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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KOSPI 200 지수 예측 프로젝트\n",
"\n",
"---\n",
"\n",
"🏝[중간보고서링크](http://khuhub.khu.ac.kr/2020-2-capstone-design2/2016103208/tree/master/%E1%84%87%E1%85%A9%E1%84%80%E1%85%A9%E1%84%89%E1%85%A5)"
]
}
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"source": [
"## LSTM 모델\n",
"\n",
"일반신경망에 시계열 개념이 추가되어 은닉계층에 이전 정보를 기억시킬 수 있는 순환신경망이 시계열 데이터인 주가 예측 모델로 적합<br>\n",
"1. 단방향 LSTM 순환신경망\n",
" - 입력 순서를 시간 순서대로 처리하기 때문에 결과물은 주로 직전 패턴을 기반으로 하는 경향 (한계점)\n",
"2. 양방향 LSTM 순환신경망\n",
" - 데이터의 흐름에 역방향에 은닉계층이 추가되는 양방향 LSTM 순환신경망\n",
"3. 성능 측정 : 실제 주가와 예측된 주가 간의 평균 제곱근 오차(RMSE)\n",
"---\n",
"\n",
"**2주차 목표 : 단방향 LSTM 과 양방향 LSTM의 성능(RMSE)과 예측률 비교**\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
]
}
],
"source": [
"from keras.models import Model, Sequential\n",
"from keras.layers import Input, Dense, LSTM, Bidirectional\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"from keras import backend as K\n",
"import matplotlib.pyplot as plt\n",
"from keras.layers.core import Dense, Activation, Dropout"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 단방향(Unidirectional) LSTM\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (1) 데이터 불러오기\n",
"### case1. Simple Price Data (시가, 종가, 고가, 저가, 거래량)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/train_scaled.csv')\n",
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/test_scaled.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/validation_scaled.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# 불필요한 열 삭제\n",
"del train['Unnamed: 0']\n",
"del test['Unnamed: 0']\n",
"del validation['Unnamed: 0']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking if any null values are present in train\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n",
"checking if any null values are present in test\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n",
"checking if any null values are present in validation\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n"
]
}
],
"source": [
"# NULL value 없나 확인해보기\n",
"print(\"checking if any null values are present in train\\n\", train.isna().sum())\n",
"print(\"checking if any null values are present in test\\n\", test.isna().sum())\n",
"print(\"checking if any null values are present in validation\\n\", validation.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
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"text/plain": [
" close open high low vol\n",
"0 0.011719 0.011665 0.011228 0.012979 0.290444"
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},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"#### Keras RNN 계열의 모델을 트레이닝할 때 요구되는 데이터의 형식 : 3차원\n",
"3차원 데이터 : **(size, timestep, feature)** <br>\n",
"일반적인 MLP모델에서는 size, feature만 있는 2차원 모델 <br>\n",
"RNN계열은 '시간'이라는 개념이 있기 때문에 한 차원 늘어난다. -> **timestep**\n",
"\n",
"---\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def make_dataset(data, label, window_size = 20):\n",
" feature_list = []\n",
" label_list = []\n",
" for i in range(len(data)-window_size):\n",
" feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
" label_list.append(np.array(label.iloc[i+window_size]))\n",
" return np.array(feature_list), np.array(label_list) "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# feature , label 분리\n",
"feature_cols = ['open','high','low','vol']\n",
"label_cols = ['close']\n",
"\n",
"##----train_data----##\n",
"train_feature = train[feature_cols]\n",
"train_label = train[label_cols]\n",
"train_feature, train_label = make_dataset(train_feature,train_label,20)\n",
"\n",
"##----test_data----##\n",
"test_feature = test[feature_cols]\n",
"test_label = test[label_cols]\n",
"test_feature, test_label = make_dataset(test_feature,test_label,20)\n",
"\n",
"##----validation_data----##\n",
"validation_feature = validation[feature_cols]\n",
"validation_label = validation[label_cols]\n",
"validation_feature, validation_label = make_dataset(validation_feature,validation_label,20)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"X_train, y_train, X_val, y_val = train_feature, train_label, validation_feature, validation_label"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"X_test, y_test = test_feature, test_label"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(167, 20, 4)\n"
]
}
],
"source": [
"print(X_val.shape)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(167, 1)\n"
]
}
],
"source": [
"print(y_val.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (2) 모델 만들기"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm_13 (LSTM) (None, 20, 64) 17664 \n",
"_________________________________________________________________\n",
"lstm_14 (LSTM) (None, 50) 23000 \n",
"_________________________________________________________________\n",
"dense_7 (Dense) (None, 1) 51 \n",
"=================================================================\n",
"Total params: 40,715\n",
"Trainable params: 40,715\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"#keras 활용한 LSTM 모델 생성\n",
"\n",
"model = Sequential() \n",
"model.add(LSTM(64, return_sequences=True, input_shape=(20, 4))) #첫번째 LSTM 은 유닛수 50개\n",
"model.add(LSTM(50, return_sequences=False)) #두번째 LSTM 유닛수는 64개\n",
"model.add(Dense(1, activation='linear')) # 아웃풋으로 나오는 값은 1개 (다음날 하루 예측)\n",
"model.compile(loss='mse', optimizer='adam') #손실 함수 ,optimizer= rmsprop\n",
"model.summary() #모델의 개요"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 3444 samples, validate on 167 samples\n",
"Epoch 1/20\n",
"3444/3444 [==============================] - 30s 9ms/step - loss: 0.0061 - val_loss: 0.0096\n",
"Epoch 2/20\n",
"3444/3444 [==============================] - 27s 8ms/step - loss: 7.1951e-04 - val_loss: 0.0086\n",
"Epoch 3/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 6.2036e-04 - val_loss: 0.0069\n",
"Epoch 4/20\n",
"3444/3444 [==============================] - 26s 8ms/step - loss: 4.9895e-04 - val_loss: 0.0053\n",
"Epoch 5/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 4.0447e-04 - val_loss: 0.0050\n",
"Epoch 6/20\n",
"3444/3444 [==============================] - 26s 8ms/step - loss: 3.6695e-04 - val_loss: 0.0040\n",
"Epoch 7/20\n",
"3444/3444 [==============================] - 28s 8ms/step - loss: 3.1896e-04 - val_loss: 0.0033\n",
"Epoch 8/20\n",
"3444/3444 [==============================] - 27s 8ms/step - loss: 2.9615e-04 - val_loss: 0.0030\n",
"Epoch 9/20\n",
"3444/3444 [==============================] - 24s 7ms/step - loss: 2.6400e-04 - val_loss: 0.0030\n",
"Epoch 10/20\n",
"3444/3444 [==============================] - 24s 7ms/step - loss: 2.3818e-04 - val_loss: 0.0026\n",
"Epoch 11/20\n",
"3444/3444 [==============================] - 24s 7ms/step - loss: 2.3996e-04 - val_loss: 0.0026\n",
"Epoch 12/20\n",
"3444/3444 [==============================] - 31s 9ms/step - loss: 2.1847e-04 - val_loss: 0.0023\n",
"Epoch 13/20\n",
"3444/3444 [==============================] - 27s 8ms/step - loss: 2.1816e-04 - val_loss: 0.0024\n",
"Epoch 14/20\n",
"3444/3444 [==============================] - 28s 8ms/step - loss: 2.4145e-04 - val_loss: 0.0024\n",
"Epoch 15/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 2.1806e-04 - val_loss: 0.0028\n",
"Epoch 16/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 2.0134e-04 - val_loss: 0.0022\n",
"Epoch 17/20\n",
"3444/3444 [==============================] - 28s 8ms/step - loss: 2.1454e-04 - val_loss: 0.0022\n",
"Epoch 18/20\n",
"3444/3444 [==============================] - 29s 8ms/step - loss: 2.2625e-04 - val_loss: 0.0025\n",
"Epoch 19/20\n",
"3444/3444 [==============================] - 29s 8ms/step - loss: 2.1269e-04 - val_loss: 0.0021\n",
"Epoch 20/20\n",
"2330/3444 [===================>..........] - ETA: 8s - loss: 2.0376e-04"
]
}
],
"source": [
"model.fit(X_train, y_train,\n",
" validation_data=(X_val, y_val),\n",
" batch_size=10,\n",
" epochs=3) "
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"226/226 [==============================] - 0s 1ms/step\n"
]
}
],
"source": [
"score = model.evaluate(X_test, y_test, batch_size = 32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(score)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"predict = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 9))\n",
"plt.plot(y_test, label='actual')\n",
"plt.plot(predict, label='prediction')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"196/196 [==============================] - 0s 2ms/step\n",
"정확도 : 0.0102\n"
]
}
],
"source": [
"print(\"정확도 : %.4f\" %(model.evaluate(X_test,y_test)[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## GridSearch\n",
"하이퍼파라미터를 최적화하면 모델 성능을 향상시키는데 큰 도움이 된다.<br>\n",
"GridSearch는 리스트로 지정된 여러 하이퍼파라미터 값을 받아 모든 조합에 대해 모델 성능을 평가하여<br>\n",
"최적의 하이퍼파라미터 조합을 찾는다.\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"grid_param_LSTM = {\n",
" 'batch_size' : [10,20,30,40,50,75,100,150],\n",
" 'epochs' : [5,10,50,100],\n",
" 'learning_rate' : [0.0001,0.001,0.01,0.1],\n",
" 'optimizer' : ['Adam','RMSProp'],\n",
" 'activation' : ['relu','linear','tanh','sigmoid'],\n",
" 'dropout_rate' : [0.05,0.1,0.2,0.3,0.4,0.5,0.6],\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"recent_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# 데이터 역순으로 변경\n",
"recent_data = recent_data.loc[::-1]\n",
"recent_data = recent_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>0</th>\n",
" <td>20201029</td>\n",
" <td>309.94</td>\n",
" <td>308.99</td>\n",
" <td>310.56</td>\n",
" <td>306.63</td>\n",
" <td>1.3904e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20201030</td>\n",
" <td>301.60</td>\n",
" <td>308.91</td>\n",
" <td>309.08</td>\n",
" <td>301.58</td>\n",
" <td>1.7891e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20201102</td>\n",
" <td>305.95</td>\n",
" <td>302.67</td>\n",
" <td>306.43</td>\n",
" <td>301.37</td>\n",
" <td>1.1103e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20201029 309.94 308.99 310.56 306.63 1.3904e+08\n",
"1 20201030 301.60 308.91 309.08 301.58 1.7891e+08\n",
"2 20201102 305.95 302.67 306.43 301.37 1.1103e+08"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def vol_char_to_num(data):\n",
" for i in tqdm(range(len(data))):\n",
" unit = data['vol'][i][-1]\n",
" data['vol'][i] = data['vol'][i][:-1]\n",
