",
       "      <td>-0.045061</td>\n",
       "      <td>-0.053110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-11</th>\n",
       "      <td>24700.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>105199</td>\n",
       "      <td>0.002020</td>\n",
       "      <td>24883.333333</td>\n",
       "      <td>71856.333333</td>\n",
       "      <td>24930.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029912</td>\n",
       "      <td>-0.025872</td>\n",
       "      <td>-0.023906</td>\n",
       "      <td>-0.064665</td>\n",
       "      <td>-0.060625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-12</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>25500.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>25300.0</td>\n",
       "      <td>25300.0</td>\n",
       "      <td>80608</td>\n",
       "      <td>0.020161</td>\n",
       "      <td>24950.000000</td>\n",
       "      <td>83142.000000</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019803</td>\n",
       "      <td>-0.007874</td>\n",
       "      <td>-0.003945</td>\n",
       "      <td>-0.015873</td>\n",
       "      <td>-0.003945</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-13</th>\n",
       "      <td>25400.0</td>\n",
       "      <td>25400.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>78126</td>\n",
       "      <td>-0.015810</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>87977.666667</td>\n",
       "      <td>24970.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007843</td>\n",
       "      <td>-0.027725</td>\n",
       "      <td>-0.023811</td>\n",
       "      <td>-0.001967</td>\n",
       "      <td>-0.021848</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>56805</td>\n",
       "      <td>0.006024</td>\n",
       "      <td>25083.333333</td>\n",
       "      <td>71846.333333</td>\n",
       "      <td>24960.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031499</td>\n",
       "      <td>-0.029501</td>\n",
       "      <td>-0.017805</td>\n",
       "      <td>-0.019803</td>\n",
       "      <td>-0.017805</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-17</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>24200.0</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>99566</td>\n",
       "      <td>-0.023952</td>\n",
       "      <td>24800.000000</td>\n",
       "      <td>78165.666667</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019803</td>\n",
       "      <td>-0.042048</td>\n",
       "      <td>-0.049861</td>\n",
       "      <td>-0.023717</td>\n",
       "      <td>-0.045962</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>24400.0</td>\n",
       "      <td>24550.0</td>\n",
       "      <td>24150.0</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>39821</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24650.000000</td>\n",
       "      <td>65397.333333</td>\n",
       "      <td>24830.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030275</td>\n",
       "      <td>-0.028228</td>\n",
       "      <td>-0.040086</td>\n",
       "      <td>-0.055791</td>\n",
       "      <td>-0.053744</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>24450.0</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>24100.0</td>\n",
       "      <td>24100.0</td>\n",
       "      <td>24100.0</td>\n",
       "      <td>36244</td>\n",
       "      <td>-0.014315</td>\n",
       "      <td>24333.333333</td>\n",
       "      <td>58543.666667</td>\n",
       "      <td>24590.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036149</td>\n",
       "      <td>-0.050567</td>\n",
       "      <td>-0.038662</td>\n",
       "      <td>-0.042048</td>\n",
       "      <td>-0.056467</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-20</th>\n",
       "      <td>24050.0</td>\n",
       "      <td>24150.0</td>\n",
       "      <td>23800.0</td>\n",
       "      <td>23950.0</td>\n",
       "      <td>23950.0</td>\n",
       "      <td>99123</td>\n",
       "      <td>-0.006224</td>\n",
       "      <td>24166.666667</td>\n",
       "      <td>58396.000000</td>\n",
       "      <td>24400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046709</td>\n",
       "      <td>-0.050876</td>\n",
       "      <td>-0.056810</td>\n",
       "      <td>-0.044723</td>\n",
       "      <td>-0.048890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-21</th>\n",
       "      <td>24100.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>24050.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>63850</td>\n",
       "      <td>0.022965</td>\n",
       "      <td>24183.333333</td>\n",
       "      <td>66405.666667</td>\n",
       "      <td>24290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036664</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>-0.028171</td>\n",
       "      <td>-0.050567</td>\n",
       "      <td>-0.034106</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-27</th>\n",
       "      <td>24550.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>24050.0</td>\n",
       "      <td>24150.0</td>\n",
       "      <td>24150.0</td>\n",
       "      <td>123584</td>\n",
       "      <td>-0.014286</td>\n",
       "      <td>24200.000000</td>\n",
       "      <td>95519.000000</td>\n",
       "      <td>24230.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018164</td>\n",
       "      <td>-0.034591</td>\n",
       "      <td>-0.034591</td>\n",
       "      <td>-0.026132</td>\n",
       "      <td>-0.042560</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-28</th>\n",
       "      <td>24250.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>24250.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>98203</td>\n",
       "      <td>0.033126</td>\n",
       "      <td>24533.333333</td>\n",
       "      <td>95212.333333</td>\n",
       "      <td>24330.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034451</td>\n",
       "      <td>-0.005994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.030459</td>\n",
       "      <td>-0.002002</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>25050.0</td>\n",
       "      <td>25050.0</td>\n",
       "      <td>24350.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>59719</td>\n",
       "      <td>-0.010020</td>\n",
       "      <td>24600.000000</td>\n",
       "      <td>93835.333333</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>-0.010071</td>\n",
       "      <td>-0.014071</td>\n",
       "      <td>0.001998</td>\n",
       "      <td>-0.012073</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close  Volume  pct_change  \\\n",
       "Date                                                                            \n",
       "2018-06-20  30450.0  30450.0  28500.0  28500.0    28500.0  195667   -0.067103   \n",
       "2018-06-21  28600.0  28800.0  28300.0  28450.0    28450.0   78562   -0.001754   \n",
       "2018-06-22  28100.0  28500.0  27850.0  28400.0    28400.0   71601   -0.001757   \n",
       "2018-06-25  28050.0  28800.0  27850.0  28800.0    28800.0   59640    0.014085   \n",
       "2018-06-26  28800.0  29400.0  28650.0  29350.0    29350.0   73446    0.019097   \n",
       "2018-06-27  29050.0  29850.0  28900.0  29600.0    29600.0  116276    0.008518   \n",
       "2018-06-28  29950.0  30350.0  29550.0  29750.0    29750.0  100100    0.005068   \n",
       "2018-06-29  29900.0  30000.0  28800.0  29250.0    29250.0   96992   -0.016807   \n",
       "2018-07-02  29200.0  30150.0  28800.0  29500.0    29500.0   97549    0.008547   \n",
       "2018-07-03  29600.0  29800.0  29000.0  29150.0    29150.0   55533   -0.011864   \n",
       "2018-07-04  29000.0  29150.0  28550.0  28800.0    28800.0   47422   -0.012007   \n",
       "2018-07-05  28850.0  29850.0  28550.0  29600.0    29600.0   99122    0.027778   \n",
       "2018-07-06  29450.0  31150.0  29450.0  31150.0    31150.0  208599    0.052365   \n",
       "2018-07-09  30700.0  31750.0  30700.0  31200.0    31200.0  232037    0.001605   \n",
       "2018-07-10  31250.0  31400.0  30600.0  31050.0    31050.0   97089   -0.004808   \n",
       "2018-07-11  30900.0  31150.0  30150.0  31050.0    31050.0   93460    0.000000   \n",
       "2018-07-12  31200.0  31250.0  30500.0  30650.0    30650.0   72367   -0.012882   \n",
       "2018-07-13  30700.0  31400.0  30450.0  31200.0    31200.0   87289    0.017945   \n",
       "2018-07-16  31300.0  31950.0  30600.0  31950.0    31950.0  134413    0.024038   \n",
       "2018-07-17  31750.0  31800.0  29550.0  29850.0    29850.0  180941   -0.065728   \n",
       "2018-07-18  30000.0  30100.0  29650.0  29700.0    29700.0   63211   -0.005025   \n",
       "2018-07-19  30300.0  30300.0  29300.0  29500.0    29500.0   65571   -0.006734   \n",
       "2018-07-20  29500.0  30200.0  29500.0  30100.0    30100.0   71695    0.020339   \n",
       "2018-07-23  30200.0  30350.0  29600.0  29800.0    29800.0   72314   -0.009967   \n",
       "2018-07-24  29800.0  30250.0  29750.0  30250.0    30250.0   50545    0.015101   \n",
       "2018-07-25  30550.0  31100.0  30300.0  31000.0    31000.0  113381    0.024793   \n",
       "2018-07-26  31250.0  31750.0  30850.0  31700.0    31700.0  173370    0.022581   \n",
       "2018-07-27  31850.0  31950.0  30450.0  31850.0    31850.0  112933    0.004732   \n",
       "2018-07-30  31600.0  31700.0  31000.0  31050.0    31050.0   54871   -0.025118   \n",
       "2018-07-31  31350.0  31700.0  31150.0  31700.0    31700.0   89955    0.020934   \n",
       "...             ...      ...      ...      ...        ...     ...         ...   \n",
       "2018-08-16  26600.0  27100.0  26400.0  27050.0    27050.0  137544    0.024621   \n",
       "2018-08-17  27050.0  27100.0  26450.0  26450.0    26450.0  104486   -0.022181   \n",
       "2018-08-20  26700.0  26700.0  25650.0  25750.0    25750.0  146836   -0.026465   \n",
       "2018-08-21  26100.0  26750.0  25850.0  26500.0    26500.0  122967    0.029126   \n",
       "2018-08-22  26350.0  26400.0  25250.0  25400.0    25400.0  190156   -0.041509   \n",
       "2018-08-23  25400.0  25850.0  24950.0  25400.0    25400.0  154319    0.000000   \n",
       "2018-08-24  25450.0  25650.0  25000.0  25500.0    25500.0   69122    0.003937   \n",
       "2018-08-27  25500.0  25800.0  25150.0  25500.0    25500.0   73029    0.000000   \n",
       "2018-08-28  25600.0  25800.0  25400.0  25700.0    25700.0   71651    0.007843   \n",
       "2018-08-29  25800.0  25800.0  25300.0  25450.0    25450.0   73901   -0.009728   \n",
       "2018-08-30  25500.0  25500.0  24850.0  25050.0    25050.0  144481   -0.015717   \n",
       "2018-08-31  25150.0  25350.0  24950.0  25350.0    25350.0   94962    0.011976   \n",
       "2018-09-03  25350.0  25350.0  24950.0  25200.0    25200.0   71629   -0.005917   \n",
       "2018-09-04  25200.0  25350.0  24950.0  25000.0    25000.0   61542   -0.007937   \n",
       "2018-09-05  25000.0  25250.0  24900.0  24950.0    24950.0   51860   -0.002000   \n",
       "2018-09-06  25000.0  25150.0  24950.0  25050.0    25050.0   56991    0.004008   \n",
       "2018-09-07  25100.0  25350.0  24850.0  25100.0    25100.0   46751    0.001996   \n",
       "2018-09-10  24950.0  25150.0  24750.0  24750.0    24750.0   63619   -0.013944   \n",
       "2018-09-11  24700.0  25050.0  24700.0  24800.0    24800.0  105199    0.002020   \n",
       "2018-09-12  25000.0  25500.0  24950.0  25300.0    25300.0   80608    0.020161   \n",
       "2018-09-13  25400.0  25400.0  24900.0  24900.0    24900.0   78126   -0.015810   \n",
       "2018-09-14  25000.0  25200.0  24750.0  25050.0    25050.0   56805    0.006024   \n",
       "2018-09-17  25000.0  25050.0  24200.0  24450.0    24450.0   99566   -0.023952   \n",
       "2018-09-18  24400.0  24550.0  24150.0  24450.0    24450.0   39821    0.000000   \n",
       "2018-09-19  24450.0  24450.0  24100.0  24100.0    24100.0   36244   -0.014315   \n",
       "2018-09-20  24050.0  24150.0  23800.0  23950.0    23950.0   99123   -0.006224   \n",
       "2018-09-21  24100.0  24800.0  24050.0  24500.0    24500.0   63850    0.022965   \n",
       "2018-09-27  24550.0  24750.0  24050.0  24150.0    24150.0  123584   -0.014286   \n",
       "2018-09-28  24250.0  25000.0  24250.0  24950.0    24950.0   98203    0.033126   \n",
       "2018-10-01  25050.0  25050.0  24350.0  24700.0    24700.0   59719   -0.010020   \n",
       "\n",
       "              close_ma_3    volume_ma_3  close_ma_5  ...    returnsOO12  \\\n",