" if unit == 'K':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000\n",
" elif unit == 'M':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000000"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/7 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
" 57%|█████▋ | 4/7 [00:00<00:00, 4.51it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 7/7 [00:01<00:00, 4.77it/s]\n"
]
}
],
"source": [
"vol_char_to_num(recent_data)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"scale_cols = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"recent_data_scaled = scaler.fit_transform(recent_data[scale_cols]) \n",
"recent_data_scaled = pd.DataFrame(recent_data_scaled)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0.406829</td>\n",
" <td>0.315212</td>\n",
" <td>0.231373</td>\n",
" <td>0.273389</td>\n",
" <td>0.746880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.000000</td>\n",
" <td>0.311222</td>\n",
" <td>0.148459</td>\n",
" <td>0.010915</td>\n",
" <td>0.961373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.212195</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.596191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.510732</td>\n",
" <td>0.272818</td>\n",
" <td>0.331653</td>\n",
" <td>0.345114</td>\n",
" <td>0.692866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.596585</td>\n",
" <td>0.580549</td>\n",
" <td>0.514846</td>\n",
" <td>0.519751</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.971220</td>\n",
" <td>0.651870</td>\n",
" <td>0.844818</td>\n",
" <td>0.722453</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.773348</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"0 0.406829 0.315212 0.231373 0.273389 0.746880\n",
"1 0.000000 0.311222 0.148459 0.010915 0.961373\n",
"2 0.212195 0.000000 0.000000 0.000000 0.596191\n",
"3 0.510732 0.272818 0.331653 0.345114 0.692866\n",
"4 0.596585 0.580549 0.514846 0.519751 0.000000\n",
"5 0.971220 0.651870 0.844818 0.722453 1.000000\n",
"6 1.000000 1.000000 1.000000 1.000000 0.773348"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_data_scaled"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"recent_data_scaled.to_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data_scaled.csv')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking if any null values are present in train\n",
" 0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(\"checking if any null values are present in train\\n\", recent_data_scaled.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
]
}
],
"source": [
"from keras.models import Model, Sequential\n",
"from keras.layers import Input, Dense, LSTM, Bidirectional\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"from keras import backend as K\n",
"import matplotlib.pyplot as plt\n",
"from keras.layers.core import Dense, Activation, Dropout\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/train_scaled.csv')\n",
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data_scaled.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/validation_scaled.csv')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# 불필요한 열 삭제\n",
"del train['Unnamed: 0']\n",
"del test['Unnamed: 0']\n",
"del validation['Unnamed: 0']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"test.columns = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking if any null values are present in train\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n",
"checking if any null values are present in test\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n",
"checking if any null values are present in validation\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n"
]
}
],
"source": [
"# NULL value 없나 확인해보기\n",
"print(\"checking if any null values are present in train\\n\", train.isna().sum())\n",
"print(\"checking if any null values are present in test\\n\", test.isna().sum())\n",
"print(\"checking if any null values are present in validation\\n\", validation.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def make_dataset(data, label,window_size = 20):\n",
" feature_list = []\n",
" label_list = []\n",
" for i in range(len(data)-window_size):\n",
" feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
" label_list.append(np.array(label.iloc[i+window_size]))\n",
" return np.array(feature_list), np.array(label_list) "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# feature , label 분리\n",
"feature_cols = ['open','high','low','vol']\n",
"label_cols = ['close']\n",
"\n",
"##----train_data----##\n",
"train_feature = train[feature_cols]\n",
"train_label = train[label_cols]\n",
"\n",
"train_feature, train_label = make_dataset(train_feature,train_label,3)\n",
"\n",
"##----test_data----##\n",
"test_feature = test[feature_cols]\n",
"test_label = test[label_cols]\n",
"test_feature, test_label = make_dataset(test_feature,test_label,3)\n",
"\n",
"##----validation_data----##\n",
"validation_feature = validation[feature_cols]\n",
"validation_label = validation[label_cols]\n",
"validation_feature, validation_label = make_dataset(validation_feature,validation_label,3)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train[label_cols].tail(6)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"train_label_pd = pd.DataFrame(train_label)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
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"execution_count": 32,
"metadata": {},
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}
],
"source": [
"train_label_pd.tail(6)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
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"text/plain": [
"3461"
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"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_label)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"X_train, y_train, X_val, y_val = train_feature, train_label, validation_feature, validation_label"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"X_test, y_test = test_feature, test_label"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(y_val)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm_7 (LSTM) (None, 20, 64) 17664 \n",
"_________________________________________________________________\n",
"lstm_8 (LSTM) (None, 50) 23000 \n",
"_________________________________________________________________\n",
"dense_4 (Dense) (None, 1) 51 \n",
"=================================================================\n",
"Total params: 40,715\n",
"Trainable params: 40,715\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"#keras 활용한 LSTM 모델 생성\n",
"\n",
"model = Sequential() \n",
"model.add(LSTM(64, return_sequences=True, input_shape=(20, 4))) #첫번째 LSTM 은 유닛수 50개\n",
"model.add(LSTM(50, return_sequences=False)) #두번째 LSTM 유닛수는 64개\n",
"model.add(Dense(1, activation='linear')) # 아웃풋으로 나오는 값은 1개 (다음날 하루 예측)\n",
"model.compile(loss='mse', optimizer='adam') #손실 함수 ,optimizer= rmsprop\n",
"model.summary() #모델의 개요"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Train on 3444 samples, validate on 167 samples\n",
"Epoch 1/100\n",
"3444/3444 [==============================] - 53s 15ms/step - loss: 0.0041 - val_loss: 0.0097\n",
"Epoch 2/100\n",
"3444/3444 [==============================] - 48s 14ms/step - loss: 7.3472e-04 - val_loss: 0.0073\n",
"Epoch 3/100\n",
"2510/3444 [====================>.........] - ETA: 13s - loss: 5.2751e-04"
]
}
],
"source": [
"early_stopping = EarlyStopping() \n",
"hist = model.fit(X_train, y_train, validation_data=(X_val, y_val),batch_size=5, epochs=100,callbacks=[early_stopping])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"score = model.evaluate(X_test, y_test, batch_size = 5)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
......@@ -30,122 +30,80 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')"
"train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv')\n",
"test_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')\n",
"validation_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')\n",
"test_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_data.csv')\n",
"validation_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/3464 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 3464/3464 [10:11<00:00, 5.89it/s]\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>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20050103</td>\n",
" <td>115.36</td>\n",
" <td>115.65</td>\n",
" <td>115.9</td>\n",
" <td>114.99</td>\n",
" <td>101240000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20050103 115.36 115.65 115.9 114.99 101240000.0"
]
},
{
"data": {
"text/plain": [
"0 2018-12-28\n",
"1 2018-12-27\n",
"2 2018-12-26\n",
"3 2018-12-24\n",
"4 2018-12-21\n",
"5 2018-12-20\n",
"6 2018-12-19\n",
"7 2018-12-18\n",
"8 2018-12-17\n",
"9 2018-12-14\n",
"10 2018-12-13\n",
"11 2018-12-12\n",
"12 2018-12-11\n",
"13 2018-12-10\n",
"14 2018-12-07\n",
"15 2018-12-06\n",
"16 2018-12-05\n",
"17 2018-12-04\n",
"18 2018-12-03\n",
"19 2018-11-30\n",
"20 2018-11-29\n",
"21 2018-11-28\n",
"22 2018-11-27\n",
"23 2018-11-26\n",
"24 2018-11-23\n",
"25 2018-11-22\n",
"26 2018-11-21\n",
"27 2018-11-20\n",
"28 2018-11-19\n",
"29 2018-11-16\n",
" ... \n",
"3434 2005-02-16\n",
"3435 2005-02-15\n",
"3436 2005-02-14\n",
"3437 2005-02-11\n",
"3438 2005-02-07\n",
"3439 2005-02-04\n",
"3440 2005-02-03\n",
"3441 2005-02-02\n",
"3442 2005-02-01\n",
"3443 2005-01-31\n",
"3444 2005-01-28\n",
"3445 2005-01-27\n",
"3446 2005-01-26\n",
"3447 2005-01-25\n",
"3448 2005-01-24\n",
"3449 2005-01-21\n",
"3450 2005-01-20\n",
"3451 2005-01-19\n",
"3452 2005-01-18\n",
"3453 2005-01-17\n",
"3454 2005-01-14\n",
"3455 2005-01-13\n",
"3456 2005-01-12\n",
"3457 2005-01-11\n",
"3458 2005-01-10\n",
"3459 2005-01-07\n",
"3460 2005-01-06\n",
"3461 2005-01-05\n",
"3462 2005-01-04\n",
"3463 2005-01-03\n",
"Name: date, Length: 3464, dtype: datetime64[ns]"
]
},
"execution_count": 54,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in tqdm(range(len(train_data))):\n",
" train_data['date'][i] = train_data['date'][i].replace(' ','') \n",
"pd.to_datetime(train_data['date'], format='%Y%m%d')"
"train_data.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"execution_count": 11,
"metadata": {},
"outputs": [
{
......@@ -180,30 +138,12 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20181228</td>\n",
" <td>261.98</td>\n",
" <td>261.61</td>\n",
" <td>262.85</td>\n",
" <td>261.27</td>\n",
" <td>62.70M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20181227</td>\n",
" <td>260.57</td>\n",
" <td>261.53</td>\n",
" <td>261.71</td>\n",
" <td>259.73</td>\n",
" <td>84.46M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20181226</td>\n",
" <td>260.98</td>\n",
" <td>261.09</td>\n",
" <td>262.49</td>\n",
" <td>259.35</td>\n",
" <td>92.49M</td>\n",
" <td>2020년 01월 02일</td>\n",
" <td>290.35</td>\n",
" <td>294.19</td>\n",
" <td>294.57</td>\n",
" <td>289.96</td>\n",
" <td>66160000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -211,38 +151,24 @@
],
"text/plain": [
" date close open high low vol\n",
"0 20181228 261.98 261.61 262.85 261.27 62.70M\n",
"1 20181227 260.57 261.53 261.71 259.73 84.46M\n",
"2 20181226 260.98 261.09 262.49 259.35 92.49M"
"0 2020년 01월 02일 290.35 294.19 294.57 289.96 66160000.0"
]
},
"execution_count": 56,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head(3)"
"test_data.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/246 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 246/246 [00:14<00:00, 18.37it/s]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
......@@ -274,30 +200,12 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20191230</td>\n",
" <td>293.77</td>\n",
" <td>294.60</td>\n",
" <td>295.49</td>\n",
" <td>293.40</td>\n",
" <td>54.97M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20191227</td>\n",
" <td>295.20</td>\n",
" <td>292.37</td>\n",
" <td>296.95</td>\n",
" <td>291.28</td>\n",
" <td>77.51M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20191226</td>\n",
" <td>294.06</td>\n",
" <td>293.12</td>\n",
" <td>294.07</td>\n",
" <td>291.79</td>\n",
" <td>68.48M</td>\n",
" <td>2019년 01월 02일</td>\n",
" <td>258.23</td>\n",
" <td>263.35</td>\n",
" <td>263.77</td>\n",
" <td>257.47</td>\n",
" <td>69950000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -305,45 +213,95 @@
],
"text/plain": [
" date close open high low vol\n",
"0 20191230 293.77 294.60 295.49 293.40 54.97M\n",
"1 20191227 295.20 292.37 296.95 291.28 77.51M\n",
"2 20191226 294.06 293.12 294.07 291.79 68.48M"
"0 2019년 01월 02일 258.23 263.35 263.77 257.47 69950000.0"
]
},
"execution_count": 9,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validation_data.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"del train_data['Unnamed: 0']\n",
"del test_data['Unnamed: 0']\n",
"del validation_data['Unnamed: 0']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for i in tqdm(range(len(train_data))):\n",
" train_data['date'][i] = train_data['date'][i].replace(' ','') \n",
"pd.to_datetime(train_data['date'], format='%Y%m%d')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for i in tqdm(range(len(test_data))):\n",
" test_data['date'][i] = test_data['date'][i].replace('년','-')\n",
" test_data['date'][i] = test_data['date'][i].replace('월','-')\n",
" test_data['date'][i] = test_data['date'][i].replace('일','')\n",
" test_data['date'][i] = test_data['date'][i].replace(' ','') \n",
"pd.to_datetime(test_data['date'], format='%Y%m%d')\n",
"\n",
"\n",
"test_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/187 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"source": [
"for i in tqdm(range(len(validation_data))):\n",
" validation_data['date'][i] = validation_data['date'][i].replace(' ','') \n",
"pd.to_datetime(validation_data['date'], format='%Y%m%d')\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 187/187 [00:11<00:00, 15.68it/s]\n"
"validation_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터프레임 행 역순으로 변경"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### train_data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"train_data = train_data.loc[::-1]\n",
"train_data = train_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
......@@ -375,30 +333,12 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20200929</td>\n",
" <td>309.44</td>\n",
" <td>310.01</td>\n",
" <td>311.34</td>\n",
" <td>308.52</td>\n",
" <td>138.82M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20200928</td>\n",
" <td>307.03</td>\n",
" <td>306.49</td>\n",
" <td>308.34</td>\n",
" <td>304.64</td>\n",
" <td>173.18M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20200925</td>\n",
" <td>303.57</td>\n",
" <td>305.38</td>\n",
" <td>305.55</td>\n",
" <td>301.93</td>\n",
" <td>156.86M</td>\n",
" <td>20050103</td>\n",
" <td>115.36</td>\n",
" <td>115.65</td>\n",
" <td>115.9</td>\n",
" <td>114.99</td>\n",
" <td>101240000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -406,41 +346,28 @@
],
"text/plain": [
" date close open high low vol\n",
"0 20200929 309.44 310.01 311.34 308.52 138.82M\n",
"1 20200928 307.03 306.49 308.34 304.64 173.18M\n",
"2 20200925 303.57 305.38 305.55 301.93 156.86M"
"0 20050103 115.36 115.65 115.9 114.99 101240000.0"
]
},
"execution_count": 10,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in tqdm(range(len(validation_data))):\n",
" validation_data['date'][i] = validation_data['date'][i].replace(' ','') \n",
"pd.to_datetime(validation_data['date'], format='%Y%m%d')\n",
"\n",
"validation_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터프레임 행 역순으로 변경"
"train_data.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### train_data"
"### test_data"
]
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 18,
"metadata": {},
"outputs": [
{
......@@ -475,273 +402,273 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20050103</td>\n",
" <td>115.36</td>\n",
" <td>115.65</td>\n",
" <td>115.90</td>\n",
" <td>114.99</td>\n",
" <td>101.24M</td>\n",
" <td>2020년 01월 02일</td>\n",
" <td>290.35</td>\n",
" <td>294.19</td>\n",
" <td>294.57</td>\n",
" <td>289.96</td>\n",
" <td>66160000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20050104</td>\n",
" <td>114.77</td>\n",
" <td>115.00</td>\n",
" <td>115.80</td>\n",
" <td>114.50</td>\n",
" <td>119.97M</td>\n",
" <td>2020년 01월 03일</td>\n",
" <td>290.74</td>\n",
" <td>293.10</td>\n",
" <td>294.88</td>\n",
" <td>289.04</td>\n",
" <td>79770000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20050105</td>\n",
" <td>114.65</td>\n",
" <td>113.41</td>\n",
" <td>114.65</td>\n",
" <td>113.17</td>\n",
" <td>142.31M</td>\n",
" <td>2020년 01월 06일</td>\n",
" <td>288.43</td>\n",
" <td>287.73</td>\n",
" <td>289.41</td>\n",
" <td>287.49</td>\n",
" <td>69960000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20050106</td>\n",
" <td>112.94</td>\n",
" <td>113.88</td>\n",
" <td>114.97</td>\n",
" <td>112.94</td>\n",
" <td>147.88M</td>\n",
" <td>2020년 01월 07일</td>\n",
" <td>291.23</td>\n",
" <td>290.01</td>\n",
" <td>292.26</td>\n",
" <td>289.64</td>\n",
" <td>65350000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20050107</td>\n",
" <td>112.71</td>\n",
" <td>113.61</td>\n",
" <td>113.94</td>\n",
" <td>112.18</td>\n",
" <td>117.39M</td>\n",
" <td>2020년 01월 08일</td>\n",
" <td>289.42</td>\n",
" <td>288.94</td>\n",
" <td>291.01</td>\n",
" <td>287.30</td>\n",
" <td>124530000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20050110</td>\n",
" <td>113.27</td>\n",
" <td>113.01</td>\n",
" <td>113.36</td>\n",
" <td>112.07</td>\n",
" <td>98.12M</td>\n",
" <td>2020년 01월 09일</td>\n",
" <td>294.41</td>\n",
" <td>293.83</td>\n",
" <td>294.41</td>\n",
" <td>292.10</td>\n",
" <td>111790000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>20050111</td>\n",
" <td>114.66</td>\n",
" <td>113.33</td>\n",
" <td>114.66</td>\n",
" <td>112.73</td>\n",
" <td>133.20M</td>\n",
" <td>2020년 01월 10일</td>\n",
" <td>297.06</td>\n",
" <td>294.80</td>\n",
" <td>297.17</td>\n",
" <td>294.17</td>\n",
" <td>94160000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20050112</td>\n",
" <td>114.09</td>\n",
" <td>114.71</td>\n",
" <td>114.94</td>\n",
" <td>113.79</td>\n",
" <td>124.80M</td>\n",
" <td>2020년 01월 13일</td>\n",
" <td>300.13</td>\n",
" <td>296.62</td>\n",
" <td>300.31</td>\n",
" <td>296.53</td>\n",
" <td>81680000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20050113</td>\n",
" <td>115.01</td>\n",
" <td>114.26</td>\n",
" <td>115.08</td>\n",
" <td>113.69</td>\n",
" <td>106.38M</td>\n",
" <td>2020년 01월 14일</td>\n",
" <td>301.53</td>\n",
" <td>302.20</td>\n",
" <td>303.61</td>\n",
" <td>300.69</td>\n",
" <td>92120000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20050114</td>\n",
" <td>117.82</td>\n",
" <td>114.23</td>\n",
" <td>117.82</td>\n",
" <td>113.91</td>\n",
" <td>151.41M</td>\n",
" <td>2020년 01월 15일</td>\n",
" <td>299.74</td>\n",
" <td>299.91</td>\n",
" <td>301.13</td>\n",
" <td>298.82</td>\n",
" <td>71770000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20050117</td>\n",
" <td>119.91</td>\n",
" <td>118.96</td>\n",
" <td>120.28</td>\n",
" <td>118.70</td>\n",
" <td>189.66M</td>\n",
" <td>2020년 01월 16일</td>\n",
" <td>302.78</td>\n",
" <td>299.93</td>\n",
" <td>302.91</td>\n",
" <td>298.95</td>\n",
" <td>85710000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20050118</td>\n",
" <td>119.50</td>\n",
" <td>119.91</td>\n",
" <td>120.42</td>\n",
" <td>119.49</td>\n",
" <td>172.12M</td>\n",
" <td>2020년 01월 17일</td>\n",
" <td>303.30</td>\n",
" <td>305.07</td>\n",
" <td>306.05</td>\n",
" <td>302.51</td>\n",
" <td>81540000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20050119</td>\n",
" <td>118.90</td>\n",
" <td>119.85</td>\n",
" <td>120.40</td>\n",
" <td>118.66</td>\n",
" <td>177.45M</td>\n",
" <td>2020년 01월 20일</td>\n",
" <td>305.58</td>\n",
" <td>304.99</td>\n",
" <td>307.54</td>\n",
" <td>304.77</td>\n",
" <td>78040000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>20050120</td>\n",
" <td>118.02</td>\n",
" <td>118.05</td>\n",
" <td>118.74</td>\n",
" <td>117.72</td>\n",
" <td>165.45M</td>\n",
" <td>2020년 01월 21일</td>\n",
" <td>302.11</td>\n",
" <td>304.94</td>\n",
" <td>306.09</td>\n",
" <td>301.85</td>\n",
" <td>67630000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>20050121</td>\n",
" <td>119.40</td>\n",
" <td>118.14</td>\n",
" <td>119.41</td>\n",
" <td>117.62</td>\n",
" <td>153.51M</td>\n",
" <td>2020년 01월 22일</td>\n",
" <td>306.08</td>\n",
" <td>301.79</td>\n",
" <td>306.52</td>\n",
" <td>301.16</td>\n",
" <td>79330000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>20050124</td>\n",
" <td>119.83</td>\n",