       "Date                                                 ...                  \n",
       "2018-06-20  29650.000000  242168.333333     29700.0  ...       0.009901   \n",
       "2018-06-21  29166.666667  205759.666667     29310.0  ...      -0.056089   \n",
       "2018-06-22  28450.000000  115276.666667     29160.0  ...      -0.043523   \n",
       "2018-06-25  28550.000000   69934.333333     28940.0  ...      -0.026387   \n",
       "2018-06-26  28850.000000   68229.000000     28700.0  ...       0.006969   \n",
       "2018-06-27  29250.000000   83120.666667     28920.0  ...       0.001723   \n",
       "2018-06-28  29566.666667   96607.333333     29180.0  ...       0.033959   \n",
       "2018-06-29  29533.333333  104456.000000     29350.0  ...       0.015165   \n",
       "2018-07-02  29500.000000   98213.666667     29490.0  ...      -0.011915   \n",
       "2018-07-03  29300.000000   83358.000000     29450.0  ...      -0.025022   \n",
       "2018-07-04  29150.000000   66834.666667     29290.0  ...      -0.008584   \n",
       "2018-07-05  29183.333333   67359.000000     29260.0  ...      -0.010345   \n",
       "2018-07-06  29850.000000  118381.000000     29640.0  ...      -0.033392   \n",
       "2018-07-09  30650.000000  179919.333333     29980.0  ...       0.070856   \n",
       "2018-07-10  31133.333333  179241.666667     30360.0  ...       0.106250   \n",
       "2018-07-11  31100.000000  140862.000000     30810.0  ...       0.096768   \n",
       "2018-07-12  30916.666667   87638.666667     31020.0  ...       0.080043   \n",
       "2018-07-13  30966.666667   84372.000000     31030.0  ...       0.055244   \n",
       "2018-07-16  31266.666667   98023.000000     31180.0  ...       0.044089   \n",
       "2018-07-17  31000.000000  134214.333333     30940.0  ...       0.060034   \n",
       "2018-07-18  30500.000000  126188.333333     30670.0  ...       0.027029   \n",
       "2018-07-19  29683.333333  103241.000000     30440.0  ...       0.023373   \n",
       "2018-07-20  29766.666667   66825.666667     30220.0  ...       0.017094   \n",
       "2018-07-23  29800.000000   69860.000000     29790.0  ...       0.045732   \n",
       "2018-07-24  30050.000000   64851.333333     29870.0  ...       0.011814   \n",
       "2018-07-25  30350.000000   78746.666667     30130.0  ...      -0.004898   \n",
       "2018-07-26  30983.333333  112432.000000     30570.0  ...       0.000000   \n",
       "2018-07-27  31516.666667  133228.000000     30920.0  ...       0.030281   \n",
       "2018-07-30  31533.333333  113724.666667     31170.0  ...       0.012739   \n",
       "2018-07-31  31533.333333   85919.666667     31460.0  ...       0.020952   \n",
       "...                  ...            ...         ...  ...            ...   \n",
       "2018-08-16  26650.000000  136187.666667     26740.0  ...      -0.172246   \n",
       "2018-08-17  26633.333333  122734.333333     26660.0  ...      -0.147527   \n",
       "2018-08-20  26416.666667  129622.000000     26430.0  ...      -0.173229   \n",
       "2018-08-21  26233.333333  124763.000000     26430.0  ...      -0.208477   \n",
       "2018-08-22  25883.333333  153319.666667     26230.0  ...      -0.181689   \n",
       "2018-08-23  25766.666667  155814.000000     25900.0  ...      -0.223144   \n",
       "2018-08-24  25433.333333  137865.666667     25710.0  ...      -0.185917   \n",
       "2018-08-27  25466.666667   98823.333333     25660.0  ...      -0.200136   \n",
       "2018-08-28  25566.666667   71267.333333     25500.0  ...      -0.080643   \n",
       "2018-08-29  25550.000000   72860.333333     25510.0  ...      -0.039891   \n",
       "2018-08-30  25400.000000   96677.666667     25440.0  ...      -0.045985   \n",
       "2018-08-31  25283.333333  104448.000000     25410.0  ...      -0.052287   \n",
       "2018-09-03  25200.000000  103690.666667     25350.0  ...      -0.048132   \n",
       "2018-09-04  25183.333333   76044.333333     25210.0  ...      -0.070843   \n",
       "2018-09-05  25050.000000   61677.000000     25110.0  ...      -0.065788   \n",
       "2018-09-06  25000.000000   56797.666667     25110.0  ...      -0.043059   \n",
       "2018-09-07  25033.333333   51867.333333     25060.0  ...      -0.048600   \n",
       "2018-09-10  24966.666667   55787.000000     24970.0  ...      -0.017875   \n",
       "2018-09-11  24883.333333   71856.333333     24930.0  ...      -0.029912   \n",
       "2018-09-12  24950.000000   83142.000000     25000.0  ...      -0.019803   \n",
       "2018-09-13  25000.000000   87977.666667     24970.0  ...      -0.007843   \n",
       "2018-09-14  25083.333333   71846.333333     24960.0  ...      -0.031499   \n",
       "2018-09-17  24800.000000   78165.666667     24900.0  ...      -0.019803   \n",
       "2018-09-18  24650.000000   65397.333333     24830.0  ...      -0.030275   \n",
       "2018-09-19  24333.333333   58543.666667     24590.0  ...      -0.036149   \n",
       "2018-09-20  24166.666667   58396.000000     24400.0  ...      -0.046709   \n",
       "2018-09-21  24183.333333   66405.666667     24290.0  ...      -0.036664   \n",
       "2018-09-27  24200.000000   95519.000000     24230.0  ...      -0.018164   \n",
       "2018-09-28  24533.333333   95212.333333     24330.0  ...      -0.034451   \n",
       "2018-10-01  24600.000000   93835.333333     24450.0  ...       0.004000   \n",
       "\n",
       "            returnsOC12  returnsCC14  returnsOO14  returnsOC14  Y3_1  Y3_3  \\\n",
       "Date                                                                         \n",
       "2018-06-20    -0.056281    -0.064539    -0.019513    -0.085695     0     0   \n",
       "2018-06-21    -0.061348    -0.056377    -0.059390    -0.064648     0     0   \n",
       "2018-06-22    -0.032903    -0.063107    -0.070415    -0.059796     0     0   \n",
       "2018-06-25     0.000000    -0.010363    -0.075508    -0.049121     1     1   \n",
       "2018-06-26     0.025886     0.015451    -0.018917     0.000000     1     1   \n",
       "2018-06-27     0.020479     0.029137     0.008643     0.027399     1     1   \n",
       "2018-06-28     0.027259     0.025533     0.046123     0.039422     1     1   \n",
       "2018-06-29    -0.006814     0.020726     0.030563     0.008584     0     0   \n",
       "2018-07-02    -0.001693     0.006803     0.008599     0.018820     1     0   \n",
       "2018-07-03    -0.040342    -0.023730     0.005080    -0.010239     0     0   \n",
       "2018-07-04    -0.015504    -0.054067    -0.018788    -0.025708     0     0   \n",
       "2018-07-05     0.015319     0.015319    -0.050687    -0.025022     1     1   \n",
       "2018-07-06     0.022728     0.040956     0.006814     0.062935     1     1   \n",
       "2018-07-09     0.087011     0.021053     0.051808     0.067963     1     1   \n",
       "2018-07-10     0.099829     0.085695     0.025933     0.019513     0     1   \n",
       "2018-07-11     0.101610     0.087451     0.077349     0.082192     1     0   \n",
       "2018-07-12     0.062257     0.076244     0.104649     0.086863     0     0   \n",
       "2018-07-13     0.071400     0.080043     0.090274     0.106429     1     1   \n",
       "2018-07-16     0.064643     0.084880     0.083243     0.103797     1     1   \n",
       "2018-07-17    -0.001674     0.008410     0.088874     0.027166     0     0   \n",
       "2018-07-18     0.016978    -0.001682     0.001668    -0.008382     0     0   \n",
       "2018-07-19    -0.003384     0.008511     0.013289    -0.013468     0     0   \n",
       "2018-07-20     0.037229     0.020135     0.010222     0.030356     1     1   \n",
       "2018-07-23     0.032398     0.022053     0.020068     0.006734     0     1   \n",
       "2018-07-24     0.026802     0.049121     0.027213     0.042200     1     1   \n",
       "2018-07-25     0.009725     0.046213     0.057255     0.071877     1     1   \n",
       "2018-07-26     0.014297     0.017502     0.059325     0.073623     1     1   \n",
       "2018-07-27     0.030281     0.020619     0.036775     0.036775     1     1   \n",
       "2018-07-30    -0.004819     0.000000     0.011138    -0.006421     0     1   \n",
       "2018-07-31     0.032054     0.020718     0.014458     0.025560     1     1   \n",
       "...                 ...          ...          ...          ...   ...   ...   \n",
       "2018-08-16    -0.155470    -0.158630    -0.161108    -0.144332     1     1   \n",
       "2018-08-17    -0.169958    -0.185781    -0.163350    -0.185781     0     0   \n",
       "2018-08-20    -0.209458    -0.187164    -0.168494    -0.204722     0     0   \n",
       "2018-08-21    -0.193268    -0.179172    -0.183279    -0.168070     1     0   \n",
       "2018-08-22    -0.218408    -0.235663    -0.186424    -0.223144     0     0   \n",
       "2018-08-23    -0.223144    -0.229423    -0.235663    -0.235663     1     0   \n",
       "2018-08-24    -0.183954    -0.219214    -0.216441    -0.214479     1     0   \n",
       "2018-08-27    -0.200136    -0.192078    -0.219214    -0.219214     1     1   \n",
       "2018-08-28    -0.076745    -0.184265    -0.180040    -0.176142     1     1   \n",
       "2018-08-29    -0.053550    -0.086520    -0.188440    -0.202099     0     0   \n",
       "2018-08-30    -0.063790    -0.069392    -0.084557    -0.102362     0     0   \n",
       "2018-08-31    -0.044366    -0.059348    -0.065408    -0.057487     1     0   \n",
       "2018-09-03    -0.054067    -0.050301    -0.051885    -0.057820     0     0   \n",
       "2018-09-04    -0.078811    -0.054488    -0.050301    -0.058269     0     0   \n",
       "2018-09-05    -0.067790    -0.080813    -0.062035    -0.064037     0     0   \n",
       "2018-09-06    -0.041061    -0.054382    -0.078811    -0.076813     1     0   \n",
       "2018-09-07    -0.048600    -0.025567    -0.061796    -0.061796     1     1   \n",
       "2018-09-10    -0.025924    -0.068319    -0.045061    -0.053110     0     0   \n",
       "2018-09-11    -0.025872    -0.023906    -0.064665    -0.060625     1     0   \n",
       "2018-09-12    -0.007874    -0.003945    -0.015873    -0.003945     1     1   \n",
       "2018-09-13    -0.027725    -0.023811    -0.001967    -0.021848     0     1   \n",
       "2018-09-14    -0.029501    -0.017805    -0.019803    -0.017805     1     1   \n",
       "2018-09-17    -0.042048    -0.049861    -0.023717    -0.045962     0     0   \n",
       "2018-09-18    -0.028228    -0.040086    -0.055791    -0.053744     1     0   \n",
       "2018-09-19    -0.050567    -0.038662    -0.042048    -0.056467     0     0   \n",
       "2018-09-20    -0.050876    -0.056810    -0.044723    -0.048890     0     0   \n",
       "2018-09-21    -0.020203    -0.028171    -0.050567    -0.034106     1     1   \n",
       "2018-09-27    -0.034591    -0.034591    -0.026132    -0.042560     0     1   \n",
       "2018-09-28    -0.005994     0.000000    -0.030459    -0.002002     1     1   \n",
       "2018-10-01    -0.010071    -0.014071     0.001998    -0.012073     0     1   \n",
       "\n",
       "            Y3_5  Y3_7  Y3_10  \n",
       "Date                           \n",
       "2018-06-20     0     0      0  \n",
       "2018-06-21     0     0      0  \n",
       "2018-06-22     0     0      0  \n",
       "2018-06-25     0     0      1  \n",
       "2018-06-26     0     1      1  \n",
       "2018-06-27     1     0      0  \n",
       "2018-06-28     1     0      0  \n",
       "2018-06-29     1     1      1  \n",
       "2018-07-02     1     1      0  \n",
       "2018-07-03     0     1      0  \n",
       "2018-07-04     0     1      1  \n",
       "2018-07-05     0     1      1  \n",
       "2018-07-06     1     1      1  \n",
       "2018-07-09     1     1      1  \n",
       "2018-07-10     1     1      1  \n",
       "2018-07-11     1     1      1  \n",
       "2018-07-12     1     1      1  \n",
       "2018-07-13     1     1      1  \n",
       "2018-07-16     1     1      1  \n",
       "2018-07-17     0     0      1  \n",
       "2018-07-18     0     0      1  \n",
       "2018-07-19     0     0      0  \n",
       "2018-07-20     0     0      0  \n",
       "2018-07-23     0     0      0  \n",
       "2018-07-24     1     0      0  \n",
       "2018-07-25     1     0      0  \n",
       "2018-07-26     1     1      1  \n",
       "2018-07-27     1     1      1  \n",
       "2018-07-30     1     1      0  \n",