" <td>119.19</td>\n",
" <td>120.66</td>\n",
" <td>118.34</td>\n",
" <td>215.32M</td>\n",
" <td>2020년 01월 23일</td>\n",
" <td>302.33</td>\n",
" <td>303.77</td>\n",
" <td>304.72</td>\n",
" <td>301.71</td>\n",
" <td>86910000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20050125</td>\n",
" <td>118.91</td>\n",
" <td>118.92</td>\n",
" <td>119.25</td>\n",
" <td>118.35</td>\n",
" <td>190.17M</td>\n",
" <td>2020년 01월 28일</td>\n",
" <td>292.77</td>\n",
" <td>294.98</td>\n",
" <td>296.30</td>\n",
" <td>291.30</td>\n",
" <td>130170000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>20050126</td>\n",
" <td>120.51</td>\n",
" <td>119.36</td>\n",
" <td>120.95</td>\n",
" <td>119.16</td>\n",
" <td>194.21M</td>\n",
" <td>2020년 01월 29일</td>\n",
" <td>293.98</td>\n",
" <td>294.38</td>\n",
" <td>295.67</td>\n",
" <td>292.45</td>\n",
" <td>85730000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20050127</td>\n",
" <td>120.16</td>\n",
" <td>120.65</td>\n",
" <td>121.00</td>\n",
" <td>119.61</td>\n",
" <td>184.68M</td>\n",
" <td>2020년 01월 30일</td>\n",
" <td>288.37</td>\n",
" <td>293.27</td>\n",
" <td>294.11</td>\n",
" <td>287.09</td>\n",
" <td>101540000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20050128</td>\n",
" <td>119.65</td>\n",
" <td>120.14</td>\n",
" <td>120.25</td>\n",
" <td>118.98</td>\n",
" <td>156.89M</td>\n",
" <td>2020년 01월 31일</td>\n",
" <td>284.53</td>\n",
" <td>290.24</td>\n",
" <td>291.47</td>\n",
" <td>284.53</td>\n",
" <td>101460000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20050131</td>\n",
" <td>121.06</td>\n",
" <td>119.45</td>\n",
" <td>121.21</td>\n",
" <td>119.45</td>\n",
" <td>141.84M</td>\n",
" <td>2020년 02월 03일</td>\n",
" <td>285.05</td>\n",
" <td>280.17</td>\n",
" <td>286.24</td>\n",
" <td>279.78</td>\n",
" <td>111060000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20050201</td>\n",
" <td>120.00</td>\n",
" <td>120.74</td>\n",
" <td>120.97</td>\n",
" <td>119.91</td>\n",
" <td>132.38M</td>\n",
" <td>2020년 02월 04일</td>\n",
" <td>290.68</td>\n",
" <td>285.19</td>\n",
" <td>291.38</td>\n",
" <td>285.11</td>\n",
" <td>105450000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20050202</td>\n",
" <td>119.74</td>\n",
" <td>120.30</td>\n",
" <td>120.37</td>\n",
" <td>119.41</td>\n",
" <td>154.26M</td>\n",
" <td>2020년 02월 05일</td>\n",
" <td>292.02</td>\n",
" <td>293.57</td>\n",
" <td>294.26</td>\n",
" <td>290.30</td>\n",
" <td>102640000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>20050203</td>\n",
" <td>120.64</td>\n",
" <td>119.88</td>\n",
" <td>120.80</td>\n",
" <td>119.71</td>\n",
" <td>166.09M</td>\n",
" <td>2020년 02월 06일</td>\n",
" <td>300.65</td>\n",
" <td>294.74</td>\n",
" <td>300.98</td>\n",
" <td>294.32</td>\n",
" <td>110220000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20050204</td>\n",
" <td>121.30</td>\n",
" <td>120.50</td>\n",
" <td>121.65</td>\n",
" <td>120.18</td>\n",
" <td>153.74M</td>\n",
" <td>2020년 02월 07일</td>\n",
" <td>298.21</td>\n",
" <td>299.59</td>\n",
" <td>300.05</td>\n",
" <td>296.01</td>\n",
" <td>108110000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20050207</td>\n",
" <td>123.20</td>\n",
" <td>122.41</td>\n",
" <td>123.27</td>\n",
" <td>122.25</td>\n",
" <td>142.40M</td>\n",
" <td>2020년 02월 10일</td>\n",
" <td>296.24</td>\n",
" <td>294.07</td>\n",
" <td>296.73</td>\n",
" <td>293.33</td>\n",
" <td>72220000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20050211</td>\n",
" <td>122.84</td>\n",
" <td>122.60</td>\n",
" <td>122.99</td>\n",
" <td>121.39</td>\n",
" <td>140.35M</td>\n",
" <td>2020년 02월 11일</td>\n",
" <td>299.28</td>\n",
" <td>297.91</td>\n",
" <td>301.10</td>\n",
" <td>297.48</td>\n",
" <td>78350000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20050214</td>\n",
" <td>124.94</td>\n",
" <td>124.06</td>\n",
" <td>125.44</td>\n",
" <td>123.65</td>\n",
" <td>235.22M</td>\n",
" <td>2020년 02월 12일</td>\n",
" <td>301.54</td>\n",
" <td>299.83</td>\n",
" <td>302.20</td>\n",
" <td>298.09</td>\n",
" <td>110630000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20050215</td>\n",
" <td>125.48</td>\n",
" <td>124.99</td>\n",
" <td>125.49</td>\n",
" <td>124.37</td>\n",
" <td>236.83M</td>\n",
" <td>2020년 02월 13일</td>\n",
" <td>300.93</td>\n",
" <td>302.79</td>\n",
" <td>304.43</td>\n",
" <td>300.48</td>\n",
" <td>115070000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>20050216</td>\n",
" <td>125.85</td>\n",
" <td>126.08</td>\n",
" <td>126.82</td>\n",
" <td>125.13</td>\n",
" <td>218.70M</td>\n",
" <td>2020년 02월 14일</td>\n",
" <td>303.01</td>\n",
" <td>300.95</td>\n",
" <td>304.20</td>\n",
" <td>299.18</td>\n",
" <td>80530000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
......@@ -753,459 +680,367 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3434</th>\n",
" <td>20181116</td>\n",
" <td>271.65</td>\n",
" <td>272.95</td>\n",
" <td>273.52</td>\n",
" <td>271.06</td>\n",
" <td>74.60M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3435</th>\n",
" <td>20181119</td>\n",
" <td>272.31</td>\n",
" <td>272.01</td>\n",
" <td>274.05</td>\n",
" <td>270.83</td>\n",
" <td>75.01M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3436</th>\n",
" <td>20181120</td>\n",
" <td>269.58</td>\n",
" <td>268.54</td>\n",
" <td>270.58</td>\n",
" <td>268.54</td>\n",
" <td>80.90M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3437</th>\n",
" <td>20181121</td>\n",
" <td>268.27</td>\n",
" <td>265.96</td>\n",
" <td>268.49</td>\n",
" <td>264.95</td>\n",
" <td>81.84M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3438</th>\n",
" <td>20181122</td>\n",
" <td>267.76</td>\n",
" <td>268.51</td>\n",
" <td>269.11</td>\n",
" <td>267.15</td>\n",
" <td>64.41M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3439</th>\n",
" <td>20181123</td>\n",
" <td>266.33</td>\n",
" <td>268.12</td>\n",
" <td>268.54</td>\n",
" <td>264.86</td>\n",
" <td>58.35M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3440</th>\n",
" <td>20181126</td>\n",
" <td>269.79</td>\n",
" <td>266.43</td>\n",
" <td>269.95</td>\n",
" <td>266.41</td>\n",
" <td>75.65M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3441</th>\n",
" <td>20181127</td>\n",
" <td>272.08</td>\n",
" <td>270.90</td>\n",
" <td>272.47</td>\n",
" <td>269.27</td>\n",
" <td>71.91M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3442</th>\n",
" <td>20181128</td>\n",
" <td>273.45</td>\n",
" <td>272.64</td>\n",
" <td>273.53</td>\n",
" <td>271.35</td>\n",
" <td>79.83M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3443</th>\n",
" <td>20181129</td>\n",
" <td>274.16</td>\n",
" <td>277.08</td>\n",
" <td>277.54</td>\n",
" <td>273.40</td>\n",
" <td>80.65M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3444</th>\n",
" <td>20181130</td>\n",
" <td>271.35</td>\n",
" <td>274.53</td>\n",
" <td>275.51</td>\n",
" <td>270.88</td>\n",
" <td>137.53M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3445</th>\n",
" <td>20181203</td>\n",
" <td>276.18</td>\n",
" <td>275.70</td>\n",
" <td>276.98</td>\n",
" <td>273.59</td>\n",
" <td>89.07M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3446</th>\n",
" <td>20181204</td>\n",
" <td>273.29</td>\n",
" <td>275.08</td>\n",
" <td>275.72</td>\n",
" <td>272.03</td>\n",
" <td>80.80M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3447</th>\n",
" <td>20181205</td>\n",
" <td>271.49</td>\n",
" <td>269.53</td>\n",
" <td>272.38</td>\n",
" <td>269.47</td>\n",
" <td>75.64M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3448</th>\n",
" <td>20181206</td>\n",
" <td>267.18</td>\n",
" <td>270.18</td>\n",
" <td>270.18</td>\n",
" <td>266.61</td>\n",
" <td>86.09M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3449</th>\n",
" <td>20181207</td>\n",
" <td>268.28</td>\n",
" <td>268.97</td>\n",
" <td>269.66</td>\n",
" <td>267.03</td>\n",
" <td>68.45M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3450</th>\n",
" <td>20181210</td>\n",
" <td>265.35</td>\n",
" <td>264.55</td>\n",
" <td>266.20</td>\n",
" <td>263.63</td>\n",
" <td>77.30M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3451</th>\n",
" <td>20181211</td>\n",
" <td>264.69</td>\n",
" <td>265.87</td>\n",
" <td>265.99</td>\n",
" <td>263.87</td>\n",
" <td>70.04M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3452</th>\n",
" <td>20181212</td>\n",
" <td>268.47</td>\n",
" <td>266.00</td>\n",
" <td>268.84</td>\n",
" <td>265.55</td>\n",
" <td>83.69M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3453</th>\n",
" <td>20181213</td>\n",
" <td>269.41</td>\n",
" <td>269.07</td>\n",
" <td>271.06</td>\n",
" <td>268.22</td>\n",
" <td>145.06M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3454</th>\n",
" <td>20181214</td>\n",
" <td>265.55</td>\n",
" <td>269.69</td>\n",
" <td>269.69</td>\n",
" <td>264.44</td>\n",
" <td>99.73M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3455</th>\n",
" <td>20181217</td>\n",
" <td>266.33</td>\n",
" <td>265.81</td>\n",
" <td>267.19</td>\n",
" <td>265.41</td>\n",
" <td>71.45M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3456</th>\n",
" <td>20181218</td>\n",
" <td>265.18</td>\n",
" <td>264.42</td>\n",
" <td>266.83</td>\n",
" <td>263.93</td>\n",
" <td>90.33M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3457</th>\n",
" <td>20181219</td>\n",
" <td>267.31</td>\n",
" <td>266.02</td>\n",
" <td>267.55</td>\n",
" <td>265.26</td>\n",
" <td>81.75M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3458</th>\n",
" <td>20181220</td>\n",
" <td>264.81</td>\n",
" <td>265.13</td>\n",
" <td>266.34</td>\n",
" <td>263.12</td>\n",
" <td>81.34M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3459</th>\n",
" <td>20181221</td>\n",
" <td>265.06</td>\n",
" <td>263.66</td>\n",
" <td>265.06</td>\n",
" <td>263.15</td>\n",
" <td>92.60M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3460</th>\n",
" <td>20181224</td>\n",
" <td>264.45</td>\n",
" <td>263.51</td>\n",
" <td>265.09</td>\n",
" <td>262.96</td>\n",
" <td>61.96M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3461</th>\n",
" <td>20181226</td>\n",
" <td>260.98</td>\n",
" <td>261.09</td>\n",
" <td>262.49</td>\n",
" <td>259.35</td>\n",
" <td>92.49M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3462</th>\n",
" <td>20181227</td>\n",
" <td>260.57</td>\n",
" <td>261.53</td>\n",
" <td>261.71</td>\n",
" <td>259.73</td>\n",