       "2018-07-31     1     1      1  \n",
       "...          ...   ...    ...  \n",
       "2018-08-16     0     0      0  \n",
       "2018-08-17     0     0      0  \n",
       "2018-08-20     0     0      0  \n",
       "2018-08-21     1     0      0  \n",
       "2018-08-22     0     0      0  \n",
       "2018-08-23     0     0      0  \n",
       "2018-08-24     0     0      0  \n",
       "2018-08-27     0     0      0  \n",
       "2018-08-28     0     0      0  \n",
       "2018-08-29     1     0      0  \n",
       "2018-08-30     0     0      0  \n",
       "2018-08-31     0     0      0  \n",
       "2018-09-03     0     0      0  \n",
       "2018-09-04     0     0      0  \n",
       "2018-09-05     0     0      0  \n",
       "2018-09-06     1     0      0  \n",
       "2018-09-07     0     0      0  \n",
       "2018-09-10     0     0      0  \n",
       "2018-09-11     0     0      0  \n",
       "2018-09-12     1     1      0  \n",
       "2018-09-13     0     0      0  \n",
       "2018-09-14     0     1      0  \n",
       "2018-09-17     0     0      0  \n",
       "2018-09-18     0     0      0  \n",
       "2018-09-19     0     0      0  \n",
       "2018-09-20     0     0      0  \n",
       "2018-09-21     0     0      0  \n",
       "2018-09-27     0     0      0  \n",
       "2018-09-28     1     0      1  \n",
       "2018-10-01     1     1      0  \n",
       "\n",
       "[70 rows x 69 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_[stock][X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame()\n",
    "for stock, model in model_set.items():\n",
    "    a = model.predict(validation_[stock][X])\n",
    "    a = pd.DataFrame(a, index = validation_[stock].index, columns=['{}'.format(stock)])\n",
    "    pred = pd.concat((pred, a), axis=1)\n",
    "    pred = pred.fillna(0)\n",
    "col = pd.MultiIndex.from_product([['pred'],pred.columns])\n",
    "pred.columns = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"21\" halign=\"left\">pred</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AK홀딩스</th>\n",
       "      <th>BNK금융지주</th>\n",
       "      <th>CJ</th>\n",
       "      <th>CJ CGV</th>\n",
       "      <th>CJ대한통운</th>\n",
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       "      <th>DB하이텍</th>\n",
       "      <th>GKL</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>현대위아</th>\n",
       "      <th>현대제철</th>\n",
       "      <th>현대중공업</th>\n",
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       "      <th>현대홈쇼핑</th>\n",
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       "      <th>후성</th>\n",
       "      <th>휴켐스</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 182 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pred                                                           \\\n",
       "           AK홀딩스 BNK금융지주   CJ CJ CGV CJ대한통운 CJ제일제당 DB손해보험 DB하이텍  GKL   GS   \n",
       "Date                                                                        \n",
       "2018-06-20   0.0     0.0  0.0    0.0    0.0    0.0    1.0   1.0  0.0  0.0   \n",
       "2018-06-21   0.0     0.0  0.0    0.0    1.0    0.0    1.0   0.0  0.0  0.0   \n",
       "2018-06-22   0.0     0.0  0.0    1.0    1.0    0.0    0.0   1.0  0.0  0.0   \n",
       "2018-06-25   0.0     0.0  1.0    1.0    1.0    1.0    0.0   1.0  0.0  0.0   \n",
       "2018-06-26   0.0     0.0  0.0    1.0    1.0    1.0    0.0   1.0  0.0  1.0   \n",
       "\n",
       "           ...                                                       \n",
       "           ...  현대위아 현대제철 현대중공업  현대차 현대해상 현대홈쇼핑 호텔신라   효성   후성  휴켐스  \n",
       "Date       ...                                                       \n",
       "2018-06-20 ...   0.0  0.0   0.0  0.0  1.0   1.0  0.0  0.0  0.0  0.0  \n",
       "2018-06-21 ...   0.0  0.0   0.0  0.0  1.0   1.0  0.0  0.0  1.0  0.0  \n",
       "2018-06-22 ...   0.0  0.0   0.0  0.0  1.0   1.0  1.0  0.0  1.0  0.0  \n",
       "2018-06-25 ...   0.0  1.0   1.0  0.0  0.0   0.0  1.0  0.0  1.0  0.0  \n",
       "2018-06-26 ...   0.0  0.0   1.0  1.0  0.0   1.0  0.0  0.0  1.0  1.0  \n",
       "\n",
       "[5 rows x 182 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = pd.DataFrame()\n",
    "for stock in model_set.keys():\n",
    "    y_true = pd.concat((y_true, validation_[stock]['sh1_Y3_5']), axis=1)\n",
    "y_true.columns = list(model_set.keys())\n",
    "y_true = y_true.fillna(0)\n",
    "col = pd.MultiIndex.from_product([['y_true'],y_true.columns])\n",
    "y_true.columns = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2018-06-20</th>\n",
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       "    <tr>\n",
       "      <th>2018-06-22</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
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       "      <td>0.0</td>\n",
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       "<p>5 rows × 182 columns</p>\n",
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      ],
      "text/plain": [
       "           y_true                                                           \\\n",
       "            AK홀딩스 BNK금융지주   CJ CJ CGV CJ대한통운 CJ제일제당 DB손해보험 DB하이텍  GKL   GS   \n",
       "Date                                                                         \n",
       "2018-06-20    0.0     0.0  0.0    0.0    1.0    0.0    1.0   0.0  0.0  0.0   \n",
       "2018-06-21    0.0     0.0  0.0    0.0    1.0    0.0    1.0   1.0  0.0  0.0   \n",
       "2018-06-22    0.0     0.0  0.0    1.0    1.0    0.0    0.0   1.0  0.0  0.0   \n",
       "2018-06-25    0.0     0.0  0.0    1.0    1.0    0.0    0.0   1.0  0.0  1.0   \n",
       "2018-06-26    0.0     0.0  0.0    1.0    1.0    0.0    0.0   1.0  0.0  0.0   \n",
       "\n",
       "           ...                                                       \n",
       "           ...  현대위아 현대제철 현대중공업  현대차 현대해상 현대홈쇼핑 호텔신라   효성   후성  휴켐스  \n",
       "Date       ...                                                       \n",
       "2018-06-20 ...   0.0  0.0   0.0  0.0  0.0   1.0  0.0  0.0  1.0  0.0  \n",
       "2018-06-21 ...   0.0  0.0   0.0  0.0  1.0   1.0  0.0  0.0  1.0  0.0  \n",
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       "\n",
       "[5 rows x 182 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7997645211930926"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "(y_true.values == pred.values).sum() / pred.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_col_df(df_name, fillna=None):\n",
    "    df = pd.DataFrame()\n",
    "    for stock in model_set.keys():\n",
    "        df = pd.concat((df,validation_[stock][df_name]), axis=1)\n",
    "    df.columns = list(model_set.keys())\n",
    "    if fillna == 'mean':\n",
    "        df = df.fillna(df.mean())\n",
    "    else:\n",
    "        df = df.fillna(0)\n",
    "    col = pd.MultiIndex.from_product([['{}'.format(df_name)],df.columns])\n",
    "    df.columns = col\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.concat((pred, y_true), axis=1)\n",
    "res = pd.concat((res, get_col_df('Close', 'mean')), axis=1)\n",
    "res = pd.concat((res, get_col_df('Open', 'mean')), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <td>44500.0</td>\n",
       "      <td>54600.0</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>130500.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>113500.0</td>\n",
       "      <td>120500.0</td>\n",
       "      <td>45787.735849</td>\n",
       "      <td>9430.0</td>\n",
       "      <td>30450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44150.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>129500.0</td>\n",
       "      <td>36550.0</td>\n",
       "      <td>111000.0</td>\n",
       "      <td>124000.0</td>\n",
       "      <td>45787.735849</td>\n",
       "      <td>9750.0</td>\n",
       "      <td>28600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42450.0</td>\n",
       "      <td>54900.0</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>35100.0</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>45787.735849</td>\n",
       "      <td>10050.0</td>\n",
       "      <td>28100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>43250.0</td>\n",
       "      <td>54800.0</td>\n",
       "      <td>110500.0</td>\n",
       "      <td>129500.0</td>\n",
       "      <td>36250.0</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>122500.0</td>\n",
       "      <td>45787.735849</td>\n",
       "      <td>10100.0</td>\n",
       "      <td>28050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42300.0</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>110500.0</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>35300.0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>45787.735849</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>28800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 728 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pred                                                           \\\n",
       "           AK홀딩스 BNK금융지주   CJ CJ CGV CJ대한통운 CJ제일제당 DB손해보험 DB하이텍  GKL   GS   \n",
       "Date                                                                        \n",
       "2018-06-20   0.0     0.0  0.0    0.0    0.0    0.0    1.0   1.0  0.0  0.0   \n",
       "2018-06-21   0.0     0.0  0.0    0.0    1.0    0.0    1.0   0.0  0.0  0.0   \n",
       "2018-06-22   0.0     0.0  0.0    1.0    1.0    0.0    0.0   1.0  0.0  0.0   \n",
       "2018-06-25   0.0     0.0  1.0    1.0    1.0    1.0    0.0   1.0  0.0  0.0   \n",
       "2018-06-26   0.0     0.0  0.0    1.0    1.0    1.0    0.0   1.0  0.0  1.0   \n",
       "\n",
       "             ...        Open                                                  \\\n",
       "             ...        현대위아     현대제철     현대중공업       현대차     현대해상     현대홈쇼핑   \n",
       "Date         ...                                                               \n",
       "2018-06-20   ...     44500.0  54600.0  105500.0  130500.0  38000.0  113500.0   \n",
       "2018-06-21   ...     44150.0  55000.0  107000.0  129500.0  36550.0  111000.0   \n",
       "2018-06-22   ...     42450.0  54900.0  105500.0  126000.0  35100.0  112000.0   \n",
       "2018-06-25   ...     43250.0  54800.0  110500.0  129500.0  36250.0  112000.0   \n",
       "2018-06-26   ...     42300.0  56300.0  110500.0  128000.0  35300.0  112500.0   \n",
       "\n",
       "                                                      \n",
       "                호텔신라            효성       후성      휴켐스  \n",
       "Date                                                  \n",
       "2018-06-20  120500.0  45787.735849   9430.0  30450.0  \n",
       "2018-06-21  124000.0  45787.735849   9750.0  28600.0  \n",
       "2018-06-22  123000.0  45787.735849  10050.0  28100.0  \n",
       "2018-06-25  122500.0  45787.735849  10100.0  28050.0  \n",
       "2018-06-26  121500.0  45787.735849  10000.0  28800.0  \n",
       "\n",
       "[5 rows x 728 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16076af4908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = ((res.Close - res.Open)/res.Open)+1\n",
    "p.iloc[0]=1\n",
    "p = p.cumprod()\n",
    "p.plot(grid=True, legend=False)\n",
    "p.mean(1).plot(grid=True, lw=5, color='#00F0F0')\n",
    "plt.title('Total, 수익률', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w+ruuImBp7GQpICwBIlIgdjSMnK/+jkhVI7KSpvbtN1D9yivY6uuJu/l3TtkPBKdEQkr5CCo81JedHbixm+0b6ZSA7o+tRjNo/PAs1O2HKz4Gg1Fti0qDUceri8nxtx8cWvlLoToPXjwRrC1w3M3w3ZNQt8/bXnVPu0hEpPZtGxoL4xaqOROn3AtG/642X9wOuz7qs6lxANkCwpPURT/+CBh9okMAOohAWNKA50BYKyupee3fRCw6neBJnp+ZPcRmkmg0XsRUAev+BUecrso5dGT6ZfDfa2HvtzDmZG945z22vavCJr/9AeLGKiGtHaoisR9CE8A/yDn76ZfB7k8h5ysY3ynUIyXs/x7Gnwkn3dVrM99v3sbsU87uXmjcTNVzzyPb2oi/+WaPnwu0SGg0B1n9AFjNcOr9XfdNOFNdfN66SE3GmvdHaDPDro8hZ6UKb0w6W83mPdzI+RqGHQUJ49XryGGqtzUUqS9yLh/RzpiT1fea+VZXkajbrybdjVrQZ52n1qDSQREIS2Ehte+9R9T55xGQnu7x84EWCY0zNJapu+xk5+KlPkn5Tvj5dTj6Bogb03W/fzBcnwHr/6Xsfn4dNVZDQOpMFbfe9RH4BXGsMRy29HInKwSkzlKiMmZoLHbfI6ZKKPkZTvjbwW1RI4Z2uCl+vPP2Rj+YdiH88Jx6r2HxB/cVbVTPaUe718cBUPnEkwg/P+Ju/O2gnVOLxOFG9krY+DJc/I77Yudf3A65q+DW7KF9QXMVKWHlXyEwAo7/U892kalwxmMwdylsfg1C42HCYohIBrtNCUXWF9QW7CE5KbnndqwtsDcDdv4X/EPhzMfVhWoo0l66YuwpB7dFj4CsL73jT29IqYr79bc3N/1S+O4pNXJrdoeLb+FP6vtJGBoVWVt276bhf/8j9rrr8E9MGLTzapE43NjxXxX+qMqChC7zEPuP3QZ7V6tRGtlfwOTzBt7mUCN7pbpoL3wYQmL6to9MhRP/dug2g1GNVBk5n6yMDJIXLOi9DZsV9q2HNY/ARzdCYFjXcMdQIOcrFY5JmnZwW9QIaKoAS/PQumlorlHhwv6Em0D9n6Qcqcp0HHvjwRXhCn+E1COHTBHAimXLMERGEnvdtYN63l/YMI1fAKVb1XPhT+5pr2SLSloCbHvfPW0OJWxtahhk7Bg4ahD/+Yx+KtZ9yXuQMgPevxry1w7e+Z3BZlU9yDEnH9orjRqhnjvmJdpa1O+jJFMd5w3q20uE91MkQOWZyndA2Tb12tKsXg87yn3+DYDmjRtpWrOWuOuuxRgxuPNTtEgcTrSZoSpb/V3kJpHIWw0I1SXP/Xpo1bpxBxtfhuoclawehMRjFwLD4NL3VZXSty+G4p8H34eeKN6sFunpGGoCFW6CQ/MS295Vo7+WHw8Pp8Gri+Dru2HPZyqfNRj0Z45EZ6acr8qLb3EU/SvZAnYrpHm/vqiUkorH/oVfQgLRl1466OcfGv0ojXso3wXSBn7BULjRPW3uXa0S1sfcoLrjOz+Eo65xT9veprkGMh6CkcfDEQu950dIDFz+IbxyKvznPPj1l0Nj1bScr0AYuk6a664nUfIzBEXCGf+Cok3qJuX7Z2DDsoPHDDtKLctpdH6ewLDCXPh+l3PGhT+oZ1dEIjhahfu2vwen3nfwJmsI9CRMq1djzswk6d57MQQHD/r5tUgcTpRmquepF6jRN+Za9eN3ldZGFbaa/X9qJmjcOJXcO1xEYu2jKpR22gMH49DeIiIZLv8IXlkIr58N16yEqOHe9Sn3a3Un3fk3FJagbkRqCw5uK9miwmZTzlcPUD3b0q1qlFDhTyqxv2NFv1wYA5DXjwOihjuXV+qO6ZeqwQTZXyp/Y0arCXdeRNpsVD7+OAHp6USdd65XfNAicThRtg2ComDy+UokijZ1DRX0h4INYG9Td5JCKPH59n51B+ntC5g72P6+GoY6VBadjx2tehSvLlJCceQVHj1d2v69sD6z+532NnWB724SmRDq+28PN1lbVS929v8daucfDMOPVQ9Qo49aG0HanfZx/fr1zJ0712l7AkJdF/zRJ0B4sgo5FW8e2P+Om6j/5FNac3JJXfY4ws87l2stEocTpdtUaCh1pgoTFP40sB/63tXgFwRpjn/yKQ6R2L4C5i11j8/ewm6H5mp1tziUSJoMl74Hby1RNaQ8yGiAvb0YGANh/OLu90WPODjrunynEpWU6b2fUIh+FwW0+oepdaoHA4NR1ela/7h67eVQk91iofKpJwmaNInwU703SVOLxOGCrU39sx59nUqGJkwaePI6bzWMOO5giYPodBV+OBxEoqVO3dGGeDec0C3Dj4Xb8tR36kHWrlvH/HnzejYw+PVcZyhquBoiCgfDnCkz3OugN5h+6UGR8HLSuu6dd7CWlJJy//0IL9YL0yJxuFCVDbZWSHaMZ087Gra9p+Y5tBeq6w/1xWquxZGXH7p94llq4lltgRINX8XsWFTR1fi1pzH6e3y0ld0Y6Po8h6gRKp9jrlP5iKCogwltXyZuLAw7Gip2u2eekYvYTCaqnnuekNnHEnrccV7zA/QQ2MOHUsf47o4iYWmEyj2utbc3Qz2P6jSypX0U0FCccdsfmh0LIA7FnoQv0HEYbEmm6kV4O/nvLn71JFzwmms3V27AWlNDxaP/xFZbS8JS7/fYtUgcLpRtA/8QNSkMDsZTXZ1Ul/etmmmb2KkkQexoiDvi4LoCvkr7fI+h2pMY6rT3GqpyoGJX3/kIXyJhAowd/Eq/pvUb2Hf11eTMm0/du+8See65BE/x/qAKHW46XCjdqi7o7Xc/MaPUXXLhTzDr6v61ZbernsToE7u/Oxx3uhoD31Kvxsb7Iu09iWAtEi7R3pPI+lxNOjsc8hFepK20lKLf/Q6/6Ghir72WiIWnETi+H4UKPYgWicMBux3KtqvRR+0IoWKrriSvy3dAc1XPK48dcTpseEKVbJjsGLtdskVNtOsPoxYoIfIGOtw0MIKjITBS1b0CSD6MehJeoPyhh0FKRrzxOv6pTiyYNIg4u8Z1FPA8kIQKUV0JBADPAkHAd1LK2xy29wHzHW1fL6XcKYQYN1BbTS/UFah1cpOnHbp92CxVlM9c179hhHtXq+dRC7rfn3a0ugPP/lKJhKlSzRQ21zmfbLXb4Ptn4dcrYdhM531zF+YaNXonMHzwz324EDUcyrer38LhMG/GS5jWrafxq6+Iv+WWIScQ4HxPIgRYKqUsEUKcAdwKjAKukVIWCCHeF0IcgxKORCnl8UKIycCjwCJg2UBspZQ/uvVdH260F/XrvN5Du2iU74T0Oc63l7da1eSPSOl+v8GoyjHnrFTF3D5bqiZJ3fjdwYVp+qK5Bl44Ht6/Cn6zdmAzw12huVr1Ig6XZKs3iB6hRCJluv4cXcRusVB+//0EpKcT8+t+hoUHCacS11LKEillieNlLdAKBEkpCxzbPkCtS30q8LbjmB1AjBDCzw22mt4o3abuihMmHrq9fSZx2Xbn22ozqyUb+woDjVuohpF+eTvs/gRO+KvzAgEqYXzBq9BYCh/9n5qNO5g01+h8xEBpT17rfITL1LzyCpZ9+0i84w4MAQNb+9pT9CsnIYRIRfUifgc80WFXNTABSAAqO2y3AomO/QOx7ezH9cD1AImJiWRkZPTnbQwaJpNpUHybsiuDwOA0Nq3/vsu+4/yjqM5cSVZLzxfwjn5G12QyzdrCtqZYanrx3WgNZI7ww7DxJRrCj2CLZSrShfeaOupKxma9RMFrv6Eg/UI1U9wJPwfK9NK9gJFMD3w/g/W9D5SB+plaZWEssKPGnyoPvd/D+bM0VFUT98yztB55JJusbTAI79MVP50WCSHEmcBi4DqgGegY5I5GXfCDHX+3Ywdq3GB7CFLK5cBygFmzZskFfS3w4iUyMjLwuG9Swka1Gle35yqcSXJTRa+L4Bzi51erwODP1MU3qjo4vVEyD/Z9R8QVb3J8/BEu+n88fFBH+o53SBfFcNbTPU7Sc+vnudMOcaM98v0MyvfuBgbsZ0UiNG1k8qLrPVYI73D+LAv/7yaa/P2Z/Ng/8U/uZSVDN+KKn06Fm4QQU4HFUsobpJTVUkozEOjoWQCcC6wC1gHnO46ZCBS5yVbTE41l0FSpqrR2R9IUqNgDVotz7e1drcoR9CUQAIuXwdWfg6sCASqWfd5LsPgJNSnr2eNg18eut+cs7TkJjeskTIAb1ni9Uqov0piRgWnVKuJ/e+OgCYSrONuTWAjME0JkOF7vB5YCK4QQrcAnUsrdQogsYJEQYh3QCNzgsB+Q7QDf4+FNWaeZ1p1JmqKKr1Xu6ZrY7oypUuUvTrzDuXNHp7unNIcQMPMqGH0SvHG2Gl478ayBt9sTUqqchBYJjRewt7RQfv8DBIweTcwVnq306w6cEgkp5SPAI93smt3Jzg7c2M3xGwdiq+mF0m2AUNVDu6NdPMq29y0S+WvU8ygvzV2ISlMzxfPXefY8LfVqcSaduNZ4geqXXqatqIjhr72KGKLJ6o7oshy+Tmmmml3d03j/mFGqXIczI5zyVqtCbd4ssRCWCKZyz4520hPpNF7Csn8/1cuXE7FoEaHHHuttd5xCi4SvU7at9x6CwajKdfQlElKqek2jjvdaYTMAwpNUeMyTa2kP9QqwmsMSKSVlDzyA8PMj4fbbve2O02iR8GXMtWqVuJ7yEe0kTVEi0dvdeVU2NJZ0rfo62IQlqmdTmefOoXsSGi9g+vZbmtasJe53v8M/McHb7jiNFglfpr08eE8jm9pJmgqt9QeXm+yOPEcpjp7qNQ0W4UnqudGTIuHopQz2LG/NLxa72Uz5Aw8SOHYMMZdd6m13+oUu8OfL9DWyqZ12ESnb3vNopL2rIXqk9xcSOtCTKPfcOXRPQjPIVL3wAm0lJYx443WEv2cXk3I3uifhy5Rug4hUCI3r3S5hgprJ3N7z6ISwt0HBeu/3IuCgSHiyJ2GuAWH03TLnGp+iNT+fmpdfIeJXiwk5yrvrZruCFglfpnRr36EmUEtUxo7tMXkd0ZANFpP38xGg1ucOCPN8TyIkRhel03gcKSXl9z+ACAwk8TbfLGitRcJXsTRDdU7fcx/aSZ56MDzViejaTNXTGDnfjQ4OgLBED+ck9GxrjXPYzWastbUuH9/41dc0bdhA/M034xcf70bPBg8tEr5K+U6Qdud6EqBKfzcUg6Wpy66YmkxIndm/NSc8SXiSh3sStXoincYpyh98iPyzz8He0tLvY+3NzZQ/9BCB48cTfcnFHvBucNAi4auUta8h0UfSup32hHRtwaHbzbWEN+YOjVBTO4PSk9Aioekb884dWMvLqXv33X4fW/Xcc1jLyki6606En++OEdIi4auUblVDOCOHOWcfM1I9dxaJ/HUI7EMjad1OeBKYKjzXvrlGi4SmT6TdjmVvPgBVL76E3Wx2+tjWvDyqX32NyHPOIeTIIz3l4qCgRcJXKd2mQk3OJl+jexCJfRuwGQJVzaShQlgitDWp1e7cjZQ6J6FxiraSEmRLC5FnnYWtqorad5zrTUgpKbvvfgwhISTc+kcPe+l5tEj4IrY2qNjlfKgJHAvXR0BN/qHbK/fQFJrm/NrUg8GBCXUeyEu0NoLdqnMSmj5pzc0FIOrCCwmZfSzVLznXm2j84guaf/iB+Ft+j1+sZ25G7DYbW7/+gpyfvvNI+x3RIuGLVO4Bm6V/IiGEykt07klUZtMc4mTIarDwZGkOPZFO4ySWvDwAAkePIv6mm7BVV1P79ju9HmMzNVH+8D8ImjiR6AsvdLtPUkryNv/Ev2+7iW9eeobsHza4/Ryd8d1syi8ZZ8txdCY6HSo6LM/R2giNJTTHneQ219yCJ0tzmB0lObRIaPqgNTcPY3wcxshIQmbOJPS446h+8UWillyAMSys22OqnnkGa2Ulw556EmF0b6HM8r25rHnjZQp3bSc6OZVf3fo3xszyfCVZLRK+SNk28A+F2NH9Oy5mJGR/CXabqvRalQ1Ac0hqHwcOMp4szdFet0knrjV90Lo3j8DRYw68jl+6lILzz6f6pZdIuOWWLvYt2dnUvP46UeefT/C0fvTy+6ChsoL177zO7vUZBIdHcOKvf8PUkxZiHKQRU1rSYIVhAAAgAElEQVQkfJHSrWqRof6W9I5OV2GqxlI1KqqyXSTS3O/jQAiOBmOAZ3oSOtykcQIpJZbcPCLPPvvAtuDJk4g480xqXvs30RdfjH9iYscDKP/7fRjDwohf+ge3+NDSZOLHD99jy5efIhAcffYFHH3W+QSGOLG0sBtxdo3reCHEA0KI+xyvLxdC7BJCZAghvupgd58QYo0QYoMQYpJj2zghxCrHtkddsdV0wG5X5TX6G2qCrnMlqrLB4Ic5OMld3rkHIQ4uPuRudAXYIUGr3c6eJueHlA421vJy7E1NBI45tLcef8stYLNR+eSTh2wP+mkjzZs2Eb90KX7RA/ttmU2N/PTxCl6++To2/e9Dxs2ex9XLXmDexVcOukCA8z2Jx4BcIMTxOgr4i5TywIr1Qoh5QKKU8nghxGTgUWARsAy4RkpZIIR4XwhxDBDgrK2U8kd3vNHDhtp8VWfJ2XIcHWkfBluTD+lzlUjEjEIahmCH0lMT6pqrVQmSoCEyu/wXiNUuuWZHAauqG1h11DgmhgV726UutOaqpHXAqENFImBYKtGXXUbNa68Rc8WVBI07AltjI2EffEDQ1KlEXXB+v84jpaSlyYSpuoqGqgpyfvyerO/WUhESQeqEqVx23hISR/YzrOxmnF3j+gohxAJgoWNTFLC1k9mpwNsO+x1CiBghhB8QJKUscNh8gFq/OrYftl1EQghxPXA9QGJiIhkZGc68jUHHZDK53bf4ivVMAjYVWzE19K9tYbcyHwP7M9eQ35DG0fu20BSa5hE/B8qkViMhtXvZ2MEvd/g5Nm8H8X5hfLd27cAc7IWh+Hl2hzf8lBJeJZhvCEQgeXjjNn4teu5ReOuzDF71LRHApvIyZKfzi8mTiAsOIu/SS7GHhSFaWjA0NlJ8xiL2dfhdSSmxtbZgMTViMTXS1tTY4W/TgW12q/XAMQY/f+qOms8bU4/HKgTfFVRxfkERUcI9y/m68nm6egvpBzwihGgD3pBSLgcSgMoONlYgEajusK0amNBP2y44zrccYNasWXLBggUuvg3PkpGRgdt9+3o1GPyZdfpl4OfCIurb0hgRIRkxbw6sLSdk1kWEGcPc7+dAMX0CO7IO8cstn2fFK2BJ8uj79cj37gG84efz+yv4Kq+EG9PiqbRYWVlVz3NzZhHaw0ggb32Wpd+upjEqivmLFyO6mbBqioyi7oMVSCmpaWslJ9CfuAAw79hMY001jdWVmKqrsbZZDjlOGAyERccSFhtL+PARhMfEEh4bR2h0BCbD++wNncWTNUcxIsifudHhvFFSxfeGYG4ansANaQmEGAc2a8GVz9MlkZBS3g3cLYQIAT4WQmwA6oGOwTg7UIPqdbQTjRKH4H7YajpStg0SxrsmEKBGONXmQ81eNaks7ghwvcil5whPgpY6sLaCX6D72m2u0UnrQaTJamNLYzOb65vZ1NDEN9UNnBEfyZ2jU9hY38SK8lo+Lq/jkpSh9Z205uURMGZ0twIBsDwynoyTf8WcjA/xKy0EoKBsH2ExsYTFxJE4cgxjjppNeEwsYbFxhMfEER4bR0hUFIZOA04slhp+3vobVjf683zzJNKCbfx3xgTiA/y5dlgcD+4t5R/5Zfy7uJrbRyWxJCkG4yCWuXdJJIQQflJKK2AGGgEJrAPOB9YJISYCRVJKsxAiUAiRKqUsBs4F7gXG9MNW046Uao7EuIV92/ZEdDrs/vTA8FclEg1ucc+tdBwGGzXcfe0213h/9b3DFCkle82tbKpvZnNDE5sbmthtasHu2D8mJJArU+O4a3QKBiE4OjKUI0KCeL2kekiJhBrZlEv4aacduqN2Hy17VvHNhj08PuU82kKi2Xrq5dzQuJuFdTuYPm0yBkPnO/1G9TDlgwnosIJwnd3AlxbBipZGfpa/p1mEkkYRd7Y+Q9yOi0EYGA28DPwYFsK95iT+sKeN5dk53B1cxgJ/E0SNgJHzPPp5uBpuekgIcbTj+A+llLuEEHuARUKIdahP5gaH7VJghRCiFfhESrlbCJHlrK2L/h2eNJRAcxUkDWAMdnS6St4Wb1av446AnE1ucc+tdCzN4S6RaCiFmjyP/1P90mix2Xm+sILlRZXUtNkACDMaODIihN+PSGRWZCgzIkKI8T/0ciOE4IrUWO7IKWZ7YzNTwkO6a37QsdXUYKuv7zKyKfeF37Jym5XvJsyjLSCQZ7bdy3vDFvFkzFGs9gvinC3fcGblGoa39jzgoiAoha9ij2Nl7Bx+iJqGTRiJlHUsql7D4rJ1jDdsI3dCEMUbb2NY6cHy5McAnwGfxJ/AgyOv4yJ7OgtqfuIu8QkTh4pISCkzgAzH312WWJJS2oEbu9m+EZWAdslW04EDa1q7MLKpnfYRTtlfQcQwtRLcUMQTpTlWP6AmEh7zG/e1+QtGSsnnVfXck1tCYYuFU2MjOC0ukiMjQjgiNMipkMj5idHcn1fCGyXVPDLO8yJR/8knVL/0MkETJxI8fTrB06cROHbsIbOjD4xsGn1QJBpyNvH5VhuR0ZHkzF/ErGA/zrvyCc6VkndqLDxVMJy/j/4tfx/9W6aFGFkcFcDiqACGBRjY0mxlZX0bK+vbyG5RIjrWv5XFbZ9yjDGH88feSHjwZcBlSCmpzr2VvPHZJJz7OgH+B6PyAjgLWGiXvFbVyuPGY/hnxHxe8fBnNgTHPmp6pDJLPSdMdL2N9lBLxc6htYZEZ9xdmqN8F2S+CcfceLBsuqZHMmoa+Gd+Gc9OHMHw4K45od0mM3fmFLO+zsT40CDenzaaeTHh/T5PlL8fixOi+KC8lrtGpxDm595SFp0xrVmLZf9+rFVV1H/0EQCGkBCCpkw5IBqtWer/LHCMmm0tpWTlc48Dgqjf/pX9lfDXEakQFY0ALo6G5MJi0o+Zwf8q6vi4oo77S8zcX2LGT4BVghGYHRXG5cMimWpbR1P+nwkLG8+0qS8TGHhwxToBjJv4MD/+dAab8/5EQsJpxMbMJyJiGgaDKsIZCNwQAxeOtGK22/E0WiR8CVOFKscRFOF6Gx3j8fHjBuySK0gpuTO3mCabnX+NS+s+ORgar+YzuGtC3Tf3QEA4zL/VPe0dxqyraeSq7fm02CWvFVdz15iUA/tabHbuzSvh38VVRPgZeXBsKlekxOFncD2RelVKHO+X1bKivJarUuPc8RZ6pK28jOApUxj++r9pKyrCnJmJeUsm5q1bqX7pJbCpO31DWBiG+Hj+ll3Ej4XFzDIHccmkAB60hpMU0MIZ8YfOs6mRgsLaRn6obyK7WYWJAg2CEIOBWqsNG1BvtVFctY7Q2n8yMWYOkyc/hZ9f1558aOhoJk38J4WFr1FQ8BwFBc9gNIYREz2bmNj5xMbMIzg4jSh/PwZjto8WCV+iqRJCB/hPFBylZhuba1U+wgt8WFHHS0VVABwdGcrFyd0kLQ1GJRTu6Enkr4WclXDK33XNpj74rtbEFdv3MjI4kPgAP94pUyNqAh0J2Wf2V/BqcRVXp8Zx28ikLnkGVzgyIoQpYcG8WlzFlSmxPY4ocgfWsnKCZ8xACEFAWhoBaWlELl4MqPWsW3bsoDkzE2NyCn/MKuKdshoCbLD7vBsxhprIqG3kzyOT8O8gio/ml/IYkZBVxPCgAK5IieW0uEiOiQzD3yDYb27l04oa3t+/k+eto0A8y6S2IH5V1MTiBH9GhXTtqSUmnkli4pm0tdVTW/s91TVrqaleR2XV1wAEB6cTGzOP+PhTiImZ47HPC7RI+BZNFRCWMPB2otO9JhLFLRb+nF3IrIgQ/ITgrpxi5keHkxrUzZDe8CTY+jbs+gSAuVYrfO/CT9Zqhsg0OPqGvm1/wfxUZ+Ky7XtJCwrkvemj2dFo5uJte/misp6zE6Opb7PyQlEFC+MieOgI95WXF0JwdWocS7MK+aG+idlRnsmTSbsda3k5/kmJ3e43BAcTctRRBM6axf9t3s1HZTXMzVzLUds3kH3aPF42ziXQILgs5eCNWpPNxvOFlUyjjWVHTWZ8aFAXkUvxb+O42r8wvm0dQcNuY1vQufyvso6H8kt5KL+USWFBLI6PYnFCFKNDgg451t8/koSEhSQkLERKSXNzPjU1a6muWU9J6QqstkYtEpoONFW5Z6RP9Ego2TLo4Sa7lNy8ez9WCU9PHIEATtiYxa1Zhbw1dVTXO8gT74Lcbw68LCsqYtgwFy5OQsC0i8A/qG/bXyib65u4ZNteUgL9WTF9NPEB/hwf48fwoABeL6nm7MRolhdV0mC1c2u6+2t9nZ0Yzb15JbxaXOUxkbDV1mKxWanyM1D34wbMDfU019fT3KAeuXbBz6GxbI9Poyw6gbk/Z3CNfyuzkn8iPjSVr6dcTpuUxAUcvGx+VllPk83O2bQwoZvyIq2tFWzdei2mpj1MGP8QKSlLmAPcODyB4hYL/6us49OKOh7OL+Ph/DJCjAb67kcdqR6GmzldBvG0Gz+j7tAi4Us0VUKqG9bLHXEcVOWocM4g8mJRJRvqTDw2Lo10RzL0jlHJ/DWnmDdLa7is81j5sSerh4PcjAyG+cBMZl8js6GZi7flER/gx4rpY0gIVAlSgxBclhLLg3tL+bm+ieWFlSyKi2SyB4aqhhgNXJQUw8vFlZS3tpEY6L6VEq0WC9k/rGf311+wf9JI7N+tgu9WYTUYKUwZSf7YKeSOPY76kHCElIxqaeTm1ipuue5qQnI+hE9rYPqlnBIX2aXtd0prSA8OYLzZ1mVfU1MumVt/TVtbLVOnLicudsEh+1ODArghTc2kLmmx8HlVPYUtli7t9MbkQah7pUXCV7DbVU8i1A3hpqOvU49BZLfJzIN7Szk1NoJLkg/mBa5KjePzynruzCnmqMhQxoXqu/3BZHtjMxdtzSPKTwlEUqeL80VJMTySX8rl2/NptNm5daTnKgZfmRrHC0WV/KekmqXpiZS2tpEljYTUmVxuU0rJmv+8QmluFsHBIUQFxxJ44aVsikvluxYbTXZJsEFwfEw4p8ZFckpsBPEBHT6DzLcgbly3N2f7zK18V2fi9pFJiIKKQ/bV1W1i67brMRj8OXLGW0RETOnVz5SgAK4dNrg3bc6iRcJXMNeCtA363b87aLXbuWn3PsKMRh4bf+hoJoMQPD1xBCdtzOK6HQV8MWtsj3V8NO5lvzTw2615hBoNrJg+utu8UEKgP6fHRfFpZR1nxEd6tGLrqJBAFkSH8/T+cp4rrMBkswPhsCV3YA1PPl49OpBohXOTYjg1NoK50eEEd1cTqSoHCn9UAx66Saa/V1aDAJYkxZBTcHB7RcWX7Nz1B4KCUpk+7VWCg4fYei39RIuEr9DkKGM10NFNXuDR/DJ2mlr495SRh96lOUgK9Oe5iSO4cGsef8ku4skJI7zgpaK2zcrPDY6yEvXNbGlsotHa37HokbA60yP+uQtVUzQc2myAjWN+6Lm4QXv90c8q60n28PuSXf6S4ESUvucG7ep4IVRZGykJ8/djfEgQ8QF+GIWg1W7vXiQy3wJhhKld16q2S8l7ZbUHBl3kOLbvL3yVnJwHiIycwbSpy/H39/11S7RI+ApNju6sO0Y3DSI/1Jl4Zn8FlybHcFo3Md125seEszQ9kccKyjkuKoyLuhsW62ZsUpLV1MLmhqYD9YZym1sBtRrXhLAgzkqIJq6fwzz37dvHiBHeE7q+MFltvFlag91u46KUWKLdMIzVU7j6WVpamtm+6iuM/v5MPfFUDH7+NK76BmtpGdYlF/FzQxPLCsoP1JUaGxLIzIhQZkaGMCsilNFBfhi2vgdjToHQRLAfWqr7+zoThS0W/jIqGQAp7eTkPMj+wpeJjz+VSRMfx2g8PEKnQ/fXoTmUAz0J3wk3NVpt/G73foYHBXDvmL7X0V6ansSq6kZeKKz0uEisrm7gtuxCilraAIjxNzIrIpQLEmOYGRnC9PAQl2f/ZuzPYoHj4jEUuSOnCIu08xCNXD5uhrfd6ZX+fpZleTlkrvyMrO/WMge45IHHiB+hRgTuu+cbpM1G+nhVVchktZHZ2Mym+iY2NTTzVXU975TVHGzsyDfV85rOS+cowo0GTo+LxGZrRcrl7C/cyLBhl3PE2DsRov+/HdPatRijowme0nv+YrDRIuErmNpFwnd6EnfmFFPcYuHjI8c6dcE1CsFJseE8XlBOk9VGqAdKNDRYbdyTW8xbpTWMDQnkyQnDOToylBFBAR6dxDVUqLS08WZJNecnxpBWXudtd9yGlJJvX32BzJX/wz8wiInHn8iRp/+K2GEHh4y3lZcRPGnygddhfkbmRoczNzr8QBv5ZgubGpoo3vSOKqk/55Ye15I/MiKUIANs234zko2MGfNnhqdd2+/fkd1iofyhh6h7+x2CJk5k5H8/cOET8BxaJHyFpkpVpsJH1mb+rLKOd8pquGVEIkdFOr8u75ERodiBzMZm5kT3vxZQb7TY7JyxOZu85lZuGp7ArelJBA1wERdf44XCSlrtkt+NSKCwfK+33XEbG979D5kr/8eM0xczZ8llXdaCllJiLSvH76STe2hBTeobFRLIKNEMW+6BmVfBqN57wPv2vUBV1TcIcREjhvd/xGBbSQlFv7+Flu3bCUhPpyUrC3tzM4aQoVERF1ToVeMLNFVCSBx0qVc/9KhobeO2rEKmhgWzNL372a09MSNC/XP83NDsdr+eK6wgp7mV16aM5I7RKb84gahts/JqcRW/6mZmry+z6dP/8uOH7zL1pIWccOX1XQQCwFZXh2xt7XG29SFsXwG2Vph+Sa9mdXWbyNv7GAkJixD0LD49YVq3nvxzz8OSn0/qU0+S8OfbwWbDvGNHv9vyJLon4Ss0VfpM0vrBvaU02+w8NXEEAf0UtRh/P0YGBwxYJGxSHlKqer+5lSf2lXNmfCSn9pJA93WklHxf10S0v5FRIYEHai4BvFxURZPNzu9HOCfc0mY7pIT2UEBKiam2mor8vVTk51Gen0veph854ti5nHTtjT2GeqzlqlCkX2If8zzqClVJ+dRZkNzzui0WSzU7dv6eoKBUJox/kPVVm51/D3Y7Vc89R9XTzxA4dizDnnyCgPR0rLVqiUhz5lZCjz7a6fY8jRYJX8Edxf0GASmlY4nKKJcnxs2MCGVdbSNSSpfyBG12yaLN2Qjg8QnDmRQWzJ25xRiE4O9OJNB9ma+rG7hiez4ARgHpQYEkBPoR6+/H2tpGFsZFdFs+AlSBu+ZNm2havx7Thg207dvPyE8+JnCk90qrtzbUkfX9eioK8qjIz6OiYC/N9Y5cihBEJ6Uw/bQzWXDFNV2WBe1IW5kqFNlrT8LWBit+DTYrnLu827kR7WRl3U1bWw2zZq7Az8/5sKi1tpaS22+nae06Is/6FUn33IMhWH0fftHRBKSnY84cWsOnnRIJIUQ8cAtgl1LeKYQYBzwLBAHftS9CJIS4D5jvaPd6KeVOd9hqUCIRPfTXQdjT1EJVm5W50a7X35kREcKK8lpKWtu6L/zXB2+VVrPdZCbcaOC0TVn8KiGalVUN3DEqmRQX2vMVpJQ8XlDO8KAA/jIqmeymFnKaW6iyWMlqaiHcaDy07pKUtGRl07R+PU0b1tO8aTPSYkEEBhI8ZQqW3DyaN23ymkisf+d1dnz4HjsAg9FI7LDhjJwxi4T00SSOHE18+kgCgpyb3Gctc/QkknrpSXxzDxT9BBe8BrGjezSrq9tEReUXjBx5C+Hhk5x+P+btOyj+/e+xVlaSdM89RF24pMtNUPD06ZjWrHH5BskTONuTeAzIBdqzKcuAa6SUBUKI94UQxwABQKKU8nghxGTgUWDRQG2llD+67d36MqZKrw1/lXY72GwI/77r6ayrbQRg3gCSzkdGqJjyzw3N/RYJk9XGo/llHBsZyitTRnJXTjErymsZGxLI9Wm+M3zYFdbWmtjS2Myj44ZxTmLPAxystbVUPPYYcV9/Q359PQCBY8cQfcklhM6ZQ8hRsxABAWQffQwtu3YNlvuHsGfDGn788D1ijpjIoquvJzZtBH5O/P56oq28DIxG/OJ66I3v+Qy+fxqOug4mndNjO1JKcnMfJiAggRHDr3Hq3FJK6t57n/L778cYH8eIt97scZhr8IwZ1H/0EW379xMwRObaOCUSUsorhBALgIVCCD8gSEpZ4Nj9AWrJ0VjgbYf9DiFEjJtstUhYmqCtyW3hppY9e2jeuImYyy9zyr70zjtp2b6DUZ983KftuloTI4MDGDaAO/ZJYUEEGgQ/NzSxOKF/y6o8W1hBVZuV18eMJMbfj6cnjuCylFhSAv37nR/xNZbtKyM50J8lST2vmWGtqWH/VVdjKSigbcoUhp17DqFz5uDfzR120IQJtO4a/GXmKwr2svL5J0kdP4mE+aeSOGqMU8dJKbHk5tKam0trTi6yzULYCScSPH2aGtkUH999jqW2AD66EZKnw2kP9HqOysqV1DdsYfz4BzEa+x6BZDebKbv379R/9BGhc+eS8ugj+EV3FfDy8nLWrFlDUEUFYwFzZqZviUQn4oHqDq+rgQlAAlDZYbsVSHSDbReEENcD1wMkJiaSkZHhwtvwPCaTyS2+BZnLORbYU1RDmRvaC//Pm4SsX8+e2hosU6f26qd/bh4xH/wXgLUff4w9suekr1XCOiKZi2XA73u4DGN1YSnHF+Uc2NbX51krBc8QwbG00fDzRjpa7nU8BgN3fe/9Ybc08j3hXEEz369d262NaGggetkT+FVWUvfbG6lJS6M+LAz27FGPToRFRBCydi0Zq1bBICWw28zN7PngPwj/AGKPmU+z2ez0Zxny5UrCHUuSSiHAYKD6xZewRUeBwYAtPKJLW8LexowtfyGkzcqm4TfSsv77HtuX0opd3gukkJ0VR072wba6+86NFRVEvrAcv5ISms48g/JFi9i79dCJeS0tLeTn51NeXo6fn5+a7Ofnx49vvEGj1Up4eDgBAe4Lkbry23RFJOrgkFXzolEX/GDH3+3YgRo32HZBSrkcWA4wa9YsuWCIlo/OyMjALb4VbYIfYfzMeYw/YuDt7Xvt3zQDCZ98yqjrr2ftDz9066e02Sh48ilaAwORra3MCAsjvJf3s7G+iZafc7hw0jgW9LMH0JkFOUX8p6SaOfOOx98geKu0mo/25JGY2HPiMaepFbvJzLJjph0oRe5prHbJs999RlNYCkckpTM2NIiyTZuYfMwxg3L+dp7NKiTW1MI9s48lpJuhvfbmZgouvAhLTQ1py19g4uzZff4+6xsaKFm1itnDhxM4dqwHvVeYGxt4//47sLWYuejeR0gaPbZf/0P7XnoZ29gxpDzyCAGjRiEtFkyrV9Pw+pOYdhQTIO1Mmjbt0Dv5L26Hxhy48D8cO2Fxr+0XFr1BdnYF06a+RFzcoevDd/azcdUqSh55FGE0krL8BcLmzTvEvrm5mXXr1rFx40YA5syZw9y5cwHI2pJJeEkp323fDkBYWBjJycmHPCIjI13KWbhyTeq3SEgpzUKIQCFEqpSyGDgXuBcYA5wPrBNCTASK3GSrcXNxv7b9+wlIT8dSUED18hdhavfx0boPPqBl1y6SH3iA0rvvxrx1G+EnndRju+tqGxHAnAEkrduZGRHKi0VV7Kpr4NsGM//ILyMaP/Lrmno+yG7njvQEjwhEWWsbPzc0MS085ECexC4lf9yRxbttwzDW2LDV7ndYR0AvBfM8xV9HJXcrEADNGzfSmpND6rJlhM6e7VR7QRMnAtCya5fHRaK5oZ4V999BTUkRZ996B0mj+3c+e2sr5m3biL70UoImOAIQgYFELl5MRMHdNITWUrIRCpZcSMo/HsYvJgZyV8E3L8K0yyF4ChQU9Ni+zd5KfuGTRAZNI7wxHYupAL+EhC6T3qTVSuUTT1D94ksETZ7MsCeW4Z96cESdxWLhxx9/ZP369VgsFqZPn86CBQuI7NBDTz3xBKqef4ErlyyhrL6e0tJSSktLyc3NRUpVQyooKIjk5GQmTJjA0R4eLuvqENilwAohRCvwiZRytxAiC1gkhFgHNAI3uMPWRf8OL0yO4n5uKMlht1hoKy0l7re/VSLx0ksY7/hbFztbQwOVjy8jeNZMIs89h9q33sK8bVuvba+rbWRyWLBb1j2e7q/ukm75PIPdqSO4ICmac8ryOXG2qusvrVZas7NpzsykZetWmjMzadu3n/CFC2HZ4wM+PygReCS/jE8r6sgzq8J/oUYDd45O4YqUWO7NK+Hd6hZuLXiFm2u+Zq+IIOfUx9lSKZkwodtIab+xVlRQuewJAkaPIuq88zBGdd9D8xeCRfE9hwJbc3aRML2ekKnOxfcBAkaORAQF0bJrF5FnndVv33ujtbmZrO/XIe1qsZ6tX31ObWkJZ992J+nT+r+wVsv27UiLhZCjZh26oyoHUbOX0GQDI06qpWhzNPsuubSDQSJ8tgoeXNVr+6YTbLRdYMPv0Qb25i4CwBgby7CnniLkSFX/ylpVRfHSP9L8009EXXghiX/7KwZHqMhms7FlyxYyMjIwmUyMGzeOk046iYSErv/TwTNmgN1OQkMDIzsIusVioaKi4oBolJWVUV1d3eV4d+P0f7OUMgNUmFdKuRGVVO643w7c2M1xA7LV4Nbifm1FxSAlAcPTiFqyBFNGBpGvvEpDRAShs2djdNzRVD79NLb6epL+9jeEEARPm0r9x5/0OMGq2WZnc30z1wwbeG9HSonfww8SNfcMdqeO4LTv1/Cn+jKKkhOp+PlnzJmZmHfsQJrNABjj4giePo2A4SNoXLkSS1ERAa4sc9qJf+SX8cS+ck6ICeeylFimhAfz1L4K/pxdxEtFleQ2t3KNNYs/lryP+N1mxr9xDuM/OpeR437P5CT3rDtc8uRjNGz+HjJ/Qvz3XRJu/xNRF1zQ71CD3/4viRzfBDtehZT7nTpGGI0EjRtHy073j3Da+PEKfvzovQOv/QODOOtPd5I+1bWCg82b1GS24LM8YdcAACAASURBVBmdjs/+AoCi9dGMOKWBUb8ZT1PEYsh4EJqq4aS7ILT3YpI2LFQE30e4LYmRN/wfANJqo+r559h/5ZUk3fd3/Kuqyb/zLmwNDSQ/9BBR55yt7KRk9+7drFq1iurqatLS0liyZAnDh/e8DHHw1KkAmFZn4Bd7qG/xQHxUFFOjomDCBAxhnlnqtSN6Mp0v0FQFgRFuWaO5rVCFRPzThuOfmEDSXXdSdM+9FN/yBzAYCJ4yhcCJE6h79z0izzvvQNc9aOpUat96m9a8PIKOOKJLuz/Vm7BIOaChr+3UrVhB4yefcvWRx9E6fhTXhwlq3/yUKJuNaj8/giZMIOr88wmePo3gadPxT01BCEFbeTm5J55E7X/eJPHPtw/Ih/fLanhiXzmXJcfy6LhhCCGw1dUxM6iNdyP9eaChlfOCjfztmydpDZgMpY1w3BPw2R8Zs+GftJRvhbl/gODeczPCYCBg1KhuhbetrIz6Tz8leskSYq66ktI77qTsrrtp/HIlyff9/ZAwRl8Etm5VmcBNr8G8W/v0q52gSROp/+RTpN2OcNPosNa9e9n+0fvENZo56ewlRF1wAYHBIfgHuf77bt60icCxY7uOHMr6AlvYSMxVrVhHn4d/7rtEjjVBVDbc+A6MO73PtvfvfwVrbuP/s3fe8XFU597/zmzv2pVWXbIs2XKvuOGGMcVUU0NLSCiBhJJCCQmkXJJAQu8JNYDpzTQ7YIMNcgPj3ptk9V53tb3MnPePkauK5dhw730vP33mM6uZM2fOtPOc85Tfw+Dxz+NKmbh/u+PkWdTecgsNv7sTtyQh5eVR8PxzmIcOBaCyspLPP/+curo60tLSuOyyyxgyZMgRBbzO5cJUXEz7vHm0z5vXZ1nnWWeS88gjR7yGY8H3QuJ/A0LNx80eEa+uAcCYr2XLcp13Hhvtdia5PYRWrSK4ciW+N98CIPDpp9T6fNimT8dYpAUXRbds2S8kPmv182aDRq1cEYlhkCQmp/SfzG8ffB98SHjdWkyFRehSPTTdcy+2qSdyxw/O1TrP4l/imTuXtUuXcuIPf4jcS2diyMjAOed0fPPn4/3Fzci2o28LwDe+ILftqmFaip2/F+d2CaBmKubORfH7mQy8L+vQqQpVALTDi+cfVEM6sBmeuqpf5zMNHUrm7+/COnHiIdvb570Cqorn6qsx5uaQ/9KL+N55h+YHHqT83Lmk33FHjwFZh0N0VGK2dBARxVjie2Ddv2DGbX03SlUg0Ih5+HA63niTRE1Nny6Z+9xPRSLRZ7Whb9aw67mnCed7GZmSRuCRx9DV1GL74x/7bk8fEMkkkQ0bcJ1/mEos3A413xBPOwvYANNugbrFULoYpv6iXwJCUSJUVT+L2z0Vd8qhz0eXkkL+c8/R/Ohj1O3YzpAnnkDndNLU1MSSJUsoLS3F4XAwd+5cxowZg+4oPMRyn3icaA8eZ4ejJ9fl443vhcT/BoSOXyBdvKYayWpFd/A0VqfDOn4c1vHjMA8bSu3Nv8B14YUgS4RWriLw+efIDgeSzUZk8xZSLr4YIQT/VVaHL6GQZTIgA9fmph116lH/xx/TcOedyFYr/rDG16T3esl+8MFDRtfGggISRUW9Coh9cF95JZ2ffIr/449xX375UbUFtLzFV2+rINds5IWRBRhkrQNufuAB1EiE7AfuR9rXhsZtsOx+OOk3kDl6fx3bt29nRI4T1jyn+eDnTYYTfgKm7rMspb2D1ueeperKH+M48wwybr8dQ04Oit+P7+23cZ55JsZcbcYgyTLuyy7DNn0GDX/8A413303nokVk3fPXPtVr6ppX0QGxvMuxmFbC6mdgyk09FFSg6ivY8SHsXADBJiyznwE043VfQsL/wYc03HXXEe6uhqaJozFICpOffRnfM8/S9uyzRDZv6XFmlNLWSs3b7/RQywEonZ2o4TDhTZupueHG/dut5nJSXSpty2pBktDnFqGc/SCJ8kWYT/mvfrW1pmYe8Xgro0b+o8f9ksFAxh2/YWdJCQFV5csPPmDz5s2YzWZOPfVUJk+ejOE/CAI0FhRgLCg46uO+DXwvJP43INQKnsLjUlWiugZjXl6Po081FqPpvvsxDR5E1l/+jKTXI4Qgtns3tb/4JYn6ekLfaLGNa/whKiJxHh+az6VZvQdv9YXw2rXU//4PWCdNIv+F51FCIeJ792LIyuqmi+0vLGPHYh45kvZXXyPl0kuPSkXSmVS4cksFqoBXRw/cn7EttHo1nf/+N2k33YRr7twDB5RsgLwYXHIdmA8YjWNGI85Zs+CiH8Oqx6HkPti5Dc5+uMdoXtd5c2l78UXann+B4BdfkvrTnyJUBTUcJvXaa7qVN+bmkP/ii/jeeZfmBx6gfO55pN92K+7LL+9+vUIgbX2XcIsR/SmTIHcCzDsXNr8BFGo8RVWrYMdHmmAINYPeAoNPg71fYmz5EgwGojt24Dyz55G3EIL2l1/GNHgQ3l//us97rFotLHn2UYonT8dosZB+y68xDR5M+7x5WlT0YZADQRLJZJ91JruMtyIRP6QOU/5ekgkj8aSHlItnIhkM7GA5zfYS7OsvJCP9TNLTz8Rq7Zl2pK1tGeUVj+D1nk5KyoQey6iqis/no6ysjBUrVgAwdepUpk+fjvV/EN33seB7IfG/AcFmbTR6HBCvqcFU2PNH0f7SSyRqa8l/+SUkvfZqSJKEeehQBrwyj/LzzidRXY1/wQJe8+RiA05pqCLcWHXU7VCDQepu/w3GvDxyn3wCyWhEbzSin9Dzx9hfSJKE58ofUf/b3xFauRL7zJn9Oi6pCn62vZLySJS3xhTtp9IW8TiNf70HQ14eqdf99NCDqr+GjJGHCIhDoDPAzNthyFnw0Y3w7lWw7X1NWBzE6CtbLHhvuomUCy6g+aGHaP2HNmq1TZ9+wJ2zh+t0X3oJ9hnTafjjn2j66z2areLeezAebBRt2oYcrMJf5SKtcCBkZ0P2eFj5GMWWobDmWgi3gsEKg0+HEedra6MNPv4F0tb3MA+Z2KfxOrxmLbE9e8i6954+XaQBdq5aRjwSYcTM2fu3uc45G9c5Z/dYvqSkhNFH8OuvuelmYqZSihYsOLAxGYcHCmHkZRTe+yQAbW0raG7+BK/3dGKxFvaWP8ze8oex24eRnn4mGeln7RcYgeAutm77JTbbEIYPe3B/tZ2dnVRWVlJVVUV9fT0tLS0ku4TYuHHjurmz/v+A74XEfyNCq78hsGQJGXf8Bqm3qEpVgXDbcaEJF6pKoqYG+0kndduXaGyk9dnncJx+OrYpU7rtN2RlkX7n72i88y72/v6PLLz/aU7a8A0trz77H7dH5/GQ9+wz+z2qjhccZ56J/uFHqLnhRqyTJuKcMwfToEF9snr+OSrxZVzmb2aV8eV72EdUHiwpIb53L7nPPH2oqktJakGOYy47coMyhsO1S+CrJ6Dk71C5Ek6+C8ZdeYgzgiE7m5xHHsF9xRW0vfwy3pt6UAkdBkN2NnkvPI9//nya7ruf8vPOx/urXx7gBlrzAqLFRKDBhbO+XmNDTT0fNt+DU2olXHAijJwBuRO1tkSBrV26cP1EqH8L1ROntLQU49IlOFO6U0q0PPkEst2OIS+PRF0d+vT0Xnm+di7/Akeal9xhI3vcf7QQqkpk/Xrss2cfuqNqJcQDmoAGVDXG7j13Y7EUMHLEY8iyiWi0nuaWxTQ3f0J5+SOUlz+C3T4Ur/cM6uvfQq+3M2bM8+j1mgfR0qVL988WTHqZHIOfCdYIXmOUlHg9Rf5N8NFLx+W6+o2CaTDz2+VB/V5I/Deh4803abznXlAUnGfMwdrbCDrcBojjYpNINjUh4vH9RuuD0fzgQ5pv9h139Hq8Y9YsGoF1199IxGzhJzMmkTfzPx/5m4cOQe89/qR7stHIgNdfwzd/PoFFi2m8u++YzA9nnsa8y6/hB0v+zYnzX+PweZH9lFO6R5o3b4d4EPK6C9QeodPDjFu1TmvhLfDJ7bDiEZj2K8g8tMO0poH19suANk2gHAESkDIhE9vTf6ThiVdpvu/+w0qkAkmqr/zxQdv2OULs6Fq6C/uwQc/ejGHUunWIdCdbn32U9M4wuR0BDEmlW/mWG7rCnSQJncuFzuNB73Gjc3vQp6YinA4qN29g7KRphNetO+RY2WTCOHAgOqfziNd7MOJ796L4fN2+n+DmN7DoTOgGagOiquoXiEQqGTvmZWRZC7Y0m7PJz7ua/LyriUYbaG5ZRHPzp1RUPIZOZ+OE8W9h7miFsreI6uy0rFrJxAEjGTdhEpkfXIxsygS7xgDgj/sh/t3PIEQixrfNFfu9kPiOIRIJmv7+dzreeBPriVMIf72ayOYtvQuJ4xhtvc+zyZB3qJAwlJZqOvcbb9xvJO0JercbQ34+H6XnUmAxMmvymP8xdMaHw5iXR/qvf433V78iXlZGsqVHhhdWqjJPJUzMlhXuPetkdGcfSreALGMd30NwV/VqbZ3fTyGxD+lD4epPoGIZlNwPi47NVfdgGIC8QoimGFATB55LW1UWIn08aT+7fv82RVFYt3YN+Xm5hIKdBIMBgsFOQoEAoa7f4VAQWZIYbmwkM+KlRpGpctpo7nc62hj4GrWlomuTEDjmvUH1cz27duq9Xgz5+fttK26fj6p/vdjrGZIdmnfdwUF0LR3l2LbNZ6HVwsS4D7fSTmXlP0n3nklq6owe6zGbsw4IjFgjQk1iMWXBvCnQugczcBlA1QdQowNJByMvAncB2NIo31XBuBMmg94IOtNBaxPojAfWx+l7SdTX0/zYY5iSaaT1reE7ZnwvJL5DKD4ftbfcQvjr1XiuvYb0W29l7+lz+o5k3i8kjl3dtC9G4mAvFaEoON55B31WVnedew9omzSF9d5sfpvp+R8rIA6GJEmYBg/ukVZiTyjKLzbsodhm5Lnxg7Hrj8Izq3o1OHMgpfusrB+NgsJZMPAkaNwKUd/R19Fb1WjhEPugJiF07vV4507GNnUqAFuWLuLLl54jmYiz8SCOZYPZgiM1DUdGBunDR+DyZjDihJE45s2AGecxdvYfUJIJar76iqaHHyJRXw96PZZxY/He2LdqTI1GUNrbMcSTpKZ1f5fVcJh4RTmx0jKt3i76iSNB7/Zg/cF4DLm5JBpDdJQ38sGuH/EzVeVdh4OGPW8yLlmCJMkMHtw/7yuzqcutdMu7qC11JE57lpdWbmGA18aZtp2wawFYU2DlgfiEcQD9yRWkO1yIHBAmQmci6ZpBzHMxsSYZNZJENumQzPqutQ5QCK9ZRWhFCUKJYsgZ2q9rOhZ8LyS+I8TKy6m54QaS9Q0HIjKVBK7xXuJ7V0DZkp4PrFylrY+DuileXQN6PSUGK09tKEUVgmRbO4lLr8M4sADdjpoj1tE8bQ6SqnLxMRL4/XdACMG7TR2UtAf2J+Rx6HS8Mrrw6AREMq65ihYcY1S1JEHW6COXOwbEtm8HJIyFRaiKQsmrL7Dx0wUMGD0OPOlMmDoNR2oadk8apt68cQpPhk1vwqy70OkNFMw8ibzxE6i/4w6CX37JgOt+jnXk8biOk7ttqSgpYUwfhuulVUuZXzqf4Useo7iskdrML5gcrCFgySVlwAScrS8QMsCY0c9gNmf3vymqAsvup0N3J6HFOYSkSoaMnQT/flSbQVz8IiQimudhuJVN36xg7MjhWm7sZFR7R5TYQesYKPFD18kYIpEg5ksl0pFPxF+AqtiBdnSmEDqXhUQCREJCjYOIgzYMGIh5nGZg1zn+8xwb/cX3QuI7QHDFCupuvQ3JaCR/3rz9XC+sfQGv7RMYDbx2Ue8VyAZwHHvQTLymmvahw7hpdy1ug56BFhNRXwc6VcGW6kHqRbsZVVRCqgqATZY4d/kSssYVgPXYI8C/K8RUld/uruWtxnayTAaG2sxMc6dxRVYqeUeb++LzP0GwEUZfesztaqurwZnqPaZo474QL9cI0nV5Obx/391UbdnICWefx8wfXcPy5Sv6x5M07kfw3tVaEN6k67T67DZyn3qSZFMThqysb6XtR8LG5o3csfwOzJioM5QwuDhGPtA20ka8zcm5yi6EIY4+53ZSU7s7a/SJbfMRrXuJitHICZhjGEvB0pvB4oYzu7ydDBZtJpmSh8/th8Gz+lW1GleI7u4gur2VyK52RFRBMsqYh3sw5wlMzW+g3/EMdB4UmChDh81OmaWYcstgak0FNBjyKLQq3MhRqjyPEt8LiW8TQtA+bx5N9z+AqbiYvH88dWjA0KbXUV2DqH7Ph/fWW3pPfm5LA/PRGfR6Qry6hgcvvBIFeHdsEQMsJir+fhf+aIyx1x8aeFYVibGwxc+CZh+bAuEDO4wWds46nfCuWn4zyswYx/98X/Cd/3ya24rHs0Fn4taCDG4vyESWJGIVFajlpaiFhf2Pzt7xMXzzNEy+AYrnHFO7OluamXf7TdhS3Mz84dUMnXbScVfhxcrLQadjw8Y1VG3ZyGnX38zoU844ukqGnw+D3oDFd2lxFtnaIEeS5f82AVHlq+IXn9+MN+nhgfof0jb5EVSjm8JaN3JkM1WFCQyJIC83u0hVdzOzO5NM71CSsOx+4u45iEaZUrmBwYksfMHLsZ42DpPVc8hwKlZegWnTJjrDYdR4HBGPI2Jd64S2VqMKStiBGveA6gF0IGKIeC0iUoEarCS+K0o0NwfH6XPwX3sNn+3aybbdlZQaHVRmDqDVdsAwbpEExUZBfsq3P1D7Xkh8SxCJBI7XXqdp1Socp51K9n33HdoRNe3Q9NGn3kvkxecIVcawXfTtUf4KIfggM5/V2QP4u7mBAYufhXMfI1FdgzJCy9NbFYmxoNnHghYfmwMaed4Yh4U/FGYxJcWOLEF09x4WvvEu8+dezJx1exhiM2P8jmwTAWHHsXZ3v8oKIKgotMaTBIediDGR4JlR+ZyfoQX+qdEolZddjtqVvtOQnY1x8CBMRYMwDRqEaVARpqKiQ59ZewV8dDPknACn/eWYr6ds3TcIVcVktfHJkw+x6bNPmH3V9f3OxNYfxPeWEx+Qx7p/f8TwGScfvYAAkGW44Fl4doYW6/Gz5b3HhvQAVU0SjzcTizUSj7ejPZ3+QYhttLQcGFELVdCxt4Enq55hiBzlp8mz6Jz6L3R6E1M8N2D58hYYdRG5Mx4jVFVLyY6nWBArobWjmTR3umbrCLdB1A+xgLYEm6C1FDoqwFetRckHGgjrTkUg2GbYwChDGuHgLNr+61kkHsUxZw6yzUZg8WJipaWkAHWHtV0yp6DPGos+axy6tGIkWYca86F2rEXtLEXEG5CMBmSDEcloRLbbCX2zhoYvSvjJ3Q/T5srBUpjKYFlwSm4WxXYrQ2xmiq0mcs1G5O/ou/teSHxLCCxZgnXVKlKvuw7vLb/uHgm75W2QdMhjL8U8ZBmRzX3TcPcXHYkkq31B1MO2RwJB/nH2xUzqbOIny69AqEmEPRt/NMZ7E6dz59rdbAlqgmGsw8ofi7I5x+tiwGG5GRLZ6bg//YAbpp3ABxOns87fR36H4wxjUJBq6r8O1qYz4UEgXnieaZvXcfJD90GXkAgsWYrq92sRwkIlVraXWFkZ4a++PoR/SJ+dhfeqi0kpljWaDQm4+CXN4HiMKN+wBnd2Lj9+8Em2lyxl5Vuv8NpdtzBy1mnMuPzHWF3HbveJlu9lq8eGXi8z80fdo7f7A1VN4ovvwXnhP9G/chG8/zMYe8UhZRQRpzW2g6jSQUzxEVV8xFQ/McVHTO3kaATD4diytfu2C7pyT4UzXkQWesZXpGFZ2mU8L/0c3Vf/xJk6iMuHFPJB01Leffm3/DhiwJxYhk50j+w+GAJQJTeN4clEpE5O1oXoWLITY7EX88TrUZo20v7qS5CMo88sxjrtSuKuPGx2F0IygjAiFD0oXZ24TkI265HMOvT6bKQBOUBPLkkCfXGSp1N8tDtTeOrjEk4aPwvb2BzMRW5k06HdtaLEUJQgRuN/xk7QX3wvJL4lRNevBBm8F07tLiBUFba+C4NOAbsXy+jR+D/8sFca7v5AFYK3Gtq5p7ye9kR3H3YAmxD8V8ndfBCdQF2ngaFNH/Lkz29mbfEYxkkSf+oSDPl9JO3ZF9dgbmrklwN6zxL3baCkpJpZoyceueBBiJWWUv7pBwD4FyzY787q/+AD9NlZpF5/HZISh0A9dNYjOmpQanagNuxBtFcjdZZiLL9Ty32aPgIueQXcx557OBYOU7N9K+PPmoss6xg1+3SKp0zj6/lvsfHTj9mzeiVFEyaTO2wkeSNG4c48CqNrF0QySXV7C002Lyf/8GfYegiEO2IdQrB7z5+or38bSTKRMv0E3DtXYZy/lpii+VG5HQ3sGmInbNW6E50iMMUUzDEVW0zFFFMxxxSW6UwsM1r4fWs7bvXwYUw/2gI84U5hpdXCLW0dTI1GATDGVUyJgzr+cCss1WJjhgEPWy0MSnyMLZEkhpG9FKBHYJItWAxuTIZsVFMRSdNgFEMhyBZ2dVaSX2enVd9JcrkfXeMeLFMUdClmJGk8jgtOQOeUkC1WEKB0BBCKHhHTvj3JrEPnMSI7jd06975QboV3PCYuIsa4mXba6ktoXhoj+ZUPkR5CdXeSMHcQTzYSj7fhcc9g3LhvN4DveyHRXwihuT3mT+mXr3Ns3VKM9gTSq+fAGX+HiT89cFzVSuis26+ysIwZTccbbxAr24t5yNEoTzVsD0b47e4a1nWGmeyy8duBWaQYDhU2gc8W0vboE6zJcxGXLRSOHcunDS2sLR7HKeu/5PxIKxanizKni3qnC4vThdXpwuJ0YnG69htXJaMRncdDsrnnuIMeIQQEGqFl10HLbo2l8ygwMRyCbUfH7GoIhyk8sxnJoEcEnkI8tRCSChnOKnSzUpAeGqx1Kl2Q6PoozCngykHkTsK/tY3WL+tIufF6UgtnHdX5e0PVlg2oSpKi8QdUjCarjVlXXsvoU+awev5bVG3ZyM4VXwKQO2wkk867mIKxJ/Rpt4hHI6z58F2CVVXEqyqpznTjSUll7Oln/Uft3LL1QVpb36ahvhhVyETTqugYZkRVZTraU4nHLWRmRTDp0xiT/XNc1mHoZduBNsY6oXY9tXsWMKt2HVconYfUr3iHE514M2raEBAC2V+FIdGJMX8C0b2ddHy6F1mVMA508XrOKhqr5/NEwEVxwAdxBYx2KDoZhp4FH94Ak38OIy6E1f+A8mWAYFYySSUS96UMJOI7G1kyahneVCAGckImxejAY43jdjfhdDqpWFpNASNRdm7DUbuWquEXMeqaC8gqdhPd00HH/FKUthgKQUCLUZFyLMgFTtRcO0mjnmhMIRlXSMRVEl2/9/2fjCkk4grJuEoiliQWCRCNhHhmgg2L0HOadCOVriAcrtVTgYNMhK3V4S7/228P3wuJ/mLHR/DuT+DKD7WXch9UVePwGTD1gBBo2UO8rg01Mw2KxmnRtTVr4NzHNE6czW+D0bGfMsAyZgwAkS2bj0pIBJIKD1Q08K/aVtwGPY8PzeeSTPf+DzQeCbNzZQmt5bto+OITmrKceDxeLr7jblLzBvDIJ0vwhls4o3I5CWMKnXXlhMNR4rHudM+SJJGRlUregAyMRXp8jWvwvXFf742LBaCzHgIN2jpx0JttsIEzG8wD4TCPKgGEoiptnQrtAQWrSWJMoZmCDAOh1lZsXi9CCIIRFV9IxRdUtHVIwR9S8Dh0TB5iIdWpvdrJmhpi/hbMI4YT274DeaAXNdpJzK/HNvYE8GSBM1drjzMbXLngyAKTRsUgAc5EgsBv7qD5/vvpeOtNJKlv0sDUSJi9lr4N+pssEga9ROTW37C3h/1DgGIgKEOjXqJ8+1be37kNpyIojgmyE4LDWyGAtVaZej0YkwpIEkaTidOuvwn5KGeozc3NLF/xKG73O7S1FZGVfRsOu5NYJE6gbTvI60lxrkdnrKWhoZjmpplkZk8nFJdJVq3BUL0cR/NaXMEyZAQuSWKj0cn7VidVxiRD26aRhp9TWlZh/+RGKsnFSxs2NJWn35BFa+wihPUkCk4J0rD7GX6y7mscQiDMCRg2V+OZKpylBaot7/I4OvEmSMmH/ANcZ/PffJPP6j9jnXsdJw5q4akzniISitDR0UF7ezvt7e37f9fU1BCLxZipjiCpKqTvfBPT7DNoSzubjx7dyLDp2WQUOHFfPJhkY5ianW3U7fbRGU6S8HXA9o5e76kAFJMEjiQ4O1AdbSjWDpJuP0lzlGqLlwrTOVybfA5RN5I2fzGyzoZOtqHXW5FlK7JiQg0ltCWSJNV7fFII9AVJ9DNopdcKJGkrsC+H3nPAeuCfgBn4Sgjxm65yfwVmogmm64UQ2yVJGtLfsr2df8KECWLdYSH+3wre+YlGoXziL2DOQZm9trwD718HP1kIA7VoTnX+Dez+w5dETp/N+EefghUPw5f3gncoXPg8vHQmDJ8L5/8T0Kb0pVNOxHH6aWTefTftL7+M7735CKVntZEAlg4bw5OnnEO73c55G7/h+mWLcUa1D8yQmUnWvffwyVvz2LtuNQZJxRaMk+F2cfrzr2E0W5jf2M5NO6t54KtH+HHio0PqT6oSEcVARDEQTmrrtpiVmrCLxogDtVv3dPxhdaWQmpNHW10NYb8PV0YmmG3olQT+pkaSifj+srJOhys9A0daOg17dpGIxxhy4gymX3ol8bfepu35Fxiyfh1lJ83CNm0ake3bMKRnMODVV/rdHpFM0vrPp4n3kQdZlRVqJ64magocMXdAKJlAJ0mYdf0fpyVUlYRQUIVAQsIoyxhkmX2CNq4qxFUFk6zDZLUhWS19suBGIlEslkO9Y4QQRCIRlGgQvSmKLBUwYeJ7VG4IUbquiYa9fqxSBwa9itNrwmgKEG/cg92+iUy5loHUYCGGAOrIokw3gNfT6tlldHBp8HrajC28Z32FcyOXMjw5BoMaZmTkv8tdQwAAIABJREFUI3Lim2jTD6TZMIxoMsHIyAK8UhsqEjICvyzztSkVX2w21VIRqqQ7uNH8VHmGCFY+tP6CYelTkWVtfyDWzsa6zxngHkl5SgUfilcYqY7Fputd9aaoCr8qPYt2tZqP5OfQTx+GxV5IY0UnnS2R/XmmjUkLqclskkMmUGE2Y3I5iEsSUVTiJIkKhaiAGBJRoSPRjzH5CGMHL47Iwag/cvIuJRzHpBjxHoWHWUlJCbO64k4kSVovhDgir87xmEk0CSFO3fePJEmfAtcKISolSXpXkqTJgBHIEEKcJEnSSOBB4CzgsaMo+60h2d5O3a234b7icpynn969QDwMuxZqvze/eaiQ2LNIW5cu1oREZz3xVe+D8BDPHah5hpz0G8g9Ad67Fl44FZToIT72kiRhHj2a0FdfU3n5FUS3bME6aRL6jO46/wqbk/tHTGBtWibD/G08+tVnjPC3w74ZiBAEV6xg7ZVXsDfDxaS0TtKWNWMuHMiAZ19FNluIKip/r2hgSFsz07fEWH3VM0w5yP1WDzi6lsORiMepfPQxolu2kPtoHxmxjHaw/mcU4ma7A4tDc/lVkglK13zNliWLaGmox1s0iIKxJ+DOzMKVkYU7MwtHqnf/SDnc6Wf9wg/YuGgh1ds2M8uehiEjHdlsxnHmGfjeeRdUlbSf/fyo2iTp9Xh/+Ys+y9TWvUF49wJgHOkZvaenDHX6aNqykdyhI3D2EH3cF4QQBNvbaK2poj3Yid5gxJOdi8Fspmn3TlzedDIGDes15uVgRCNNuJzaOyYQBFobSDTuJlVtx0gSfVJQ0FTO2m/eYHP1MPKzfFxR/DJu/zKtAgVN9dHlnd0pUqlUJ1MvxtOoTCQsWXlrwEP4DGYur/o1jngOdopwFX7CRrGeIXUno+BhHTdx8DBPDScpi59FrnsJUUc5H6U2s0eXyvTmMzHqujsMpEqNhAx6VqonU6H6aK3dTIqqBZu16HYQNzlo96eT7sviSWUIg+JumvWdVJibKTc3UW5uptzUTLs+CBJkJlykqU4+8lSyJsuN8NdjCEeR7HpkWwSdEkZRBD6hp809iYTFi5xsR4rWIonYgUWNQ9dvvYhjUGO97tf+j9GUbOLKjUlO3iKwxgS1aRK1qRK1aRA1dX+mZxScwYNZD3bbfjxxPITEfguUJEl6wCyEqOzaNB8tZ3Uq8CaAEGKbJEmeoyl7HNrYJyKbNhNevZrw6tVErrmG9Ftv2U+VDWgzBTWpqUnCrbD8YZh5m+ZPXdaVQL30czj9Hlj9NHG/NnpLHizhi2ZrroNPT9OExJ5FMGCaRvyGltc2tGIFaihEyeNPM8+ZTqKHWV5TPIFNp+O+wiyuzB6D7vzuXhLRTatY/pe/YIklSFvVjCE1jbx/vYLcFVH7ZHUTtdEEj34yH1P+AKKWLEgt6te9MgApuSNoXbCClMETD71P3wJ0egNDp85k6NSZh4yCeoPV6WLGFVcxYtapvPOXu/iivpwZmVpH6Dr3XHxvvY1kteKc08Ng4BigqjEqK/+ByzWeQOeNjBjRPXp4H5a//hK1y9qYe+WzmKz/WfY8IQS1O7ay5qP3WLd4AxAjfeAMzr/6AQzG3h0PDkZLSwkjBk8iuPYNQiueYWS0FBWJWN4MzCdeR2vUSaj0FqbLdzG6YCqO2FqImOCk32lqOdBoJlIHQdpgnGYnTjRjsSpUbvnyFlpr63hy9pPMzD2Isn3bT3h4/cOceIeXYvcB9Wq8LkhwVR2hDU2Uz0njL57L2B45MGMo7yOlyv30lmVOc3TwxFSeWB+hIOrmtQEGvDEPQwIpTGwdtH9e3GaU2O2QERKAwsLhl1Jj6564Ks2gp8iUpDqUQFIV5sSfZ3BsHfYufjVFCZNItKOo2szeaMrAbivGbhuM1ToSWT7MQ09VMTR1YC5vwLF8M47VO0FVEToZWTlg4E+kuYjlpxPPzyCWl04sP53U7DG935TjhGP6wiVJsgFFkiQtBxqB2zigeqLr9zC0fI4HWzqTQEZ/y0qSJAshDhZG1wPXA2RkZFBSUnIsl4Fl+TKcQGTiBNpffJHGFSvw//RaVJeLvOoPKCpfiCrp2TL8t4zd/CfEF39hW3OShMHJ+KgPv3MIrpZdrF3wEuM2PU97shAh+fDbbIe0zRD3MTUWIGgvxLH6n/h2LmPH8NuJmzzI+XkYzjmbp087j8+MDoZEwwygu7ppPIJzkjFcpW2sKD2wXZcMk9q2Fm/LKmpLa+g0FDEh5EeVrTT87EZqt2sau6+EgSewMV2NMubrFdSfdSbBYPCo7qHF58cpBCsWLER1f3f0HP1tZ32kntfrX2fYqAEUfNnKMoLsfupRJCTsA3NJZmSwdd4Lx7VtppQt2DIbqSudQlvHevbu6MFvswvBjd+gz8xl/jfrj/3EJ55K2uBRREp3wqjxvPnFatpLBaIrT4+kA88gCb3l0FGoLVRNSn0JO9fcilHE8EtOvvFeSZt9AoGQILagnWi7BaP+Hk41LcTFCqr1F9BmmEFbyy6se9KR1X12lzCw+ZD6F5iWsMS0gwvjPyK6QOIzVuzf55KykOy5PPDhi1wS1fJI6KMSxgB8lvI1701sp910PlJIwen/N2c4ppCq722sKBhU9iJRczq1ueeiqCoV5RVIsoTBYCAaiTImp4hz1oM1BkvHScS8CrVALWBISngCkNqexNsQZFgLuCQHfjXI2br3EcKAJBUixFokTiUgCWrjAWrjbobj50dswmtIoMoWZGk3IEBvQ9IPB2kEEiOQJI/W4/lBagijr6s7sNTWoa+vR47FAFBtNuKDB2PavRupS0AkMjJQMjJAr8PU0opt6zdIXW7araOKKDkyo/x+HO23DsfBJrG/Ikk6Da3jdu9TP0mSdAlap58PLBBCrOjavhyY07XtiGWFEL1mjjkeNonmhx+m7eV5DN28ic6FC2n4038hO+wMuPVMTJvv10ZLw85FnP8M4v4CmiyZLHWMYbTLzsidr7Po3Lc46+Mf8PXAC9kTTdLROY5kq4+OM86guPjASGnotnlM/vovfHTRJ/giQc2eYbBofPBGGx83+1jlC3Jzfjp3FmahO5IXVdQPuxdptpKypaDECJpymbdzEN4BRVz854cgkUA2aSPLtf4QF28qY6zDyqsuHXVnn03WfX9nU0rKEUfoByPwxZfU3ngjBe++cyBvwXeAI80kFFXhpa0v8dTGp8gyJiBu5c7n9OwoyiYUj31r7ZJklWGX7yUeMFD28QAON8b3hMUzz2PL8KNz5/2/hAnJKv4ob8fa0MKoIX3knuishxUPabErIy8EoLy8nFde0exNs2fPZkRdOtFd7biuzKE68U8CgW2kpEzCkzId0x6ZwPsLCSxZgojHMY8YgevCi4hO1LGz9k4KC27H3xKltmopyagZVTFiMmZgtw8lkfARCGxHdI15073jsNmHYjHngiqgqRZq9iJqyqF6L6K2DKntgCedsBpRch0kc40kcgTx7AhJjxllq4KRUdjGn4dv006UDWXoWzpRJYn6LCPbMxO0W6OkdUZJy83h1t/2nFq1J3znNglJknRCiH3D3RY0m6pJkqQcIUQdcCHwZ2AQcDGwQpKk4UCtECIiSVK/yh5LG/uD2g4/P/3zo8S+2qGlCX38JdRQJ7JOYcKIvzFv+13cIo1lwVe7eNoxmsJIDbcP+hWL11/HN84RXOPzssacib+zid+O/Nuhle850PxPt73DNtsgftZqA2xQ1KXjruwAOjDLEk8Oy+cHmb1r2ES4g9D69/Ft+hRfxXZ8UQM+4cYvn4QvDNFwBEkSzLr2ZjqSKgtbAyiiExV4pLKRHJORl0YNRP5KIw405udDZ2ev5+sJ+nRNj55saoJvUUi89/F7xGIxLjr3Isw9cBspqkKZr4zNLZvZ0rKFdY3rqAvVkRfJ5Jd5tbQmO1g/ehw/mjYXy9lHb9YSIkmHfwWxeAtttUEayvzEY0k6zRK1Th1t5jQaTPnk6DczRr+becbbGfrLSVBfT35+Qa/1SrLMD+1OftiPNujbd2LZ3P9O4P8HpMU7mOFbf0DUlvVdXjG56VQsJBbOQ2luxwLMjnXS0B6gONFCeHMEMTzKurqfo6oJUhIj8L/7NvFVb6JvlxBWGd3s4XjOvhDXkKkkwh3sWns9VGSy4sUoKl66iML3I5y5A0tqGSKURiLgRRdSCYa/gMAbqL52zO1BZEUbgAsZkhmCRKFKcoYgkaMtqiuOklBJRp0kIy6S0UwMER/myeXEdYuJsximgDwFkooeNWEhLWFmRtKCmjCjJCwYAvbjffu74VgVyoMkSXoRiHctN6DZFN6TJCkGfCyE2ClJ0m7gLEmSVgABoCs7CbceRdlvDZvQUe9O5QK3Hadep9Fz1y1DVfScsXcpCfTYimZzqTUFe/Fsilbfy2bLRjKCewhmjGPLxALsa+Kc3v41m0dlUDb3EtyXXsKe0aOZ2kXPrGsrJXXZLgKz/8yWySMOnPzft2mzgOu+wOZIw3YYG+n6F+9hzdKVqF0TvqQqkxT7tKhFSLKEMy2DlMwshmRkkpKRRc6wEaQOGMj5G8pY23kgIjrdqOe10YV4DHrau3JLGPPyYHuvzmM9Qp+uBdQlmpuP6rjesGXLFuLxOBMOyqlRWlbK442PEzAE+PC1D7ntlNsIKkGW1Sxjc8tmNjVtYlvbNiKKpve1YCE1lsqUzin87OQT8PsfJMsAe6d0EPZ48Hr6H5WqqkmamhZQUfkUkUiltlEGZ9ekMA04XD3e0lnM1M8K0altgIkIDUc4y+EkDj3jPPefcOtrebftAQR9e0x57AbGILDl2TFkHrB1VDaE2Lq7gVNTXyFTWstW5TL8Ygr6/GVYCoKkWqbhMkxGLx/KDyYZdejyZbbtuIlQqBSbtYgRIx7DZPRSVfMCpZUv0GQYQ0qylBRZxeS9iLu3fYLLYOOeE36C3dBdsMfibbS1ltAZ2IwQgvKYRInfQMgnc3J+nFEWTcWyEg+6BgnDbhk5IiFF6VqkA+uYtpaDIM279ZDzeLuWjmVa0ib1U5l0gwxxEyKxC7OAfTM+KayiLtpG26Jt+/Xf7q4lm+f69Zz2QXEKEtmCwHCJaKqesMtAyGokETeRCBtJhg0kyowktxpIhA0IZd8z1SPJDiATn6sAQ36IdDkVc8KFIWlFVs0oqpFk0kgyaSCZMKMkbLhspX0157jgmISEEGI3cDhfcjmaAfrgciqaADn8+LX9Lfttoqbr43tgSB4OXzl8dDEY7YhrFqM+/izhGj1X//Z2cp58AvO4s2H1vWTsfA8Ae9NG7K+dA1EtMMy9uwRPeytZ+Xk0S4L0fTQSu94HScYx9lIcB1NLzLgZtr4K6/5xCB+QEILlLz3JusWryU8ReDK10bvOaMJVNI6U4kmkZOfgTEtH14Px+OnqZtZ2hnhkSB6np2kROQ69jKnLJTJeU41ktaJLO3o/a31qKuh0JI+DkFAUhUWLFhEOh3G5XAwePBhFUXhmyTP4LD5Gu0azvWM71y3X2EepBUlIuOIusmJZpEZT8Sa95LvySfemM272OILBezGZsqgItDMmq5rV9TX0J0ZaEw4fUV7xD6LRKuKBfBq33sCurPF8VWSkUVUotJq4LMPDmQYjoqqMxuqttIcqSa2ZQrbNiFEGoSrojsK1tTfYpFJyTVupTlzDcGffEdeyouI1yHhOcWMPPIPIGEFT6hQaAwpD86oY5v8bsdgsqpK34MGOW1KQKq5GH7binJWHZbQXSdezmmz4pNdp3PMR2YUXY7RpNihbwc94fPu/qQzvBWRGeoah2/ANkmLib2e9xIA0jX9KCEGiMUxsTwfR0g70HVEKJ12EYZoOX80K8hd+xZQFqxEtfuQBeRhPHoOc7aFzUwmd2TWExglMuyXMu02Y6q3ojFYkmwnJa0aymogmVTrCMZrCUTokFwn7RISsw+ywYJYUhvii2ELtVPtrkCQbSJrLsCrrUWU9QtLv/63KelRJh5BAlc0gfAwtNpAqpVFdtoPygDaY8gwKYBnbSUSYEBIYLXpUWUJnsSIwgiJr7jwqGFWBURUIBdSkiUTUTSLqJh71EI+lEIulkIi4UeMHBHS6ClRCAm3RkAQiqEoEoYZB7USICLLn2yfY/D8fTCdUlRqzFXcijiPcDK9eCEhw5QdIwUZ0SgfGM25HefQzKi+9jIHvvI3J5tWiph1ZiLShtO78BnX4j0ivfIfwunfwWUxYrSZCzY007i0FoZK2/nWUzIl0tASh5WDpL0H2XCh5HbLO0aiIgU2LF7J92VLGeJqZ/ZfXkb3dk+b0htJQlPsqGjgjzcnlWT0nB0pU12DMzf2PWEclnQ59WtrRRV33gqqqKsLhMCaTiffff5+f//zn7Ny1k9W61WSbs3ll7ivUNNfw+If34g93kGMbxjDPMHIG5pCeno7X68Xtdu+PS4jGGlm1aiUFBTewrmol2ZmbaRGL2LnzJJxOJ0IImpub9yezj0QigEqat4ycnE2YzQGCAQ/VVSezQZrEV+NH4LfYSA/4Oal6DwVtTdSrgheFRLGSxRTzFPILz0B3ghFVqOzpqKK+tYGU45C3e0Db6yRiVsrzp6DIfUenqxK0eXWM2XQXjs7NSEAmYMWBhQAt+kuIJX9Mk9xGR/5CJlxwKVVLVHKaof3t3eg+q8QxMxfbhAykg6L1hSqIfOJD/80QmqWt6Nxmklk6fmX+C42xBH8Yfz0BYSb4yhvMWahRY4QfPZddFhuyxQGyDfQ2ZKMDnceDZDYSWFSFGmpE7awHVcU0YgKW2eOJ7d2Ab+Vi/FckSJ4DOsWCKgYSO6mK8PQQsixIcY1CDQykdn2civU7URJRQIerYAI686kQkdGLCGpMISHJpOY6qIqGqetsIrW5FDlWRVmaQMgGkExIkhEkkCQZWadDb7Rj1DtxusxMGzYbsSNEaXAT63VVDJgwjc7wbgLFnRiLVGxhB5Ji0r4hVSAEJGN2YmE3sbCHeMRDPNL1O+RGSRw6s5LMHci2FnSZW7E6/VhdnTgcNpy2gYiolUSyHdVQimrYDvomJAkkjBjlgRh1gzDpBuFJ/ZbDrfleSJBsbaU+1UueGtdyOkTa4aqFmkvo0r+CpMM050YGTrue8rPOpuWpf5A7YyZsmw+DTmWX40w++SQJFVVcmGfDGfmar4onwIuaHnnX/NfIsfi5rKCez/bY2fnFLb20ZBj8VZtJdDg9rB89lV/kdHDKJdcgHYWAUITgV7uqscoyDxTn9SoE4jU1mAoHHtW9Ohj69PTjMpPYvn07BoOBq666ipdeeol33nmHjaGNdLo7uXPCnegkmfy6L/kdiwm4VURelGRGiKT0NdFYgMrKIGV7gySTQRQliF6fAqhkZlzI0IYqNju3MTxvB++//zKJxIGcbTabDa83TmFROVbLVmRdG+HODMq2n0ajaQJfFuezy2QhW01wdaKDkdEA0QYZyZiLzm0iYkqwrbGccL6BH1zwA0KhEB9++CG1tV02qOCRrz0qR9nm2UZUF+22LycZ5aXoVt62ZvIv9Xm6MTb2gMtKG5gTrOVpdxE7nenMjLYzNNLBy5Y8hjVOJ03fwV8KHibFI/hoq0q7vh3PSA+DM3OYWjmc3I9i1H2ynTX5u1ifW0pSVjh/21SGtuSzLncPIWMUb8jF8O0DmJY5ilN/+GfGeMcQKyujYvGTGIadgD5jLMmmVkQsAEoI5AgoQdRwA4n6Dkgm0Wdmo/Nkok8fgyH/RGR7upaXZ2w67WPWI2I2UtRLKHSdS+WHn5HmttOibKHTvpu2nK+QXCXYx8DIdBeGhoFYAkXU+y206SsYLu0hMzUfyWRHCJW41EJqRQmuhtUgyyQyskhzeTFaMzCa09Gb3UiSTFjpxB9vxR+vwp9opTMY5fMNJmRTnBAxsoboySkuxZWxEUmWqP3yUgz+U4jHI0SUIAo6kJ1I0gEtgRAqiE6E4kdVdyNUH6rqQ6h+UDuROlWkrr8woMVqt0K3LOsZGOwerOkhrOlhrOk1mFP3gA4qNheRN2TxkV+OY8D3QqKxkfq0DKaEdkDrHvjhu/v58tm1UKPbsKVisIH7x1fS9vQzJOZchQEQg05l3Suf4c7O5aQfXYOj/H1Stz/NzKgPz58eYevWrYwaNYq0zY+h1u1h6E8fYoje0nNDvn4KqlaBeyDPF01lY/oU/jh0KPnDJzISaIol+Ft5Ax83+1D7YNRUBSSE4OnhAw6oug6DUFUSNTXYTzrKRCwHQZ+eTqK6utf9QigoSphkMqAtShAlqXXmSSVIMhlAp3Oyd+8WiotHk5HhZc5p2ZTvfpkJaQ2MxMyE8G7KP7qXOlMj8WLtWnTJHejrdqAzuNDb89AbUzCa0tHrHYRVA/6mdwDYuesOMnVhFkdkRlkSTDnxfThIpy8BqkggSRDtyKN1+2U4809lz6kO3vL7set03DMwk6uy09ABzU9tRLVmkHHbCcgmPU2hJop2VbHo34t45plnCAaDGAwGLrjgAlpaWpgxo+dcyvuwp2MPt664lY5YB0Wu7jEqV1ZtIynJfJE/BLc46DPVhpMH6MO6/h8S9nF1Ux2r3ZksLywGSWI+2qw0NeLkoqpCPkj/HGHtIKkvxhf1EVSDyDGZtQ4fa0duZ2BnFifXjufkvWM5sXI4HaYAWeFUPsgv4RPTUpBA9uj5ZfhH/KBjDrnOkYhkkvrf3QkGC4aBl2MuzsU82I252I0+20ZnSxMNpbuoL91Fw55d+OprMTqc5DuGkaEfQL2xjJi8FV3Ch2PEUnRxO/lrf48h5iFEJ16mQBukM5J0H4haQcxeS8i7kWD6RqLjNxFg0/7gzwTQLb/i5IP/qQQqCbO2x+fSWxApaI6+ImGkauktxHyD0JtrEeYYsqrHLMvodc0Y9AH0ugAGfRC9LoQk9fSt6oD+ky2qQKvZSb3VQ73VQx2p1EfcNFjdTNJVcX6/a/rP8H9eSITqG2j2pFFY/Qmc88gBXqbWUo2IbsIBeuXUq66i49XXaFpUTe4PH6RWzae5ci+nXX8zRSdMgkE5iG1PU1yUwHXCJGoCYYpGj4bPV8LwuRRO7qNTHvoIrHgEmrfTZJDJiLcjbKmcu3Evl2d5eLuxnYQq+EGmG7eh78dWZDVxfh/pRZNNTYh4HGP+f5CfuQv6dC+Rg12PN70JW94iKSls99TSagn0q55Ro0FOvMfyJb9H0aukZMq4FD06Q5DtLf8EhyBVP5jc4b9j6zbBrHG5Gk/Ptvko+mY2TLqdf+eey2edSdTQZv7Mm8jus4iGNxAb3cg5XedRFYloRx7RtgKEeiBqN9oxCJNDZdvEBj40Bwj5FK7MTuOOgVmkGrX7HPy6nkR9CNulbjCofFrxKXetvItJ6RP4w0n/j733Drejug723zXl9HPvub1JqDeqANM77oAbBowLJHYc23GM4882/vlzHJxfvsTxZ8dxHJuE4Bog4BqMMcYOTYDBdBAIgSqSblG5/fQpe+/vjzmSrsSVdIUqcN7n2c+cM7NnZp01c/baZe21T6Zw/3+QscdJxfOoe79Ji4HB3UR+DwyUQuGrQE/MkNzSj2g3SiqGrYRUOIAyTXzn+XEMBm0ZlGMIbY1yDMrRKNsQOhpla8QZZizbSjx9Lp8K1+E1rodaAdUy8G4MmmMX/pa3zL2c7q73MDLyEGvWrmDWzJ1bk2NUC1vw1uWJDZYZ7nFoff5B/qQq2K5L4FUpxf+L0dbF9P/yFtKMUZ22FH3SMYzmbsdKupjNUN1QoFgYoeoV8Y2HWNDWnWBmp0PcbSTmDAKD9BhFqbKe6uxomdXpq96NveUlCr330ju7kdGuEiIWtsTINCRp78mSzbmkK0eQKHfjbdnEptF1jEsj7ZXVmJnDqMYybtBEamAx3pZRXhjbDAZ0uNXhw2A5BjuuiOc84g0Gy00hkkUFTahKE1q52HYRy5QJsxrH8rDFMLTsnXjj02k99uc0zLqHWHzy0Dl7QmMxTAsb6WYT3WysJYVNljwN5CmTop/pDNCDL9snSDaZYXroZRGPceKBjRIO1I0EG4aG0T0dzKwOwKKvbD/wwu3RduGF23bZjY00X3klQ//2b1Q/eRVP/PpnJBsaWXRWZFhMtoviphSZrg3R2rYAq/4nms9w3B6Wuky3wtsi99kXHlnOyZkkfz9vGh9e9hI/7B/ira0N/O2cHmalpjabdncU175EIZNhencHvHgHM9bdDkse2f1JdgxSLdFa251HY7W38mhbnruf/A7Th9dy4cM/YktzM6vmWvhxQ2IkzTBtDDgdbHC6WBObTr/TRYUUFVI0BGVS5Ol2e1nkPk+Iw+OcylJrMYEdJ24qHEMvi5OtfPDY02jNJBFZQql5Lg+c/U/8fs5nuGs4z7CVwhkY4yz6uSx1J34lzl+OfoDLB0/joqfvYjy3nk2taziyqwurdS2Z5g2kwmaCYIQKhuLsh9nidjDdivOF8BpavAFiKzz+uEKIglVARiVJnmGhBgqEqxO81F/mS7EGTl7/30wPf8ZY1sZkOwgrWTaW87Wzdk/Odphm2nDHEtSm+GIw5BsDBruGGWptIrQttnmA73HoyAEMTvUpMluOJz18JCYRgqXR4jM4/cekVjexsXIzfX3bQ0uvfWkXl8vUEtA+Sbf3ENGyqGWAdwI887I8yVqaiAd4k/m0KpvYcAcD7Q+ipgWo1BCd6y9kYe+FgMZYIaYUYjYHGCvAWCGWFZCwGpge66Sp/SmKswbRbhRIMnCHGZ9xD8yA6ZVGykPzUdUGwmqG0MsQVpoIK03kX2pC+ZO1HTRMEqNMWxXWdl3HktEXMKMxLDGkY4YmzzBnk2HORkPnSHSmAcZTWfqbu+lr6aKvpYv+5i56W7oZaO4gmLAmScor0zO8kVgY0J+exfJUA3G/yszBPk4YWs6MwX5mDPZyxFA/GW97sMyGU8+DcycRfz/yujcSL9WW5lzfl+Hn3/wmjuuv6oiYAAAgAElEQVRiOy5nlW9EnB4e/9mvsGv7EukMR11yMdaNN7L2299i7Ug/p158OY7tYJTC7+tjdGWCbNcI5oXbQTdhlv4EMh0w4yzYRcC+iZSVZn3F59L2HO2OxX8fO4t1FZ8F6WjQa2LQv/FQsbJUZUXZY2W5ysqyx8pSFQN8bkYH7+9setmEvJGREW558EZOeHeJ2H2XgfGZBVErfIqElsNPmtv5/iU2056+lp8MbOa5tgQb5gtaLP41/DOeb9m+tKcdbMTx1+MUn8YJ1uP46yjpIqFKkE6dxKZ5HyQbb+bCBHwipmnLdLC8YHPPozFubLb4/uMrOLkxTWDSLP/DMjxtaHRszu/o4gKeZHrfdxize1EVoWOTxdutB/lhx1u45U2zaAnGaPHHSfpVmsMtnBh/mKw1DvFuNBYlJ0lM+ywsraU5HN9FWVyAEMQWqi0Bp9iwaGU/vW2t/LRL0ZoRwEcpcDc14BRAwloKJDq3lghBAjD9FutWedhFn7DFUDlFUz5VoVpBqg6xF4T4ZkFqdQ3xBKsKUgGr5gYafY/2a4G+0zRjJ47h9dzDpPEFFbBWyD/qUF2Vxg8Fz9H4rsKLKaxmRbJNk23TZFsNyQYwGyG7XHAHhGC6obzIYLpqTkL7E1vhtQ8AYAUpep79C9LDx5DvfIQwPlpraTlRMhM+a5dEfhbZLW/ASEi5+QXKzcsp5VYw6sfZsuZ8Kv3HIma7wGEswEv5VJOKUrOikBqjkLQYSzmMJmMMJ+MU7Qxu4JDyDKnAZx4rOMosY2b6aToSBWC7i3HPUIHWkZDNne2sP3k6DyVmszwxn+eSc8nb2+cxOEbR7ZeZVq1w6tAWZpRDjigbZhSE1koMJ2xGO1W8dD9+5gn85GZUcgw1dwx95CgqlkdhMabTGO2gQ5eKX2Xmfn4UO7PfZlwfKvZ1xvU3/+U6vnHcqfzFf36NedOnoYKAeDjOZZlbebR4JE+Oz0YFASoMUEFAqjHHyV2zWPuHJfQ1ZznvhQ3EwwmFvxjmXrQFv2DT/3Az8961iZFVabY8MzVvlxdmzOGTX/x7/u66b3LW0h1/12CumZ+98QJe6jmC9V09DOW2T7pLeFWO2DTAjI19bGpt57m5C5nbu44P3Xkr6Uo0l2A0l+PZY+ZzbvxuWlXA44kEL7g5hu1urLRLIAGeeFR1lYqqEJitDniGmXaZk+IjHBsfpQFFbCTNcG8zLW1ZTMtayBTJO7O4Jvw8Ep/GJ6a1cUJDiqMySTLOy0usNWvWcONNN3L5+y5n0aJFLztefKif0dvXUJ6T5dbQ41dzk1TskHdN7+TNjT495f9h86ZfUC6vxZIUieAkmlaMYHpBJY/nqVmn8+TG9QSLpuMvvYeSZXjqyIWUU1Hd1tVw1ZocPaUKo7KSIWeEIXeUIWeUIXeMIWcET3xylU4K8VGSnsfpLxoWz2yh/egtGDvEESgri+XrLRY/Z2hMG/IzLSoNBmyN2FF8R7ENliXYVhQmQ2yzbUxBFx2sTIgxYCox8H0kG+UDUMNx9FgDYu0058BoTBBiwgAThogfktysaXgxIDYgSO3RGbGw2uazdF6cjTM6CSse9kv9WDFItlbJtBRJdVRIt5dxanMUtBICz8Eg2HHBsTxEomVBti0ToSFebSLhNyNeGoaGkE1bCMjgO834sU7KbjMeWaokGXfS5O0kZTtOUGykUliPUf1YzhHYsWOx7ABL5Un7g8zKzsR10mwefIzRwlocpREMW4dmJBQsEghJlNtEOWnjp2xapI121YxVTqAqCqOg6grPzIrhNTkMpS0GGmyKrpBSkAoNqdCQVpAMDWllSIWQUtH+lIJ0aOhNWdzb4TCYeLllbAwKPPrY5cS1T1L7OxxT2OTtdnzaEdWOG7airCZ8qwnfaqBqpVBOQGh5BJZHaHk4Koat2lCqh8BKEdiCZ1u1LVRcn0qsQjVeIIjlKdgh0y/6EGfZNnOmEA7+lcy4ft0biU9/+zpuXXQ8373nu7zr/94Y7Xz0erjzavjLx6Fte1iNLevW8rt//xcG10VN7bmd0zl9QTTjWFcqjN70X1iZDN3vaCNTvptNwbF0us8y2nQVoTu1lcVubWznmu653LH6KY4Itnu9BAhXzDyGlfEU870yc7Ymv8Icr0y3X8HWJWw9hpIUdzbN5xvtM9gS27eF0o8yz3IZNzOXVWgslnMUIQ7H8gwGwWAR4PLfXMrdvI1Tx0Y4Z3QjcbN7d5x8Pk+1WuXqq6+mb/mzDKx5BDdbwkqM4qSLlPpW4CV7MXZIprSYpuffhnZLjPU8QKl1KVia+OhcUn1nEd94MqgESEDWtrGMDRjWjzzJM94fabGnoXTIkOrnqPPOodRwA/rFP+Ok4tHRGOIEG2+MYVQZ+r2QjYGmgk0iKHDksuvJjUfPPWi2yb8nhTNikf5dEaeiCDJZ8hcuxD9qEMHGLrfglJtxyi3ESy3EVQuOk8S2baxymYqzgvGZj1DuWId2K2AbMEKy0EF8cCax8R5URlFpXkEltwZjvXyNjx0Q87IuqTHTyOPDF2M399Nl9dFNP42Mv/zcSXpWwqpNUHaIp0KeSRzPL3kfedNIRSdA27SOrScTLOcLL53IDc2PsDodopxWbOlErFaUm8GPxygkHKp7EwTSGHIlTVte0TquaC5qUp4mXTWkPE3KMySD3ZdZ+aSwrt1lXYdDd5vhMtI0BwG2laKiDf4rKPM0hhVasX6oSgConhRBxuG44duYW/gD1zV8mjGaSegCll3Gcj1s18dyFZarEBeMa1N2kpSdBGU3SclJUXDSlNwUZSeB58ZRr3B+zQ3JJFfE9rykbt1IvAIu+P6NbMi18fv0OnreXgsf/Z/vgMJm+NRjL8uvwpDHb/sFy+6/m4u/+Lc0d0fRMPs/+znyd93FrF/8nERXA3zrKDAK2hbBJ/84pdXsAK5Z1c8NA0M8MmMmHTMatrmw/uPajXx7/Wa+nxviIm9VtLJdfgDGa9vCQBSpFjBioz50O7c80c9D/RvJZLLEEwkGU+u4pO9OQppIzPsGxoDXmyfMe2CDlpBAhwTag+QqstN+Syq3hsBrxAxcxKbykfS2RS9xjEGmVW8jlrJZwyUEOkuT8mlUUa0YJRgtUX+7Foxm+2cjoGHG7Ok0mGHWb7qGprnbQ4PoSiOZ8jTyVReTzeNk1iFWzeh4WaT3DOzeM4l5zaQTISnbwg1drMAFI6jBF3hy7H7WuXla4jNpyV2Mp4WYVNhUfJJFmZnMTM2gb8WPiK9+nHioKTTMZkv7CWxpPQ4v0YTokObRF2geeZHe6efjxZtojvXSpNaR61tNauN6CrlZjHYdhX/CBtLzHsFOjhOWmzAqhpMaRuxw228y2kaVmlGFVlS1gXjXMo4YHWLO2gqu1oQ4hDqBMglCHSckjsHGS4SMtwV4ksQPG/BUA9Uwhxfm8FQjRjugHLBCYh2riLeuwcoOsUS9mfJj72Vev+G5GTF+dUoaRJhp1nIyD3MSj5GrDCMjEIwl8Att+JXZ+P6R+OUZaD+JV32Ex089gfsWdjOvvI75o5sYLc+nIglWdycopF5es3ZCQ6aqyVY0maqJthW9bV/KM5jS7wj1Zo5ZvI6R3kb6BpuZlcmTMpfje01MXPPKkhJGKvjGxzcBmiqOVSITGyOVHCWRLBA6FiYO2fgYywdaGR016GMMzrhPeSjDM0efzIp5xxDEEyjXxtgWGBBjoi0maiZt+wwq5uKnkwTpJNp1MJZM+T98KPheMslH60ZicvbFSCivyuLfLKGjsJm7LjwNaZsfLan5jblw5mfgjddM6Tr53/2e/s98hrbP/BWtn6gZmp98MHKhfeNX4KzP7v4CE7jk6dX0DhS58vYRpi1s4sxL57EyDRc/vZrL6eVb938oymjHMQ3dhIkORssJBscMgyM+45UqZ7e/iGMZPtpxNC9mh2hSzbSEnVxYeoiP5Mcpxs7BkRe5tXQhA6u2T+xz4nGaj4jTfuQ6nM7NSNUl80gzmd9VoeBhZzrw25voueJScpdeysrFx9N05RV0XH31tmsYY/if7z/P6ienPofCSY4ST1sEYzZWqUJzspHpTgn9yL+DsVix+IMwexOgaQqOpMUYMp6QVQ044tTuqxGxMDqk0vdH7pFltCYWUoydQlU7xE2BUDKYWmd6utRH2+BS/FiGoZbj8OM5RIdkqhtokAFSyTxVB4pehbF8hUrrG2h/w1Os17NZVj2NvoY0MSrMyj7KdL2KvLTRZ2YzZHcQiIUlgohGTIBFgEgYGTpbI6KI+xrH6MggmHh0fGuV3hhQGhUL0U7NONpgdlHJtEKhdUsSP64oxkNK5TbOfDpL+3hIkjVUmYvV+DwDx6zm4aZTeT61vXvP1QGZsELar5LyfVKeT9ILSFV8+ppyrOiczmXLfs/bn9hCLAYFu5sRMxNsQzULg402abtEj97ITH8tiXKRvHIZUQnyJKhIA4FkMDqDqAyibZL+IDl3Ay32KMVqKyPeEZQTcwnFJck4HeW7SSa2kM+Oszbn4EkSTRxjoqFwY8UJrTi+G6fiJigkswy2tDDU3MJQUwvDTU2MNTRidrPg0muNaxMJPhnfs1NL3UjsJct/dwdvsdu5eP3v+NePfAksG57+L7jtk/Dn90HPCZOep5Wid/lz2K5LwgjDH/kose5uuv/zJvpXjlEuhFibnmDaii9RvfhW2o6av8eZzcsfvI+n7vw1XznzUmZv6OXipeMYmY4vcV6YFccWi4uH7kaUpk/NplSqEvoVzIR+ULEcHDeGlayy0Opjs23zXCyDF1gk7IA3eUVCkyMm0bSdvngT2s5gjYUkYyUSCx7BmbsafBf/iYUETx+JiTURxmOERmOVyjhhgK1CgqxNocUlZlVobcyTtdPkbZdHmEdb/zRWTDPE2cK0sQFaCsOkdJGcDJNzhmhwRmlwRyl6WZa2nU7R6UJWNGN7GuJZxlI9aGPoGHyC1tHnEK2pHNNGomsm2bCZVJAjFTZi1/wuPG+UykgflcJG7JbptDTPRxCqGmIiONbLl0h9pMXmZ0fEGEgKgSWEAqEFgUBoCYFAYIGyJn9uM4qK0BIGkoKpPduYMnRVNMlX5hU5CSYaezA6MhzbHnR0v4RnmNMbMLc3JB5sOwPfiVZzO0WP0CI+j6eaWZZppCETkExr1jcnWN6WoRizqTg2nmtRdSx8x8J3bHxbCGwLQejKB0gomFpflo3BNpp0SZEqhCQrikLGZazRpZKyCW0LLaAtQdmRbrQVBbnTlhA6QiXuELjbC3DRBkeZHfbV2Tv+OZHgfx0gI/G69m5a8uB9hG/6EEc64/zme58mloGTRh4kk8yytvhrWHn79swDGueBDA1/ci5LbryNDc9FLn9HDI0z00uw8oiLKH3xAYJwq0obEP4V891+GtpGWHByB/NP7iTXsWOslaBa5Z4fXcfzS+5Gt82hlEzRVWlFmRZCKwoDM3+9TzwIeEkWoy3QIuCAYwu11nHNjVIIxRAGhmVmPnFjOCLy5ESAlbV7agOiQecFO16k5djfkpnzR4y2GX3hjQy/8GZUkMbsHNYpIYgIohWWMYgnxGLDtI4+zyzrRZSlaLRGsWPPcummcRy3iOOWsVs8xBaMSaFpR5s5hKqZ1kQ/b9Z301eYSdy8DWKgTS+FZ77PupYz2dhzNnLEcczIFpmhcjiFaHyl7I6xKdHPiPEY8i3KppEwNR+SCyMx85o5cYuk9hhxtnD/dMP6xkaMGSG0DC+0z2I4laXFL3FMcRNuqLFCgx0aHK1xJcR1FI6tsCyDJZpYH9h56PILzC8MkrYCCtkEg41ZVjR1UiaGpYWKG0MhiAHbRN0YYgxiwNJ669QFisk4+XSSfDJB4NpoyyK0LJRtoS0hsG3KiRSlVIJiMr7NEIkxGASFhUJ4/DRwjMZCU4k7lBIuyt5/he2qqc/5esUYKzLUdaLnmww08UDjhgpXGVylcZXGqX2euA1VhQXHzWbhAWw1vW6NxKbVK1lZigaGqxWPFUvW44ri/PkbeHask0e+/UdSbQGp1oDOciszN30YK97Ec1/+Ib1hL6d/4J00tM1lxfeW89i8I5FKwPzEg0xLr8c4RRqcleTMKBu8k1iafzuP3zGXx+9Yh5MskmkeJdc8juuG9L3wIqMbN9I+93SWJhYD0Dbm8qtT4qyYJhy14mmOW/scF1v3cVKujzsWzGU4s5CcUyHBGI41wku6zJKizXMVGwvDqWnFRW6SE5cGdOleCimboXSapoEQf63F4EsZLFujGqD3She3zWN4fRPjS7NIcTU2q1Exl2omRSWdRDtxtBPDppWe8U46go3M1f/DnORa3JjCGJuyPoeKOoNZZhphrBsfl21tnFqws4kY5SHEQX2EJlmFN38t1aSFn7CIH/8ejir2UE1lWJdyGDApdHIDvtVHtWGAMFvG2ILGwmChsVDaIla0GJUc/U6WjV4f48YQSAIjLsrtwU8eh7HSdJs+3l95kO4wz5bmJsZSacYTGcbiKUbcdDSYaKWo2Ek8K0UgSUIngXb2fY5KnVcxxuCEIW4Y4gYBbhgS931ivk8sCIgFQXS8dmxivt3um3DMVmoKq5FsZ0tsnH877VsH7CfD69hINHZ2kZsTeS6V1gzT0g5XvP8ynP95GPe4S+kctNny0hq6Xmxg1vSL0aqADsp0OHM4+o0P4/f/ij/e+2G82EIWubdzWtMvqJjjCMIzUOF8tGpnk+ql1f0tl+a+QkUneb70XlZWz2Gsfzrj/dNoskNmu/PomlfFiWme74oGnnoW3MqZzU/RUnRxWjJYiSZ6NhdYc28rc39aZi5PYxJpSj3C0IyApm54b3vIFS0hboMGy9A2WCSTKEMZsmVFtpyP1h5dAO0LosBCG7oThF3tNKzJMac/RqHZYTyXoahTBGTQbge65Si8tnmoWBynsJH+ljUUQ0XJP4Z1Tg9xZwZW8a2IbqWYGGcsO8J4ehXlRBkvpggcRWArQlsT2prAVgQ2KMuiQjt5M5+8dQZl9034NXe/ggtjNvhGEKWxlEb0DEQbjFXr3hGpNZ6irRBgmSqGIlgByk2hnTTaTqCdJGCwgxJx3+OZzOk8be0+bEadVz+Jqkfc94h7HgnPI12pkC6XSXkhiKBF0FbUctNiYazt++KBZvZggaM3V1gw6JEIaq1nQBmFNiFKB3i6iq+rhNonNAHKBCgdRlsTRvt0iN5plUkDtS686P01gBHB1lG1Z3doBGUJoQjx2IEvwl+3RiKZyaIbEtg6JNZX5Jh3HU36pd9CvIFjzjqTo6pF8reNQKtLxbuFDU0tjI62U0jnGH30qwQmSZPdy4UtX6O/NY6qxMl5f0Drh8ACL5alFGvHOC5DZjou4xyV/k+O4QdUTQNF3YavkxhM5CMeCxjOfZBceCKXjt+CsyUV9QlZIdoOoMWQflcV8RMEbkgpWYV4hrjTSMVpZIRW1gRNjI3k0K5LW3IUf6HDaCxBxUng2TFKbpphaWFMNzOYa2FTdxslJwtTDiQ5ja1rBr8aCe044b55BNfZT4gxOEGIcqKutq1YSuGECtcPSQcVEr6HEyqsUGEHGjtQuGGIEwbEgqj2nfA9GgvjNBRLpEplGopF0uUyrtbYKkqO0ri+IhaEWMZgMBija4W1xhizbRvNfd8+BjRoNMoEhCYg1AGVOGzsTDLcaqOdCsoK0KLRYralmgMfhmiio6H2XUz0XUytq3jCvYzBJsTWhlgoxAMh7jskfIu4bxH3hbgHMR8cz8LWQkt5zx5N+8rr1kjkQ8XD2Q4aK6MsPKIfN/EM4bJxhptirF71cZSJES6OE5oYiii4nBhDrJpm0/BCNo228KGx3/G7ziP54jGf48o/3sY7/nAXVeIErhslx4aURJMzk9MhBZISTFIgCSYp2DFNWlXIhCWWNS+kU43zjaarGYh3ktFlGnSeIO6womMua1pnMphtoRqLb/PSqVPnZSgdlUq1lpZd8UmVy+TKY2RKhSiVi8R8H1spLKWwdbS1QoWtNY4Ko+/G4JoARxShsQnFIbBdSukMxUyWaiJJ4/gQ7cMbaRneAn6IUoIODZbW0bWNRnTUInSUIlmtEPM8BCi5Fr2NGfobchDaiIrhaIu3u8/zifRD3OPP5w5/Ib5Y0fyEqChH0NgoHBS2VcUyBZyKYAUWRccwagu+axEmQ8KYIrQVoR1QsUxtYL227IMYlF2LjVWba2KMkKraTB9Mkq12kM/2kM+kKaSTVBIuvqOxjdQMUBlHVXGUh61NpDttsLQCdOR4QPTZiEKLwohG1z4rS6Fq29AK8e0YxolRjLlMnPiyK/eiluKBW5Z3K69b76b7n3iG9+cVPdVeTu18kgE1E7fqsindRtFN49sxfMel6sYoO3EqVgJfYmgfxFeIMpiEjU67qF1EW63z2sMJQ8RoLK1IlUs0FMZpLIyRLhdIVsokq2UsHXUXGJFtCaKuDGrf416VdKVIqlzCDYPaNXVtkDsqYBPVCqlKiZRX2jbgLbUpjCIS+fRLzbcfQ9Kr0OQXaJRSVIya/TkYLPjaxlM2vrYxCKFlM9jaRT6bI59ppJpK4+qAmPKJhVFylI+lAkQHoAIc5dHlb2GW2UJnrMSoHaNfpenTKYq4CNBoV/mIvYYNtsv/zTVHcxQmYiDpQ3MhQcd4ltDJ8vT8NvKZGeB0kNaKtPFJ7DQDGqMRo8AoLO1jGR/RAZb2I1dl7SPGx9IByk7iJXoI3Z1G7mstjUgl+7eiJsbQGBbp9rbQ420mqTxCsQksByU2gTiRkRab0HIIxGG9pPn7j35nyvd4zXg3icj/Ac4mku9jxpi9W19zNzyvFNf5PvdNa2dzLMFGey5PcN7UL5Dec5Y6hz8zxns5ddPTLBxZQ9YvkgnKZIISGb+0/XNQJj0hJUIPawrB+/Yr8VqaCruKcX2g0UC+lvaEzcuj/jmTL6g0T/l8f3jT7q+3dUG3vt3melVRFZcRaaBi4ti1FlO0rbWeTLS10Dxjzd/zBfeRw85IiMhZQIcx5hwRORr4BrD3q9jvgmFj+K7vQ7Je2gO4yqezNEgmKJMKK6SDMqmgQjqsRNvafkeF+JaL78QYj2XZmGlnY7qDkpvE0QrbKGIqIOsXyfpFUmEFV4c4WuHqAEcrHB3W9oXYWpEIPBqL46RLRaxKiKccyipO3koz5OYYjjcznshSSSTw43FEIKYCXB0Q1z5x5RNTUY0xoTzi2scOQvA02jMEnoXnOZQDF42FxAU7BolqhWQtntVGYCNpIuvfdigfxT4iFEgybtLkSaHN/q3lWqLJUCVDhbRU9soDZ6pEjrzb52S8EhQWJZOgRAKPGK+2jpICSfKkmULYXwBOSix92frR+5vDzkgAbwFuATDGLBOR5j3k3ytOtO2dw/W8Kon5HrEgcr9zfR/HD3D8ANsPscNa4WyiwliUBmWIhT6tlRE6K4M0VcaQoqJasShIhgoxqttSnFGJUaWBKq1UiUWFjjKIMtFECwAGMbYQxCz8uE3FFsYNYNJR2hsEsCTqWjCAX0tlwbgWxCzYqdtBtCEWhrgqxFFBtNUKW1s4xiamhVQA8SrETNRfbeIWoSlQtPrIp1KEtg1mq4fJts6cyNul9kcNbAcvFqPqxtAShXSY2E0rYnaxuMz+pdZrVetiYls5Eu3XOG6AY/sgGqMEowRdC4ditIUJLQQLsKLAgxOuscM9aliEtFmb6Uz2krOGCY1LVcdRyqFxvYff3xjNBwkUbhjghMHLL7jzb8DUKhVRzXhnXAJs0Qynmiml01SSSUJLEKWQMEBUGKUwwFIhbP1uDK2lMrmyh6OicQHBoDIG1W1QXRrToTBTXVVWgX4uRWl1jsB2o9BYRiNiiM0tkFiUR9zJn7n2bJRnoz0H7dm17y//jLK3q2ub2mpdmfESthsy6WiE2f4hU9hDTK/9wOFoJNqBiYsnhyJiGbM9YpyIfAz4GEBHRwdLlizZqxvMXLCANan9t4C4aI0bBsRUiKuCqD9WRbVd0TpyndspWbWJVgbBd2NUnRjKdshUizSWC2SqJWwTDRxa2kAFGgoFcoUxYj5YBsBCMLi2h2tXcK0KMaeEa5dxCCNXOmNhmyikMgi+CaN3LC7bujGUM4yfNqi0j9iRmsNqgmq+CX88hz/WSOjHCG1DmDBYOiBdKZMul3D9CTO+ay966GQJnUa0lSS0E2g7ibYS0eLysmOkSouAJqcXhULjYIxLQXVj0BDrR8cG0aGFqjo42icZVkgHHkm/SlyFbF2JQczWWYNSk6NWmlqGtnmrSabyhH4c5cfwvCTVcgPVSgZjLEQ0lhNi2QG2o7DtAMsJt239aobiSCvaxNDINm+VRLLA9OnP09K+7qAYiT2hlU1x43zyfQvw/XTkUaOFsJTDcT3cRBlbtjpeblvGIqptv6xsn7BDbOJZCytWwnKGSTQNkDqtHy/fytjaE4hlhkm2bSDeMAjUDJK2wVhobWN2SBZKNEpq1TQDgoCxsLSDGAcxwlxrJdgqClUissd6tYhVewGjH2Mw2G4JJx4tfuWHLqOlVvLFDvxKK65xiOHgGps4Nq5xcI2DU4t0aPxGyDXCGyJdGInm5FAbMjebq1jJLbWfIBgdQ4cNmDALxq2V41EATFwwrkBm29sK2zout7rBbj1mRddn6/12T94b26vyr1gs7nV5edgNXIvI14HbjTEP1r4/YIw5e1f5X8nA9ScqFf6jVrh1FTZz4sCznDCwnM7iIAnlkQx9EqEXfVYeidp3KyixKddGIZ6hSgMZr8TRI2M0VYuApgxUCRFCNLEokN3WkNBs9czYsW6wtXG9u7+BoHGlXCuIhNAkKKh2CmEH5bCbUthFIeyiqndudGkS1jgZexMZeyMpe3iHu9deTxwUKauKoFENZYLmPEFzHr85D270Z9ZegrCaJfByBNVGQi+L8rK1xd1lwvV2LG/MJFsDWCiOiD9DqJP0hXPBxAgwhOikgLsAABmoSURBVAJjxFhq0mz2fTLZPqY3DDA9M0hoYHWpgdXlHEN+YvvddnqFZduNbN7ZuZY3d6wnH8RI2iGutb32qrSFwcbZU4RVQMQhmz2aeLw9OldVGBl5CHDw+zJUncUw80wABgYG6O7eRdRfDbocYiVtsCd/5ibU6LLaFptbRNhTKWmwCJPHQ7xphxAwrm1x7oJ2mtORq6QxBlOtoqtV/vD005x7/vk7XMcLFcNFn+GiT/9YhWf7xnh6wxjP9o1R8qN3IenAKR1PcvH8n5OuLfIzUOzihaFFKGPjSIhj7ZwUjhViy/agh9EcywM307oSJlg1OocXR+bRX+yaUqGbAjqwyE4iV055NAalSSUWFZJGkdI+KRWQ1AEp7ZNUASkdkNR+tFUBKePj7KdyN7X4KDq/dNWU879WBq4fBC4BHhSRIzkAQ1J/5rrM7n2Rwje/RW5s+6BZuZZ2RNg6eujE2jjqTz/HX/sJRhMWn1jlMX20yrO+Zm7cJucIxVDzdFlR2sV8GINh3DL0OZoNjmaLrbf5Se/SUAhYlrWtpl6y85TcPEk7Q9bObWsROManUQlZLSQ0ZE2RlHbIyiKy3iKcqbyXBaB/63018cY+Um0riTcOYMcL2PECqaYN2PEiduzl2tobthbNPTz3smM7v7lV3YboMqc2RbU3Tzczro5irDqXofU5RldUCQeHENclsWgRsfnz6dj0bRZ1rKdv/Byee+w0/DVraHzT6cwsPcfcZT+j87LTic+Zie1kcOwMjpPBcbLYEz5bdpJSaTVjo48wNvYElfL6mm6EI9rfyxF3/Zy4ePDRr0ImGtNYsmQJ555+7j7p5kAhIkgyiZVMRotd7ETcsenOJenOJTlmWiNvO7oTAKUNq7cUeaZ3lBWbimgzk+f882lWj5NXR1JhGrGdwrioWtqVk6ZjCW3ZOO0NcZpSMaxJ4ps9++xSjj32uFf8e097xWduJ5NwmNueoSGxay/GiYXva43D0UjcAVwgIg8SFVkf3983OMlx6Ols5xPnDfHhoz/MW2e8FUssXhp/iS888AUGy4N88ZQv8raZb9vhPNt1cOMJbu4f5ZuPreXKxdPo+kAbi0Qw2rDqD