" <td>84.46M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3463</th>\n",
" <td>20181228</td>\n",
" <td>261.98</td>\n",
" <td>261.61</td>\n",
" <td>262.85</td>\n",
" <td>261.27</td>\n",
" <td>62.70M</td>\n",
" <th>181</th>\n",
" <td>2020년 09월 22일</td>\n",
" <td>308.82</td>\n",
" <td>315.53</td>\n",
" <td>316.02</td>\n",
" <td>307.40</td>\n",
" <td>304360000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3464 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20050103 115.36 115.65 115.90 114.99 101.24M\n",
"1 20050104 114.77 115.00 115.80 114.50 119.97M\n",
"2 20050105 114.65 113.41 114.65 113.17 142.31M\n",
"3 20050106 112.94 113.88 114.97 112.94 147.88M\n",
"4 20050107 112.71 113.61 113.94 112.18 117.39M\n",
"5 20050110 113.27 113.01 113.36 112.07 98.12M\n",
"6 20050111 114.66 113.33 114.66 112.73 133.20M\n",
"7 20050112 114.09 114.71 114.94 113.79 124.80M\n",
"8 20050113 115.01 114.26 115.08 113.69 106.38M\n",
"9 20050114 117.82 114.23 117.82 113.91 151.41M\n",
"10 20050117 119.91 118.96 120.28 118.70 189.66M\n",
"11 20050118 119.50 119.91 120.42 119.49 172.12M\n",
"12 20050119 118.90 119.85 120.40 118.66 177.45M\n",
"13 20050120 118.02 118.05 118.74 117.72 165.45M\n",
"14 20050121 119.40 118.14 119.41 117.62 153.51M\n",
"15 20050124 119.83 119.19 120.66 118.34 215.32M\n",
"16 20050125 118.91 118.92 119.25 118.35 190.17M\n",
"17 20050126 120.51 119.36 120.95 119.16 194.21M\n",
"18 20050127 120.16 120.65 121.00 119.61 184.68M\n",
"19 20050128 119.65 120.14 120.25 118.98 156.89M\n",
"20 20050131 121.06 119.45 121.21 119.45 141.84M\n",
"21 20050201 120.00 120.74 120.97 119.91 132.38M\n",
"22 20050202 119.74 120.30 120.37 119.41 154.26M\n",
"23 20050203 120.64 119.88 120.80 119.71 166.09M\n",
"24 20050204 121.30 120.50 121.65 120.18 153.74M\n",
"25 20050207 123.20 122.41 123.27 122.25 142.40M\n",
"26 20050211 122.84 122.60 122.99 121.39 140.35M\n",
"27 20050214 124.94 124.06 125.44 123.65 235.22M\n",
"28 20050215 125.48 124.99 125.49 124.37 236.83M\n",
"29 20050216 125.85 126.08 126.82 125.13 218.70M\n",
"... ... ... ... ... ... ...\n",
"3434 20181116 271.65 272.95 273.52 271.06 74.60M\n",
"3435 20181119 272.31 272.01 274.05 270.83 75.01M\n",
"3436 20181120 269.58 268.54 270.58 268.54 80.90M\n",
"3437 20181121 268.27 265.96 268.49 264.95 81.84M\n",
"3438 20181122 267.76 268.51 269.11 267.15 64.41M\n",
"3439 20181123 266.33 268.12 268.54 264.86 58.35M\n",
"3440 20181126 269.79 266.43 269.95 266.41 75.65M\n",
"3441 20181127 272.08 270.90 272.47 269.27 71.91M\n",
"3442 20181128 273.45 272.64 273.53 271.35 79.83M\n",
"3443 20181129 274.16 277.08 277.54 273.40 80.65M\n",
"3444 20181130 271.35 274.53 275.51 270.88 137.53M\n",
"3445 20181203 276.18 275.70 276.98 273.59 89.07M\n",
"3446 20181204 273.29 275.08 275.72 272.03 80.80M\n",
"3447 20181205 271.49 269.53 272.38 269.47 75.64M\n",
"3448 20181206 267.18 270.18 270.18 266.61 86.09M\n",
"3449 20181207 268.28 268.97 269.66 267.03 68.45M\n",
"3450 20181210 265.35 264.55 266.20 263.63 77.30M\n",
"3451 20181211 264.69 265.87 265.99 263.87 70.04M\n",
"3452 20181212 268.47 266.00 268.84 265.55 83.69M\n",
"3453 20181213 269.41 269.07 271.06 268.22 145.06M\n",
"3454 20181214 265.55 269.69 269.69 264.44 99.73M\n",
"3455 20181217 266.33 265.81 267.19 265.41 71.45M\n",
"3456 20181218 265.18 264.42 266.83 263.93 90.33M\n",
"3457 20181219 267.31 266.02 267.55 265.26 81.75M\n",
"3458 20181220 264.81 265.13 266.34 263.12 81.34M\n",
"3459 20181221 265.06 263.66 265.06 263.15 92.60M\n",
"3460 20181224 264.45 263.51 265.09 262.96 61.96M\n",
"3461 20181226 260.98 261.09 262.49 259.35 92.49M\n",
"3462 20181227 260.57 261.53 261.71 259.73 84.46M\n",
"3463 20181228 261.98 261.61 262.85 261.27 62.70M\n",
"\n",
"[3464 rows x 6 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data = train_data.loc[::-1]\n",
"train_data = train_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>2020년 09월 23일</td>\n",
" <td>309.64</td>\n",
" <td>311.50</td>\n",
" <td>311.76</td>\n",
" <td>303.29</td>\n",
" <td>208050000.0</td>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20050103</td>\n",
" <td>115.36</td>\n",
" <td>115.65</td>\n",
" <td>115.90</td>\n",
" <td>114.99</td>\n",
" <td>101.24M</td>\n",
" <th>183</th>\n",
" <td>2020년 09월 24일</td>\n",
" <td>302.48</td>\n",
" <td>304.85</td>\n",
" <td>306.98</td>\n",
" <td>301.95</td>\n",
" <td>181250000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20050104</td>\n",
" <td>114.77</td>\n",
" <td>115.00</td>\n",
" <td>115.80</td>\n",
" <td>114.50</td>\n",
" <td>119.97M</td>\n",
" <th>184</th>\n",
" <td>2020년 09월 25일</td>\n",
" <td>303.57</td>\n",
" <td>305.38</td>\n",
" <td>305.55</td>\n",
" <td>301.93</td>\n",
" <td>156860000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20050105</td>\n",
" <td>114.65</td>\n",
" <td>113.41</td>\n",
" <td>114.65</td>\n",
" <td>113.17</td>\n",
" <td>142.31M</td>\n",
" <th>185</th>\n",
" <td>2020년 09월 28일</td>\n",
" <td>307.03</td>\n",
" <td>306.49</td>\n",
" <td>308.34</td>\n",
" <td>304.64</td>\n",
" <td>173180000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>2020년 09월 29일</td>\n",
" <td>309.44</td>\n",
" <td>310.01</td>\n",
" <td>311.34</td>\n",
" <td>308.52</td>\n",
" <td>138820000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>2020년 10월 05일</td>\n",
" <td>313.51</td>\n",
" <td>309.65</td>\n",
" <td>314.66</td>\n",
" <td>309.09</td>\n",
" <td>157990000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>2020년 10월 06일</td>\n",
" <td>314.45</td>\n",
" <td>315.13</td>\n",
" <td>316.48</td>\n",
" <td>313.03</td>\n",
" <td>139890000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>2020년 10월 07일</td>\n",
" <td>317.35</td>\n",
" <td>312.34</td>\n",
" <td>317.44</td>\n",
" <td>311.82</td>\n",
" <td>124430000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>2020년 10월 08일</td>\n",
" <td>317.47</td>\n",
" <td>320.31</td>\n",
" <td>320.38</td>\n",
" <td>316.60</td>\n",
" <td>168690000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>191</th>\n",
" <td>2020년 10월 12일</td>\n",
" <td>319.15</td>\n",
" <td>318.99</td>\n",
" <td>320.01</td>\n",
" <td>317.67</td>\n",
" <td>140940000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>2020년 10월 13일</td>\n",
" <td>319.58</td>\n",
" <td>321.04</td>\n",
" <td>321.45</td>\n",
" <td>316.98</td>\n",
" <td>126740000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>2020년 10월 14일</td>\n",
" <td>316.57</td>\n",
" <td>319.60</td>\n",
" <td>319.88</td>\n",
" <td>315.71</td>\n",
" <td>129340000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>2020년 10월 15일</td>\n",
" <td>313.92</td>\n",
" <td>315.76</td>\n",
" <td>315.89</td>\n",
" <td>312.40</td>\n",
" <td>124410000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>2020년 10월 16일</td>\n",
" <td>311.83</td>\n",
" <td>314.41</td>\n",
" <td>315.47</td>\n",
" <td>309.67</td>\n",
" <td>126440000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>2020년 10월 19일</td>\n",
" <td>312.59</td>\n",
" <td>313.43</td>\n",
" <td>314.94</td>\n",
" <td>312.19</td>\n",
" <td>138220000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>2020년 10월 20일</td>\n",
" <td>314.71</td>\n",
" <td>311.02</td>\n",
" <td>314.91</td>\n",
" <td>309.95</td>\n",
" <td>135680000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>2020년 10월 21일</td>\n",
" <td>315.76</td>\n",
" <td>315.34</td>\n",
" <td>316.61</td>\n",
" <td>314.16</td>\n",
" <td>136810000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>199</th>\n",
" <td>2020년 10월 22일</td>\n",
" <td>313.77</td>\n",
" <td>313.62</td>\n",
" <td>314.29</td>\n",
" <td>311.55</td>\n",
" <td>140730000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>2020년 10월 23일</td>\n",
" <td>314.79</td>\n",
" <td>314.98</td>\n",
" <td>315.83</td>\n",
" <td>312.31</td>\n",
" <td>198020000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>2020년 10월 26일</td>\n",
" <td>313.53</td>\n",
" <td>315.63</td>\n",
" <td>316.78</td>\n",
" <td>313.33</td>\n",
" <td>170880000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>2020년 10월 27일</td>\n",
" <td>311.80</td>\n",
" <td>311.50</td>\n",
" <td>313.93</td>\n",
" <td>310.55</td>\n",
" <td>162320000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>2020년 10월 28일</td>\n",
" <td>313.17</td>\n",
" <td>310.66</td>\n",
" <td>313.30</td>\n",
" <td>309.44</td>\n",
" <td>120210000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>2020년 10월 29일</td>\n",
" <td>309.94</td>\n",
" <td>308.99</td>\n",
" <td>310.56</td>\n",
" <td>306.63</td>\n",
" <td>139040000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>2020년 10월 30일</td>\n",
" <td>301.60</td>\n",
" <td>308.91</td>\n",
" <td>309.08</td>\n",
" <td>301.58</td>\n",
" <td>178910000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>2020년 11월 02일</td>\n",
" <td>305.95</td>\n",
" <td>302.67</td>\n",
" <td>306.43</td>\n",
" <td>301.37</td>\n",
" <td>111030000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>207</th>\n",
" <td>2020년 11월 03일</td>\n",
" <td>312.07</td>\n",
" <td>308.14</td>\n",
" <td>312.35</td>\n",
" <td>308.01</td>\n",
" <td>129000000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>208</th>\n",
" <td>2020년 11월 04일</td>\n",
" <td>313.83</td>\n",
" <td>314.31</td>\n",
" <td>315.62</td>\n",
" <td>311.37</td>\n",
" <td>209920.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>209</th>\n",
" <td>2020년 11월 05일</td>\n",
" <td>321.51</td>\n",
" <td>315.74</td>\n",
" <td>321.51</td>\n",
" <td>315.27</td>\n",
" <td>186090000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>2020년 11월 06일</td>\n",
" <td>322.10</td>\n",
" <td>322.72</td>\n",
" <td>324.28</td>\n",
" <td>320.61</td>\n",
" <td>143960000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>211 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20050103 115.36 115.65 115.90 114.99 101.24M\n",
"1 20050104 114.77 115.00 115.80 114.50 119.97M\n",
"2 20050105 114.65 113.41 114.65 113.17 142.31M"
]
},
"execution_count": 60,
"0 2020년 01월 02일 290.35 294.19 294.57 289.96 66160000.0\n",
"1 2020년 01월 03일 290.74 293.10 294.88 289.04 79770000.0\n",
"2 2020년 01월 06일 288.43 287.73 289.41 287.49 69960000.0\n",
"3 2020년 01월 07일 291.23 290.01 292.26 289.64 65350000.0\n",
"4 2020년 01월 08일 289.42 288.94 291.01 287.30 124530000.0\n",
"5 2020년 01월 09일 294.41 293.83 294.41 292.10 111790000.0\n",
"6 2020년 01월 10일 297.06 294.80 297.17 294.17 94160000.0\n",
"7 2020년 01월 13일 300.13 296.62 300.31 296.53 81680000.0\n",
"8 2020년 01월 14일 301.53 302.20 303.61 300.69 92120000.0\n",
"9 2020년 01월 15일 299.74 299.91 301.13 298.82 71770000.0\n",
"10 2020년 01월 16일 302.78 299.93 302.91 298.95 85710000.0\n",
"11 2020년 01월 17일 303.30 305.07 306.05 302.51 81540000.0\n",
"12 2020년 01월 20일 305.58 304.99 307.54 304.77 78040000.0\n",
"13 2020년 01월 21일 302.11 304.94 306.09 301.85 67630000.0\n",
"14 2020년 01월 22일 306.08 301.79 306.52 301.16 79330000.0\n",
"15 2020년 01월 23일 302.33 303.77 304.72 301.71 86910000.0\n",
"16 2020년 01월 28일 292.77 294.98 296.30 291.30 130170000.0\n",
"17 2020년 01월 29일 293.98 294.38 295.67 292.45 85730000.0\n",
"18 2020년 01월 30일 288.37 293.27 294.11 287.09 101540000.0\n",
"19 2020년 01월 31일 284.53 290.24 291.47 284.53 101460000.0\n",
"20 2020년 02월 03일 285.05 280.17 286.24 279.78 111060000.0\n",
"21 2020년 02월 04일 290.68 285.19 291.38 285.11 105450000.0\n",
"22 2020년 02월 05일 292.02 293.57 294.26 290.30 102640000.0\n",
"23 2020년 02월 06일 300.65 294.74 300.98 294.32 110220000.0\n",
"24 2020년 02월 07일 298.21 299.59 300.05 296.01 108110000.0\n",
"25 2020년 02월 10일 296.24 294.07 296.73 293.33 72220000.0\n",
"26 2020년 02월 11일 299.28 297.91 301.10 297.48 78350000.0\n",
"27 2020년 02월 12일 301.54 299.83 302.20 298.09 110630000.0\n",
"28 2020년 02월 13일 300.93 302.79 304.43 300.48 115070000.0\n",
"29 2020년 02월 14일 303.01 300.95 304.20 299.18 80530000.0\n",
".. ... ... ... ... ... ...\n",
"181 2020년 09월 22일 308.82 315.53 316.02 307.40 304360000.0\n",
"182 2020년 09월 23일 309.64 311.50 311.76 303.29 208050000.0\n",
"183 2020년 09월 24일 302.48 304.85 306.98 301.95 181250000.0\n",
"184 2020년 09월 25일 303.57 305.38 305.55 301.93 156860000.0\n",
"185 2020년 09월 28일 307.03 306.49 308.34 304.64 173180000.0\n",
"186 2020년 09월 29일 309.44 310.01 311.34 308.52 138820000.0\n",
"187 2020년 10월 05일 313.51 309.65 314.66 309.09 157990000.0\n",
"188 2020년 10월 06일 314.45 315.13 316.48 313.03 139890000.0\n",
"189 2020년 10월 07일 317.35 312.34 317.44 311.82 124430000.0\n",
"190 2020년 10월 08일 317.47 320.31 320.38 316.60 168690000.0\n",
"191 2020년 10월 12일 319.15 318.99 320.01 317.67 140940000.0\n",
"192 2020년 10월 13일 319.58 321.04 321.45 316.98 126740000.0\n",
"193 2020년 10월 14일 316.57 319.60 319.88 315.71 129340000.0\n",
"194 2020년 10월 15일 313.92 315.76 315.89 312.40 124410000.0\n",
"195 2020년 10월 16일 311.83 314.41 315.47 309.67 126440000.0\n",
"196 2020년 10월 19일 312.59 313.43 314.94 312.19 138220000.0\n",
"197 2020년 10월 20일 314.71 311.02 314.91 309.95 135680000.0\n",
"198 2020년 10월 21일 315.76 315.34 316.61 314.16 136810000.0\n",
"199 2020년 10월 22일 313.77 313.62 314.29 311.55 140730000.0\n",
"200 2020년 10월 23일 314.79 314.98 315.83 312.31 198020000.0\n",
"201 2020년 10월 26일 313.53 315.63 316.78 313.33 170880000.0\n",
"202 2020년 10월 27일 311.80 311.50 313.93 310.55 162320000.0\n",
"203 2020년 10월 28일 313.17 310.66 313.30 309.44 120210000.0\n",
"204 2020년 10월 29일 309.94 308.99 310.56 306.63 139040000.0\n",
"205 2020년 10월 30일 301.60 308.91 309.08 301.58 178910000.0\n",
"206 2020년 11월 02일 305.95 302.67 306.43 301.37 111030000.0\n",
"207 2020년 11월 03일 312.07 308.14 312.35 308.01 129000000.0\n",
"208 2020년 11월 04일 313.83 314.31 315.62 311.37 209920.0\n",
"209 2020년 11월 05일 321.51 315.74 321.51 315.27 186090000.0\n",
"210 2020년 11월 06일 322.10 322.72 324.28 320.61 143960000.0\n",
"\n",
"[211 rows x 6 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### test_data"
"test_data = test_data.loc[::-1]\n",
"test_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_data = test_data.loc[::-1]\n",
"test_data.reset_index(drop=True)"
]
"source": []
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": 19,
"metadata": {},
"outputs": [
{
......@@ -1240,30 +1075,30 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20190102</td>\n",
" <td>258.23</td>\n",
" <td>263.35</td>\n",
" <td>263.77</td>\n",
" <td>257.47</td>\n",
" <td>69.95M</td>\n",
" <td>2020년 01월 02일</td>\n",
" <td>290.35</td>\n",
" <td>294.19</td>\n",
" <td>294.57</td>\n",
" <td>289.96</td>\n",
" <td>66160000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20190103</td>\n",
" <td>255.77</td>\n",
" <td>258.19</td>\n",
" <td>258.70</td>\n",
" <td>255.35</td>\n",
" <td>90.52M</td>\n",
" <td>2020년 01월 03일</td>\n",
" <td>290.74</td>\n",
" <td>293.10</td>\n",
" <td>294.88</td>\n",
" <td>289.04</td>\n",
" <td>79770000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20190104</td>\n",
" <td>257.89</td>\n",
" <td>255.62</td>\n",
" <td>258.12</td>\n",
" <td>254.55</td>\n",
" <td>91.20M</td>\n",
" <td>2020년 01월 06일</td>\n",
" <td>288.43</td>\n",
" <td>287.73</td>\n",
" <td>289.41</td>\n",
" <td>287.49</td>\n",
" <td>69960000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -1271,12 +1106,12 @@
],
"text/plain": [
" date close open high low vol\n",
"0 20190102 258.23 263.35 263.77 257.47 69.95M\n",
"1 20190103 255.77 258.19 258.70 255.35 90.52M\n",
"2 20190104 257.89 255.62 258.12 254.55 91.20M"
"0 2020년 01월 02일 290.35 294.19 294.57 289.96 66160000.0\n",
"1 2020년 01월 03일 290.74 293.10 294.88 289.04 79770000.0\n",
"2 2020년 01월 06일 288.43 287.73 289.41 287.49 69960000.0"
]
},
"execution_count": 49,
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1294,7 +1129,7 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1304,7 +1139,7 @@
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 20,
"metadata": {},
"outputs": [
{
......@@ -1339,30 +1174,30 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20200102</td>\n",
" <td>290.35</td>\n",
" <td>294.19</td>\n",
" <td>294.57</td>\n",
" <td>289.96</td>\n",
" <td>66.16M</td>\n",
" <td>2019년 01월 02일</td>\n",
" <td>258.23</td>\n",
" <td>263.35</td>\n",
" <td>263.77</td>\n",
" <td>257.47</td>\n",
" <td>69950000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20200103</td>\n",
" <td>290.74</td>\n",
" <td>293.10</td>\n",
" <td>294.88</td>\n",
" <td>289.04</td>\n",
" <td>79.77M</td>\n",
" <td>2019년 01월 03일</td>\n",
" <td>255.77</td>\n",
" <td>258.19</td>\n",
" <td>258.70</td>\n",
" <td>255.35</td>\n",
" <td>90520000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20200106</td>\n",
" <td>288.43</td>\n",
" <td>287.73</td>\n",
" <td>289.41</td>\n",
" <td>287.49</td>\n",
" <td>69.96M</td>\n",
" <td>2019년 01월 04일</td>\n",
" <td>257.89</td>\n",
" <td>255.62</td>\n",
" <td>258.12</td>\n",
" <td>254.55</td>\n",
" <td>91200000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -1370,12 +1205,12 @@
],
"text/plain": [
" date close open high low vol\n",
"0 20200102 290.35 294.19 294.57 289.96 66.16M\n",
"1 20200103 290.74 293.10 294.88 289.04 79.77M\n",
"2 20200106 288.43 287.73 289.41 287.49 69.96M"
"0 2019년 01월 02일 258.23 263.35 263.77 257.47 69950000.0\n",
"1 2019년 01월 03일 255.77 258.19 258.70 255.35 90520000.0\n",
"2 2019년 01월 04일 257.89 255.62 258.12 254.55 91200000.0"
]
},
"execution_count": 50,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1400,7 +1235,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1416,86 +1251,27 @@
},
{
"cell_type": "code",
"execution_count": 77,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/3464 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
" 42%|████▏ | 1445/3464 [12:44<17:43, 1.90it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 3464/3464 [30:22<00:00, 1.85it/s]\n"
]
}
],
"outputs": [],
"source": [
"vol_char_to_num(train_data)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/246 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 246/246 [02:14<00:00, 1.95it/s]\n"
]
}
],
"outputs": [],
"source": [
"vol_char_to_num(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/187 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 187/187 [01:37<00:00, 1.95it/s]\n"
]
}
],
"outputs": [],
"source": [
"vol_char_to_num(validation_data)"
]
......@@ -1509,7 +1285,7 @@
},
{
"cell_type": "code",
"execution_count": 79,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1518,43 +1294,22 @@
},
{
"cell_type": "code",
"execution_count": 81,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test.csv\")"
"test_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'validation_data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-42-84d885a6a29a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'validation_data' is not defined"
]
}
],
"source": [
"validation_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"validation_data = pd.read_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
"validation_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
]
},
{
......@@ -1567,7 +1322,7 @@
},
{
"cell_type": "code",
"execution_count": 111,
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
......@@ -1578,11 +1333,10 @@
},
{
"cell_type": "code",
"execution_count": 94,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"##----train_data----##\n",
"train_data_scaled = scaler.fit_transform(train_data[scale_cols])\n",
"train_data_scaled = pd.DataFrame(train_data_scaled)\n",
......@@ -1593,7 +1347,7 @@
},
{
"cell_type": "code",
"execution_count": 104,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1605,7 +1359,71 @@
},
{
"cell_type": "code",
"execution_count": 105,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_data_scaled.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_data_scaled = scaler.fit_transform(validation_data[scale_cols])\n",
"val_data_scaled = pd.DataFrame(val_data_scaled)\n",
"val_data_scaled.columns = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_scaled.csv\")\n",
"test_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_scaled.csv\")\n",
"val_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_scaled.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"val_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/validation_scaled.csv\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 이동평균 데이터 \n",
"\n",
"- 단순이동평균\n",
"- 지수이동평균\n",
"\n",
"종가이동평균 : ma, 거래량이동평균 : vma\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv')\n",
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
......@@ -1629,6 +1447,7 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
......@@ -1639,93 +1458,58 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.143847</td>\n",
" <td>0.277840</td>\n",