0+SfqjMmxzFWOPP+OueEfqCyMbNy83jvOnncd70czmiYca2axo0Vz34lywdWsrNF97MvKZ5u5X9uqXXce0z13LlvPdyzWnXYO3KDW9wJVx7UhTJ9qzPYbTBq4STZn3ooT9wxhln7l5pOoDNy6HvCRjvAw+0p3aYSeo4CtuaWg1JVQLGnmkn8bbLSJ/UueMxVcLzNlMt9RP8+Gqay1vg3T/l0UKek06ezvD6uxlacycpHqE9+yDzjwZrtk0mnIW9URGsvgWzzKJ0bkhTsJjz3/09zNt81l32PoLr70EXi+Te9z663vq3U5I1Ee+kpXkn/VRG4QdvBS+EP/vFNgPxWsW2hAWdWRZ07uxCtYf3Zh/RAw5nz39t6/Zw57AzErWxh7840Pfpbujh1stux7a295EvyhzDDe+5mc8u+Sx/8+T/zzqvj0+f8OmXFcTH9zRx03tO3Pa9HJT5l6f+hVu23MKCBXP4x5XvITf+J1xpPU9uRhtzc/NoSjRFzZQV4LOjW9/f25/nppH/4rYbbyDlpKiEFTz18vqXwaB0yDUtf8Vb/LdQvL//ZXm2sfIBUJdC+QJY0rtbXbT0WfiP78HVEIAO4ELYxeCfP/nuyfNuyRMGI2SPmYXt7jhr1HUbSSS6aWw8Hv2GZ7Ae/garPv1J0mefz9B/rKfyzDOkgNyCBSTecSrqpBzFzpcYG3ucYm4zekHUkdFYXsDiC25BxEaSSXq+/W1euuQS4osW0fGl/70X0gIqgLu+AkMrou+j62F0HVxx6w4LU9Wp81rjsDMSB5OJBmIrTYkmrn/z9Xz1sa/yg2U/YM34Gr521tdIu9u9dVaOruTap6+lqqIAgWvH17K5tJkPLfoQVx1/Fcllv2b8F7/k5NGLkVEBxslPtiLYBC7nLVMXfBAKL67fQ6YFUbpvCBjabc5WLPIr1039/vuJxKJm7OzuwwpYPUcDEMsEZH91G3rhQto+81dk3/pW4rNmTXqOMQZjfCxrxwkG8dmzmHP7r7EaG7GmEFZ5wgXhjs/CUzdA1+IopHyyCc7/Msyqx4Cq89rmdW0kdoVru1xz6jXMy83j649/nSvuvILvnP8dejI9PLn5Sa665yocy2F6w3QAZjbM5GtnfY0TO2qti9lnknM/ztB8mzkf+PrB/wHP/RJu+0v4wM9g9i7H/LfxwAMPcvbZh6Cwc6bgy98yB4Cea67ijwMZzrrssj2eIiKITG4E3J6evRIRgIf+JTIQZ30e3vg3e39+nTqvYupGYheICB9Y9AFmNs7k8/d/nvf/5v1cedSVXLf0OrrSXVz/5uvpynRNfnLjNGiaRS7/HOLueXHy/c4zP4LmHph3zqSDkztjbA6NnFOheTYAjhpGtc8++Pdf9t9w99/C0e+F8/764N+/Tp1DTN1I7IHTu0/n5gtu5qp7r+LbT32bY1qP4do3XhuNMeyOmWfS+Nx/w9olB0XObZSGYMPD8Oa/m5KBOOyJpaGhB0bWQNNBjkD7xI/gt5+H6afCu/7ttaHPOnX2krqRmAIzG2dy0wU38ft1v+ei2ReRcqcwEW/O+bhP3wg3vOvAC7gzTgIWf/Dg3/dA0TwbhlfDQVglDQAVwu+/BI/9B8x9E1zyQ3DrMcbrvD6pG4kp0hhv5LIFe+4P38aR7+bJE4Y48bijD5xQuyLTAemd1x59FdMyF5b/CuYehHtVRuHnH4a198Fpn6q1yA7Trrg6dQ4CdSNxoLAsCg3zYcbph1qSVz8tc6EyihNMJczoPjC0Gm55X+Te+q5r4fgPHdj71anzKqBuJOoc/tQ8nFLljQfuHmvuhZ//KVgO/MntMGN/zNWtU+fVT30krs7hT0vUz5Ss7Gby4CvFGHj0P+CmS6BhGvz5fXUDUafOBOotiTqHP7kZIDbJyn5uSYQ+3Hk1PPljWHABXHw9xA/Vyj116hye1I1EncMfJwa5I+jpvwOuPWX/XdcrQL4fzvwsnP83dRfXOnUmoW4k6rw6OOcLjD50I+1t+zPYm8BR/wBHvWc/XrNOndcWdSNR59XB4g+wfKyb9tdoOOY6dQ5X6u3rOnXq1KmzS+pGok6dOnXq7JK6kahTp06dOrukbiTq1KlTp84u2aOREJGZIjIoIo9MSM+LyFkislZEltRScy3/X4jIAyLyqIicU9vXKSK/EZEHReTHIuLubd46derUqXPwmWpL4g5jzKlbE7ARyAHfNsacW0sjIjIDeAdwDvBO4Bu18/8B+Kox5ixgELh4b/Lu+8+sU6dOnTqvhH1xgc0BozvtexPwc2OMATaLyIiI5IAFxpiHa3l+CVwOZPYi708n3kREPgZ8DKCjo4MlS5bsw884cBSLxcNWtonU5dy/1OXcf7waZITXtpz7YiTiwCdE5OPA74wx/wdoB56bkGeYaBUAa5J9e5N3B4wx1wPXA4jI4HnnnbenBZ8PFa3saYHpw4O6nPuXupz7j1eDjPDqlHPGVE54xUbCGPN94Pu1MYPvicgFwDg7FupNRF1GMsm+vcm7Ozn25xTc/YqIPGGMecOhlmNP1OXcv9Tl3H+8GmSE17acr9i7SUQcAGNMAIzVdj8IvLd2vB1wjDFFoF9ETqjleS9w917mrVOnTp06h4CptiQuEpEnJnxPAp8WkXcTGZpHgDuNMUZEnhaRh4EK8Jla/v8P+KGIaOBx4Pd7k3dffmCdOnXq1HnlSDRuXOdAICIfq42fHNbU5dy/1OXcf7waZITXtpx1I1GnTp06dXZJfcZ1nTp16tTZJXUjUadOnTp1dkndSNQ5rBCRRhFpPNRyvNYQEdlzrkNH/bnvP0TE3p/XqxuJfUREPikiHxORWYdall0hIh8WkUsO9zhYtZn09wIXHGpZdoeIfKKm02mHWpbdISLvEZEfHGo59sSr4bmLyJki0nmo5dgTIvK/gC+LSHx/XbNuJF4htZrPL4HjiEKU/I2InFY7dljU2kRkjojcSxQf6wLgwtr+w0K+rYjIW0TkN8AbgCepzcQ/DOVsEJGfAicA3cCXROTk2rHD8b9kAZeIyJyay/nhps9Xy3M/Gfgb4KTDtaJVC8T6AjAP+LExxttf1z4cX+zDmgmFgQO8aIz5uDHm68A9wFUA5vBxGQuAvzbG/ClwC3AiHD7yTSgM3gB80xjzMeA+4K1w+Mg5ARtYaYz5mDHmH4A7gf8NYIzRh1QyJjVUNvAS8Ek4fPS5dSIu0ft4WD73nXR5DrAZWATMOTQSTc4EozVEZGi/YYxZvzUq9/6gvsb1FBGRFPA1YFxEHgKWArNFxK3NOt8CJEXkfGPMvYdIRoso8OEaY8xdwBZjzAYRSQB/CuRF5B1Exm2ViMih+ENO0GVRRO43xnx1wmEHWFbLZx3KwneCPlcbY+4GZlGLd1PT3e0i8k4Rea8x5peHUM6J7+YTxpjbaoeGgKuBj4vIqcaYR2r5D+Vz/zoQiMiDxph/nHD4sHjuu9DlLcA/AV8EFotIvzGmcCjk20nOrwNeTc5bROQW4AoRaQA6ReQu4HFjzPJ9eeb1lsQUEJEjgO8BeeAO4F+JYk+tA/5ORL4KfBBYDviHSEyALuB4ooIBY0y1tv+9RBF7fwCcDrzpEBYUE3X5a+A7O/X1Sk3ew6F2vlWfXwAwxjwFpETkgxN09xvgkMUPm+Td/IaIdNUOvxXYBHwT+K6IfF9E7EP03G2iqAojwI+B/yUi54hIupbF5hA/913ostMY01fT2cPAQmD+oZBvKzvp8gbgkyJyJtGzng0Y4J+JWj1n7+t/vW4kpkYvcIMx5su12tjNRH3S/wjcCKwF/hwoEnXxHCreDfwEWCciX5yw/2ZjzKeMMU8QBUzccgib9Dvr8kaiwngrvyZqYfQcEul2ZKI+v1zb94/Ax0SktfZ9HlEr8lCxsz5vALbqrkw0fnI50AAMGWPUoRETDZwK/MgYs5SoFnwu2yOR/opD/9wn0+W2d9MYcz9RwXzaIR7E3lmX/0TUJeYDnzLGXF2r0BSJnvk+/dfrRmIK1JQ8MdBgB1AwxuSNMcuBW4HvAqcAAwdbvgl9+z81xtxH1Cx+54QapSMi80TkBuAsYPXBlnErk+iyG+if8L0LiAGJgyXTzgOlu9DnBSIyzRjzJFEt87Mi8mvgTGDVoZATJtVnD1FhB9BJZCC+R1So/OJAywiT6tOqyXkXcDaAMeb22uG5te00wOUgPfe90GV/Lf/WsvJeIjkPSo/BFHV5G1F33WxjTFFEZovITUTv5j7/1+tGYgIi4orIKTLBz3jry7G1BiZRSPTVxpjNIuLUBo4WAk8YY95tjOmd9OL7T0ZHRD4uIidO2L1VxqHaSzRCVDP76wl5zgD+YIx5T632cUCZTM7d6HJLLb8YY14gWoxqzYGWsSaDTeSdNpFd6fNLteP/DHwVuL72zJ/jADOZnLt7N2tZvmKMebsxZpkxZqTWkjyocu5UyK0B2kXkyNr3h4Erar9hGfCLg/HcJ5NxT+8m29+JZcaYb9XeiYMhZ8NEOSccnkyXH6x9Ph5YYox5pzHmmX0WxBhTT1Fr7O1EA2efAezaPplwfFpt+xGiVfXeSeRNcMZBlPFy4H7geWDGJDL27JT/aeDs2mfrMJJzMl0+AZx7kJ/55UQ1sn8H3ryzjnahz7MOpoxTlHNX+tz67OUgyXkFUU38h0Rdde5Ox08jKsj+rfb9fUQegQdFvinKuFtdHmQ5f0/UCnwz0VIKe9LlXx2I511vSWznDOAy4EfA3K0DfCJylkS+8RfV8r2dyJ/7PcCfGWMeOtCCiUhKRH5GtCb4h4CfE7njUZPxjJqM7xARW7a7GH4G6KvlO+CDgXsh52S6/KgxZsmBlnGCrFmi9dO/SNR9dAbR4Cl70Gf/ZNc7xHLuSp8PwMFxKZVoTZh3AJ8iGmc6nlrNVkTOFpGfAEcTdc2Wap44H6W2xMCBlm8KMu7uf75NlwdJzuaaHJ8h0teZ1Fpce9Dlb2H/P+/XrQusiHQAjcCAiRY7UkQvxAVELYrNtT78c4BfG2P+q3bq94CbzHY3wwMtY84Ys0JEPm+M2VDb/xLRoCQikiR68W81xvxk4vkmGmg74OyDnAdNlxPkbCQq6DNE/bWbiPqXPwE01Fr05wG3G2Numnj+QdbnK5HzUOmzl8irZr0x5kURWUU01nCWiNwHvJHoP3Rz7bwvAu3GmI2HkYxvAm7bKiMHX5cNgG8ij8Tjibq6XhCRtTXZPycifyAa7L99a3l0MHT5ugwVLiIfBL5MNAjVacz/a+/cYu2qqjD8/RyklEKoUAIxhEsIphRQxKQPhCrSgpWbAt5Sgja8NCliTOEBozVEqjSFhGCBEDWnQsKtaGLCJQSSVitawwPGKKAoDQ9G0xIgehpubfl9GHPbnZPuenrOOmuveTq+ZCf7slbzde999lhzzjHG9NWSHgJesr1G0oeJK4wR23cN2XEz8BHbX+h77Y7iOrqP81pNbZ2sZ9uM85xHDM+vIQLXQuKq7A3gGNs39p03zPdzwp5tM85zLnEl+wxwg+0/SFpF+D/svjUbtVgDMVnHtlG0d1lD/K2sk3Qs8Tkvs/0PRU3EdUSAe7zvvFbey4NuuqkM3z9DzH9fTxSjXAJsITJ/sP0WkaX0ejmn1RHXOMeVwJikr/ctqG8Ezhq3wK7i3uYP2qQ922QfnruAa8oV+BPA1bZXESmPuyUd1reQOcz3c8KebbIPTxFTIj8GvlKuzGcTmWrvlXN672dbAWLSjkPgXOBlolDv47bfIILELQC23yZGlDuL50h5vpX38qALEo5Kyd3sLa//AfBF4FGiEnS5pEXEnHrvi717yI4/JIbDvVL7EaKa9tC+c1ofElbsuQb4bLliu4qoTl1AFEmO2X6/rT/AGeS5jFhfuJlYEF4PvEtM47b2g1aTYx9HEL8/TxGjHWzfCZyqaMx5NnAOkXqLW651OeiChKQjicrooyXNsv0i8UO2jBjSmVgkfGz8nPQQHV8m5iW/UQ7ZAZxJ+dIMi8o9/0VcHHyPWAy+F9hoe016HrDni8TI+4ZyyGLgaeB5t5TKXKljb/T/iO3XiCAxS1Jv8Xw1MdL5EbGm88wwPGdskJD0VUmf63vcG6LtJH7IPkppeEcsUh0LvG77fuAy2w93zHEU2KPIunoVuLYcN+3MUM+fEOtRf7L9HeDCNj7zGew5Sly5Q/Q1W2R7QzoO9OwVxf0PR+3FY0Tn3hHbv7O9Hljct6DeOjMuu0mRPvYQcQU2ImmO7Z97b5HMQuKLsh34pqSPEaOI9b0PbbqnRKbo2NrQ+GDw7P0b6dmI593FsY2CuM47/h/PD8rrnyISZDaXU7YQSQpLiDqJYU6DBW6xQKSNG3AK8N1yfyFwd7k/B7iHyHQ4vTy3iBjSLUnH9EzPejxrcJyg5yZgwbhzjmzbc3+36lNgS+bRlUSf/z+WK4gVxHzjhUT/lVGiC+p2239Jx/RMz7o8a3CsyfNAmAlrEucQ1ZGflyTbzxNdEc8n0sZuJhqdXdr7QNTwHrAzxDE907PLnjU41uQ5YaoMEiUlsMeXifL5Q4i5PIiunIfavtbRpfUD9nbGxC2kkNXgmJ7p2WXPGhxr8pwsVU03KSoTv090Rvwl8CAw2/bbkpYSLTRut/2moqXGGDCLSCNba/s36Zie6dltzxoca/KcKrUFidXErlGbiGK3fwKjtscknUrsbPWm7VHFlp3nASfZ/lk6pmd61uFZg2NNnlNmOlbDm7wRb/464GJir9mjyvNnEttKfqk8HiHm/e4B5qdjeqZnPZ41ONbk2eSts2sSkmZLuo/oyvo3on/6WUSmAMSmG38G5kr6kGNe7xWigddb6Zie6dl9zxoca/KcDjpbTGf7HUm/sP0sgKSrgN8C10ma72j3u4fIhd5VztkBtNLatxbH9EzPLnvW4FiT53TQ5ZGEgN/3PXURsI3YVeq28voZxObpQwl2NThCejZNejZHDY5Qj+d00Nn/jGNibwxA0mnAe7a3AdskzSdaFRwHXO+Wu7TW5Jie6dllzxoca/KcDqrIblLs97CTmAtcC2wlsgjeH6pYHzU4Qno2TXo2Rw2OUI9nU3R2JDGOE4GVREvvDd67lWiXqMER0rNp0rM5anCEejwboZaRxBXE/rT3djVa1+AI6dk06dkcNThCPZ5NUUuQaHWf4clQgyOkZ9OkZ3PU4Aj1eDZFFUEiSZIkGQ6dTYFNkiRJhk8GiSRJkmQgGSSSJEmSgWSQSJIkSQZSS51EknQKSf8BXigPdwN32n5yP8efABxt+69t+CVJU2SQSJLJ8ZLtCwAkHQ88Iunftp8bcPxS4HAgg0RSFTndlCRTxPZ24CbgawCSvi1pk6QXJF0u6ZPE3sarJK0rx9wi6deStpTXk6ST5EgiSZrhNeCkcv+ntm+TdHK5f5GktcDhtu+TtASYa/vTko4BHgAuG452kuyfDBJJ0gxnA3+XdAjwrdIuehdw1D6OPRdYLOlX5fFIO4pJcuBkkEiSKVJaR98KLAc+AcyzvaJMIy0ph+0BZpX7rwAbbd9azj+iXeMkmTgZJJJkciyQtJn48d8BLLf9qqQ5wPzy2tN9x28FnpB0HLAaWCrpOWKPgg3Axnb1k2RiZO+mJEmSZCCZ3ZQkSZIMJINEkiRJMpAMEkmSJMlAMkgkSZIkA8kgkSRJkgwkg0SSJEkykAwSSZIkyUD+C7G9uqwiPWmKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16076901978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = ((res.Close - res.Open) * res.y_true.shift(1)).cumsum()\n",
    "p1.plot(grid=True, legend=False)\n",
    "p1.mean(1).plot(grid=True, lw=5, color='#00F0F0')\n",
    "plt.title('y_true, 수익', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16076d3e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p2 = (((res.Close - res.Open) * res.y_true.shift(1) / res.Open) + 1).fillna(1).cumprod()\n",
    "p2.plot(grid=True, legend=False)\n",
    "p2.mean(1).plot(grid=True, lw=5, color='#00F0F0')\n",
    "plt.title('y_true, 수익률', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16076fbbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p3 = ((res.Close - res.Open) * res.pred.shift(1)).cumsum()\n",
    "p3.plot(grid=True, legend=False)\n",
    "p3.mean(1).plot(grid=True, lw=5, color='#00F0F0')\n",
    "plt.title('pred, 수익', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16077373dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p41 = (((res.Close - res.Open) * res.pred / res.Open) + 1).fillna(1).cumprod()\n",
    "p41.plot(grid=True, legend=False)\n",
    "p41.mean(1).plot(grid=True, lw=5, color='#00F0F0')\n",
    "plt.title('pred, 수익률', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160778a8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p4 = (((res.Close - res.Open) * res.pred.shift(1) / res.Open) + 1).fillna(1).cumprod()\n",
    "p4.plot(grid=True, legend=False)\n",
    "p4.mean(1).plot(grid=True, lw=5, color='#00F0F0')\n",
    "plt.title('pred, 수익률', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_t = pd.Series(index=res.pred.index)\n",
    "portfolio_t.iloc[0] = 1e8\n",
    "for i in range(len(portfolio_t)-1):\n",
    "    money_stock_invest = portfolio_t.iloc[i] / len(res.pred.columns)\n",
    "    stock_share = (money_stock_invest / res.Open.iloc[i+1]).replace(np.inf,0).astype(int)\n",
    "    portfolio_t.iloc[i+1] = portfolio_t.iloc[i] + sum((res.Close.iloc[i+1] - res.Open.iloc[i+1]) * stock_share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x160774df8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_t.plot(grid=True)\n",
    "plt.title('Total', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = pd.Series(index=res.pred.index)\n",
    "portfolio.iloc[0] = 1e8\n",
    "for i in range(len(portfolio)-1):\n",
    "    money_stock_invest = portfolio.iloc[i] / res.y_true.sum(1).iloc[i]\n",
    "    stock_share = (money_stock_invest / (res.y_true.iloc[i] * res.Open.iloc[i+1])).replace(np.inf,0).astype(int)\n",
    "    portfolio.iloc[i+1] = portfolio.iloc[i] + sum((res.Close.iloc[i+1] - res.Open.iloc[i+1]) * stock_share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16074db7240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio.plot(grid=True)\n",
    "plt.title('y_true', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio2 = pd.Series(index=res.pred.index)\n",
    "portfolio2.iloc[0] = 1e8\n",
    "for i in range(len(portfolio2)-1):\n",
    "    money_stock_invest = portfolio2.iloc[i] / res.pred.sum(1).iloc[i]\n",
    "    stock_share = (money_stock_invest / (res.pred.iloc[i] * res.Open.iloc[i+1])).replace(np.inf,0).astype(int)\n",
    "    portfolio2.iloc[i+1] = portfolio2.iloc[i] + sum((res.Close.iloc[i+1] - res.Open.iloc[i+1]) * stock_share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16074e244a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio2.plot(grid=True)\n",
    "plt.title('pred', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio 검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-06-20    100000000.0\n",
       "2018-06-21            NaN\n",
       "2018-06-22            NaN\n",
       "2018-06-25            NaN\n",
       "2018-06-26            NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio = pd.Series(index=res.pred.index)\n",
    "portfolio.iloc[0] = 1e8 # 초기 자본\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000000.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 주식딩 투자 자본\n",
    "money_stock_invest = portfolio.iloc[0] / res.y_true.sum(1).iloc[0]\n",
    "money_stock_invest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1일차 주식 보유 (다음날 Open에 사서 당일 Close로 청산)\n",
    "stock_share = (money_stock_invest / (res.y_true.iloc[0] * res.Open.iloc[1])).replace(np.inf,0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1일차 Daily 이득\n",
    "portfolio.iloc[1] = portfolio.iloc[0] + sum((res.Close.iloc[1] - res.Open.iloc[1]) * stock_share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-06-20    100000000.0\n",
       "2018-06-21    101312780.0\n",
       "2018-06-22            NaN\n",
       "2018-06-25            NaN\n",
       "2018-06-26            NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}