" <td>0.242639</td>\n",
" <td>0.165408</td>\n",
" <td>0.207573</td>\n",
" <td>2020년 01월 02일</td>\n",
" <td>290.35</td>\n",
" <td>294.19</td>\n",
" <td>294.57</td>\n",
" <td>289.96</td>\n",
" <td>66160000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.087047</td>\n",
" <td>0.163147</td>\n",
" <td>0.126912</td>\n",
" <td>0.116907</td>\n",
" <td>0.381423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.135996</td>\n",
" <td>0.106024</td>\n",
" <td>0.113673</td>\n",
" <td>0.098604</td>\n",
" <td>0.387170</td>\n",
" <td>2020년 01월 03일</td>\n",
" <td>290.74</td>\n",
" <td>293.10</td>\n",
" <td>294.88</td>\n",
" <td>289.04</td>\n",
" <td>79770000.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" close open high low vol\n",
"0 0.143847 0.277840 0.242639 0.165408 0.207573\n",
"1 0.087047 0.163147 0.126912 0.116907 0.381423\n",
"2 0.135996 0.106024 0.113673 0.098604 0.387170"
" date close open high low vol\n",
"0 2020년 01월 02일 290.35 294.19 294.57 289.96 66160000.0\n",
"1 2020년 01월 03일 290.74 293.10 294.88 289.04 79770000.0"
]
},
"execution_count": 105,
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data_scaled.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_data_scaled = scaler.fit_transform(validation_data[scale_cols])\n",
"val_data_scaled = pd.DataFrame(val_data_scaled)\n",
"val_data_scaled.columns = ['close','open','high','low','vol']"
"test.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"train_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_scaled.csv\")\n",
"test_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_scaled.csv\")\n",
"val_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_scaled.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 이동평균 데이터 \n",
"\n",
"- 단순이동평균\n",
"- 지수이동평균\n",
"\n",
"종가이동평균 : ma, 거래량이동평균 : vma\n"
"del train['Unnamed: 0']\n",
"del test['Unnamed: 0']\n",
"del validation['Unnamed: 0']"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv')\n",
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')"
]
"source": []
},
{
"cell_type": "markdown",
......@@ -1743,7 +1527,7 @@
},
{
"cell_type": "code",
"execution_count": 99,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1760,7 +1544,7 @@
},
{
"cell_type": "code",
"execution_count": 100,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
......@@ -1777,7 +1561,7 @@
},
{
"cell_type": "code",
"execution_count": 101,
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
......@@ -1801,7 +1585,7 @@
},
{
"cell_type": "code",
"execution_count": 102,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1818,7 +1602,7 @@
},
{
"cell_type": "code",
"execution_count": 103,
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
......@@ -1835,7 +1619,7 @@
},
{
"cell_type": "code",
"execution_count": 104,
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
......@@ -1867,7 +1651,7 @@
},
{
"cell_type": "code",
"execution_count": 105,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1884,7 +1668,7 @@
},
{
"cell_type": "code",
"execution_count": 106,
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
......@@ -1901,7 +1685,7 @@
},
{
"cell_type": "code",
"execution_count": 107,
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
......@@ -1925,7 +1709,7 @@
},
{
"cell_type": "code",
"execution_count": 108,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -1942,7 +1726,7 @@
},
{
"cell_type": "code",
"execution_count": 109,
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
......@@ -1959,7 +1743,7 @@
},
{
"cell_type": "code",
"execution_count": 110,
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
......@@ -1990,7 +1774,7 @@
},
{
"cell_type": "code",
"execution_count": 113,
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
......@@ -1999,7 +1783,7 @@
},
{
"cell_type": "code",
"execution_count": 143,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -2026,6 +1810,28 @@
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"\n",
"##----test_data----##\n",
"test_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/test_SMA.csv')\n",
"\n",
"test_SMA_scaled = scaler.fit_transform(test_SMA[scale_cols2])\n",
"test_SMA_scaled = pd.DataFrame(test_SMA_scaled)\n",
"test_SMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/val_SMA.csv')\n",
"\n",
"val_SMA_scaled = scaler.fit_transform(val_SMA[scale_cols2])\n",
"val_SMA_scaled = pd.DataFrame(val_SMA_scaled)\n",
"val_SMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
......@@ -2034,7 +1840,7 @@
},
{
"cell_type": "code",
"execution_count": 144,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -2062,7 +1868,28 @@
},
{
"cell_type": "code",
"execution_count": 145,
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/test_SVMA.csv')\n",
"\n",
"test_SVMA_scaled = scaler.fit_transform(test_SVMA[scale_cols2])\n",
"test_SVMA_scaled = pd.DataFrame(test_SVMA_scaled)\n",
"test_SVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/val_SVMA.csv')\n",
"\n",
"val_SVMA_scaled = scaler.fit_transform(val_SVMA[scale_cols2])\n",
"val_SVMA_scaled = pd.DataFrame(val_SVMA_scaled)\n",
"val_SVMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -2075,6 +1902,18 @@
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"test_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_SMA_scaled.csv\")\n",
"val_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_SMA_scaled.csv\")\n",
"test_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_SVMA_scaled.csv\")\n",
"val_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_SVMA_scaled.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
......@@ -2083,7 +1922,7 @@
},
{
"cell_type": "code",
"execution_count": 146,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -2110,6 +1949,27 @@
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv')\n",
"\n",
"test_EMA_scaled = scaler.fit_transform(test_EMA[scale_cols2])\n",
"test_EMA_scaled = pd.DataFrame(test_EMA_scaled)\n",
"test_EMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv')\n",
"\n",
"val_EMA_scaled = scaler.fit_transform(val_EMA[scale_cols2])\n",
"val_EMA_scaled = pd.DataFrame(val_EMA_scaled)\n",
"val_EMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
......@@ -2118,7 +1978,7 @@
},
{
"cell_type": "code",
"execution_count": 147,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -2140,13 +2000,49 @@
"val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
"\n",
"val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
"val_EVMA_scaled = pd.DataFrame(val_SVMA_scaled)\n",
"val_EVMA_scaled = pd.DataFrame(val_EVMA_scaled)\n",
"val_EVMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv')\n",
"\n",
"test_EVMA_scaled = scaler.fit_transform(test_EVMA[scale_cols2])\n",
"test_EVMA_scaled = pd.DataFrame(test_EVMA_scaled)\n",
"test_EVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
"\n",
"val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
"val_EVMA_scaled = pd.DataFrame(val_EVMA_scaled)\n",
"val_EVMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 148,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
"\n",
"val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
"val_EVMA_scaled = pd.DataFrame(val_EVMA_scaled)\n",
"val_EVMA_scaled.columns = ['5','10','20','60','120']\n",
"val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EVMA_scaled.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -2157,6 +2053,25 @@
"test_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EVMA_scaled.csv\")\n",
"val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EVMA_scaled.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"test_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EMA_scaled.csv\")\n",
"val_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EMA_scaled.csv\")\n",
"test_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EVMA_scaled.csv\")\n",
"val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EVMA_scaled.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
......
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KOSPI 200 지수 예측 프로젝트\n",
"\n",
"---\n",
"\n",
"🏝[중간보고서링크](http://khuhub.khu.ac.kr/2020-2-capstone-design2/2016103208/tree/master/%E1%84%87%E1%85%A9%E1%84%80%E1%85%A9%E1%84%89%E1%85%A5)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LSTM 모델\n",
"\n",
"일반신경망에 시계열 개념이 추가되어 은닉계층에 이전 정보를 기억시킬 수 있는 순환신경망이 시계열 데이터인 주가 예측 모델로 적합<br>\n",
"1. 단방향 LSTM 순환신경망\n",
" - 입력 순서를 시간 순서대로 처리하기 때문에 결과물은 주로 직전 패턴을 기반으로 하는 경향 (한계점)\n",
"2. 양방향 LSTM 순환신경망\n",
" - 데이터의 흐름에 역방향에 은닉계층이 추가되는 양방향 LSTM 순환신경망\n",
"3. 성능 측정 : 실제 주가와 예측된 주가 간의 평균 제곱근 오차(RMSE)\n",
"---\n",
"\n",
"**2주차 목표 : 단방향 LSTM 과 양방향 LSTM의 성능(RMSE)과 예측률 비교**\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
]
}
],
"source": [
"from keras.models import Model, Sequential\n",
"from keras.layers import Input, Dense, LSTM, Bidirectional\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"from keras import backend as K\n",
"import matplotlib.pyplot as plt\n",
"from keras.layers.core import Dense, Activation, Dropout"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 단방향(Unidirectional) LSTM\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (1) 데이터 불러오기\n",
"### case1. Simple Price Data (시가, 종가, 고가, 저가, 거래량)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/train_scaled.csv')\n",
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/test_scaled.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/scaled/validation_scaled.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# 불필요한 열 삭제\n",
"del train['Unnamed: 0']\n",
"del test['Unnamed: 0']\n",
"del validation['Unnamed: 0']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking if any null values are present in train\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n",
"checking if any null values are present in test\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n",
"checking if any null values are present in validation\n",
" close 0\n",
"open 0\n",
"high 0\n",
"low 0\n",
"vol 0\n",
"dtype: int64\n"
]
}
],
"source": [
"# NULL value 없나 확인해보기\n",
"print(\"checking if any null values are present in train\\n\", train.isna().sum())\n",
"print(\"checking if any null values are present in test\\n\", test.isna().sum())\n",
"print(\"checking if any null values are present in validation\\n\", validation.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.011719</td>\n",
" <td>0.011665</td>\n",
" <td>0.011228</td>\n",
" <td>0.012979</td>\n",
" <td>0.290444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" close open high low vol\n",
"0 0.011719 0.011665 0.011228 0.012979 0.290444"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"#### Keras RNN 계열의 모델을 트레이닝할 때 요구되는 데이터의 형식 : 3차원\n",
"3차원 데이터 : **(size, timestep, feature)** <br>\n",
"일반적인 MLP모델에서는 size, feature만 있는 2차원 모델 <br>\n",
"RNN계열은 '시간'이라는 개념이 있기 때문에 한 차원 늘어난다. -> **timestep**\n",
"\n",
"---\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"def make_dataset(data, label, window_size = 20):\n",
" feature_list = []\n",
" label_list = []\n",
" for i in range(len(data)-window_size):\n",
" feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
" label_list.append(np.array(label.iloc[i+window_size]))\n",
" return np.array(feature_list), np.array(label_list) "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# feature , label 분리\n",
"feature_cols = ['open','high','low','vol']\n",
"label_cols = ['close']\n",
"\n",
"##----train_data----##\n",
"train_feature = train[feature_cols]\n",
"train_label = train[label_cols]\n",
"train_feature, train_label = make_dataset(train_feature,train_label,20)\n",
"\n",
"##----test_data----##\n",
"test_feature = test[feature_cols]\n",
"test_label = test[label_cols]\n",
"test_feature, test_label = make_dataset(test_feature,test_label,20)\n",
"\n",
"##----validation_data----##\n",
"validation_feature = validation[feature_cols]\n",
"validation_label = validation[label_cols]\n",
"validation_feature, validation_label = make_dataset(validation_feature,validation_label,20)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"X_train, y_train, X_val, y_val = train_feature, train_label, validation_feature, validation_label"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"X_test, y_test = test_feature, test_label"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(167, 20, 4)\n"
]
}
],
"source": [
"print(X_val.shape)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(167, 1)\n"
]
}
],
"source": [
"print(y_val.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (2) 모델 만들기"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm_13 (LSTM) (None, 20, 64) 17664 \n",
"_________________________________________________________________\n",
"lstm_14 (LSTM) (None, 50) 23000 \n",
"_________________________________________________________________\n",
"dense_7 (Dense) (None, 1) 51 \n",
"=================================================================\n",
"Total params: 40,715\n",
"Trainable params: 40,715\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"#keras 활용한 LSTM 모델 생성\n",
"\n",
"model = Sequential() \n",
"model.add(LSTM(64, return_sequences=True, input_shape=(20, 4))) #첫번째 LSTM 은 유닛수 50개\n",
"model.add(LSTM(50, return_sequences=False)) #두번째 LSTM 유닛수는 64개\n",
"model.add(Dense(1, activation='linear')) # 아웃풋으로 나오는 값은 1개 (다음날 하루 예측)\n",
"model.compile(loss='mse', optimizer='adam') #손실 함수 ,optimizer= rmsprop\n",
"model.summary() #모델의 개요"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 3444 samples, validate on 167 samples\n",
"Epoch 1/20\n",
"3444/3444 [==============================] - 30s 9ms/step - loss: 0.0061 - val_loss: 0.0096\n",
"Epoch 2/20\n",
"3444/3444 [==============================] - 27s 8ms/step - loss: 7.1951e-04 - val_loss: 0.0086\n",
"Epoch 3/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 6.2036e-04 - val_loss: 0.0069\n",
"Epoch 4/20\n",
"3444/3444 [==============================] - 26s 8ms/step - loss: 4.9895e-04 - val_loss: 0.0053\n",
"Epoch 5/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 4.0447e-04 - val_loss: 0.0050\n",
"Epoch 6/20\n",
"3444/3444 [==============================] - 26s 8ms/step - loss: 3.6695e-04 - val_loss: 0.0040\n",
"Epoch 7/20\n",
"3444/3444 [==============================] - 28s 8ms/step - loss: 3.1896e-04 - val_loss: 0.0033\n",
"Epoch 8/20\n",
"3444/3444 [==============================] - 27s 8ms/step - loss: 2.9615e-04 - val_loss: 0.0030\n",
"Epoch 9/20\n",
"3444/3444 [==============================] - 24s 7ms/step - loss: 2.6400e-04 - val_loss: 0.0030\n",
"Epoch 10/20\n",
"3444/3444 [==============================] - 24s 7ms/step - loss: 2.3818e-04 - val_loss: 0.0026\n",
"Epoch 11/20\n",
"3444/3444 [==============================] - 24s 7ms/step - loss: 2.3996e-04 - val_loss: 0.0026\n",
"Epoch 12/20\n",
"3444/3444 [==============================] - 31s 9ms/step - loss: 2.1847e-04 - val_loss: 0.0023\n",
"Epoch 13/20\n",
"3444/3444 [==============================] - 27s 8ms/step - loss: 2.1816e-04 - val_loss: 0.0024\n",
"Epoch 14/20\n",
"3444/3444 [==============================] - 28s 8ms/step - loss: 2.4145e-04 - val_loss: 0.0024\n",
"Epoch 15/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 2.1806e-04 - val_loss: 0.0028\n",
"Epoch 16/20\n",
"3444/3444 [==============================] - 25s 7ms/step - loss: 2.0134e-04 - val_loss: 0.0022\n",
"Epoch 17/20\n",
"3444/3444 [==============================] - 28s 8ms/step - loss: 2.1454e-04 - val_loss: 0.0022\n",
"Epoch 18/20\n",
"3444/3444 [==============================] - 29s 8ms/step - loss: 2.2625e-04 - val_loss: 0.0025\n",
"Epoch 19/20\n",
"3444/3444 [==============================] - 29s 8ms/step - loss: 2.1269e-04 - val_loss: 0.0021\n",
"Epoch 20/20\n",
"2330/3444 [===================>..........] - ETA: 8s - loss: 2.0376e-04"
]
}
],
"source": [
"model.fit(X_train, y_train,\n",
" validation_data=(X_val, y_val),\n",
" batch_size=10,\n",
" epochs=3) "
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"226/226 [==============================] - 0s 1ms/step\n"
]
}
],
"source": [
"score = model.evaluate(X_test, y_test, batch_size = 32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(score)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"predict = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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hHjQeAkApWMyMnDSAfhPvJiIJjIUQQgghBF5/ALc3oL9dW/Mx7TZvPmXT0rBZTCwotI3cAkdBAkUkQgghhBBisnKGx0HrDA/bqsBgAnsJ/3RJLrecNUP/YJBxSgJjIYQQQggRHgetu5Si/RRkloDRxLwCG/MKJna2GKSUQgghhBBCAJ1uL5Bgxji7bMTWMxYkMBZCCCGEEL0ZY701xm2nIGvmCK5o9ElgLIQQQgghcHoSKKXwOMHVLBljIYQQQggx+TgSKaVoP6XdZkvGWAghhBBCTDKORLpStFVpt5IxFkIIIYQQk02olCJDV2AczBhnlY3cgsaABMZCCCGEEIJOt5cUkwGLyRj/4LYqSLFB2sSedDeQBMZCCCGEEAKH24ddb6u29lNafbEysQd6DCSBsRBCCCGEwOH26R/uMQl7GIMExkIIIYQQAnC6vWTo6WGsqpOyhzFIYCyEEEIIIQhljHUExs5G8HVLxlgIIYQQQkxOugPjSdrDGCQwFkIIIYQQaO3aMiw6aownaQ9jkMBYCCGEEEKgtWtLaLhH1owRXc9YkMBYCCGEEGKKCwRUnB6d7draTkFGIZhTR35ho0wCYyGEEEKIKa6rx4eqoq9dW1vVpKwvBgmMhRBCCCGmvITGQbefmpT1xSCBsRBCCCHElOdwa4Fx3BpjXw90np6UPYxBAmMhhBBCiCnP4fYCOkopHGdADUBW6SisavRJYCyEEEIIMcV16s0Yd9Zpt/bpI7yisSGBsRCAqqp87qnt/OaDqrFeihBCCDHqnKHAON5I6M7T2q29eIRXNDZ0VFgLMfmdbu/m1QMNvHqggVSzkU+sjnCJyNEAJgukZo3+AoUQQogR1FtjHKeUYpIHxpIxFgLYXdMOwNz8DL7x5328dqC+/wE9LvjlenhoBez/E6jqGKxSCCGEGBm9NcY6SilSbGC1j8KqRp8ExkIAu6vbSTEZ+NO957GkOJMvPruL2jZX7wHbHwNnPaTnwR8/A3+4HTyOsVuwEEIIkUROjw+DAmkpxtgHdtRC5uTMFoMExkIAWsZ4yXQ7mWlmvvORcnp8AY43OrUHe7rg/Qdg9kVw32bY8O9w6K+w6aGxXbQQQgiRJA63jwyLCUVRYh/YWTdpN96BBMZC4PUH2He6g+Wl2QBkpmr1VaF6K7b9ClzNcNG3wGiCdf8M8y6DHb8Gn2esli2EEEIMy7bKVn7y2hECAZVOt1ff1DsJjIWY3I7UO/D4AiyfoW2qC70wONw+rVxi04Mw9xIoPav3SWd/Drqa4MDzY7FkIYQQYtj+svs0D711nF9uPInD7dM33MPZAPaS0VngGJDAWEx5u4Ib71aUhgJj7YXB4fbCh49Bdytc+K3+T5p9EeTOg62PyEY8IYQQE1KoRdv/vnaEPTXt8QNjZz2gSsZYiMlsd3U7uekplGSnAtrGA6NBwdHthZ1PwszzoWRV/ycZDFrWuG4X1G4fg1ULIYQQw+P0+Jg9LZ2ZOWk0Ojw6WrUFh3vI5jshJq/dNW0sL80KbzhQFIUMi4mslp3QehJW3Br5ictu1lrWbPvlKK5WCCGESA6n20eezcLDt67EYjKQk54S+wkdtdrtJO1hDBIYiymuo9vLiaYulpf2H9phs5pY3PQ3MKdD+bWRn2yxwYrb4MBfwNk0CqsVQgghksfh0eqKy4vsvPjF8/na5QtiP2GSj4MGCYzFFLcnWF8c2ngXMs3iZ3nH27D4o2DJiH6CVXdAwAv7nhvJZQohhBBJ5/R4yQiOgF5QaCPfbo39hPBwj8xRWN3YkMBYTGmhiXcVJf0D4w1sI1V1wfJPxj5BfjlMXwm7nxmpJQohhBAjwun2kRFvw11fnbWTOlsMEhiLKW7HqTbm5meEexeHbHC/wRlDAcw4L/5Jln8SGvbBmb0jtEohhBAi+ZweHxkWHb2LQyZ5D2OQwFhMYT2+ANsqWzlvTm7/B9prWOjexcuGi7TuE/Es+TgYUyRrLIQQYsLw+Px4/Wr8Fm19ddZN6o4UIIGxmMJ217TT7fVz3pxp/R84+AIGVP7sW6vvRGk5sOAq2PcHrfm5EEIIMc6FehiHaozj8nvBUT+pO1KABMbjzskmJ/tPd4z1MqaE9483Y1Dg3NkDMsaH/0Zj2jwOeXJR9Q7vWH4ruFrg2GvJX6gQQgiRZE5PgoGxIzTcQwJjMUo+rGrl2p9t4tNPfKg/IBNDtul4M0uLM8lM61Nf5WyE6i1U519MQIWuHr++k825GDIKYNdTI7NYIYQQIokcoYyx3lKKcKs2CYzFKPjgRAt3PL6NHn+AJoeHyuausV7SpOZwe9ld087auQPKKA6/BKg0Fl8aPk4XowlW3g5HX4Xm48ldrBBCCJFkoYyxTW/GuPO0diub70Q8Hp9ffwAVwYdVrXz6iW0UZ6Xy5KfPAmBbZWv48WMNDs79rzepbnH1Psnvg19tgPuXwuv/BvX7hvz9p6Jtla34AyrnDwyMD/0VsmcRyF8E9H6i1uWse8Bohi0PJ3GlQgghRPKFaozTEw2MZfOdiOfBN47x8V9sHtJz69q7ue+3O5iemcrv7jmHc2bnkJuewraq3sD4hd11nOlwc6zR0fvE7Y/D6e1gK4DNP4NHzoejUt+q16bjLVhMBlbOzO69s7sdKjdC+dXYUrWxmAl94MnI18ZE734GupqTvGIhhBAiecI1xomUUqRkgMU+gqsaexIYJ0FVSxc1rd0JP8/t9XPPU9vxeAM8evtqcjMsKIrCmrKcfhnjNw41AL2/xHQ1w9s/hNkXwmdfh68cA2sWHHohCT/N1LDpeDNrynKwmo29dx57TZtiV35tuH1NZyIZY4Bzvwg+N3z4WBJXK4QQQiSXYyilFPZiUJQRXNXYS6B5nYim3eWl2+vH6w9gNsb+rFHZ3MX2qlbcvgDvHG7kQF0n/3f7aubm944dPmtWDq8cqKeuvRt/QOVwvZYpDgfGb34Perrgyv/WfkHTc6HsfC3bKeJqdLg50uDguhUD6qQO/RUyCqF4NfZgjXdCpRQAeQtg3uWw7VFY+yUwpyZp1UIIIUTyOIey+c5eNIIrGh8kME6CNpd2ub3L4yMrLSXmsV/74x4+rGoDtJj2a5cvZEN5Qb9jzpqVA2i1xy3O3r64TrcPTu+EnU/BuV/QgrCQWRfA4b9BWxVklw3/h5rENh9vAWBt3/7F3m44/gYsuwUMBmxWrVNFZ/cQasfP+wd48mrY90dY+alkLFkIIYRIqi6PD4MCqX2vnMbibNA3DXaCk8A4CTpcWvDqcMcPjBsdHi5fXMAPP7qUtBRjxKL38iI7GRYT2ypbOdnUxdz8DE40OXG6vfDqtyE9Dy74ev8nzVqv3VZulMA4jo1Hm8hOM7OkOLP3zhNvgdcF5VcDhEspEs4Yg5a9zyyFY69KYCyEEGJc0sZBm1D0lEaoKjgatL00k5zUGCdBKGOsJ4hq7eqhKDOVPJsl6k5Qo0Fh1cxs3jnSxLaqVi5bVEBGionpje9A9Wa46JtgHVD8nrcA0vOlnCKOQEBl47Em1s3Lw2jo82Jw6G9gzYSydYD2CdpoUIbWbURRtAx+5XsQ0NkHWQghhBhFDrcvfHU0Lnc7+D1gKxzZRY0DEhgPk9vrp9urBT/xgiivP4DD7SMnPXZWGbRyitPBGuNLFxWQaVG4uOZhyJ0HK24f/ARF0bLGlRu1T3YiooNnOml29nDB/LzeO/1eOPIyzL9Sa7cGKIqCzWoaWsYYYPYF2gtJ/d4krFoIIYRILqfHq3/qnbNRu80oiH3cJCCB8TB19KlBDW+Oi6I9mFnOTov/Ce3sYJ3xtAwLy0qy+LjhbQp6quHS72nDJCKZtV6rAWo+pnP1k9+OU63srW0Pf/3u0SYA1s3vU198apMWxAbLKELsVvPQ+1OHSltOvjO05wshhBAjyOnx6d9456jXbiUwFvGEgl2IX0rRFqxFztaRMV5akkl6ipHLFhdg8Lm4o+dZjqQsgQVXRX9SuM743fgLnyL+5Q97uOvJ7XQHRzu/e6SJxdPt5NusvQcd+iuYUmHOhn7PHVbG2FYIeQvhpPxdCCGEGH+cbp/+4R6hjLGUUoh42l29XSMccTLGbV3BwDjOBj0Ai8nIC19cyzeuXAg7niAn0Mav0++M3T8wuwwyZ0idcVCL00NVi4tGh4dfb66k0+1lZ3Vb/zKKQEAbAz13A6Sk9Xv+sAJj0OqMq7eAzzP0cwghhBAjwOHx6e9h7JSMsdCprV/GOPZl93DGWEdgDDA334bdpMLmn3EkdTk7/PNjPyFUZ1z1nhbwTXG7qrUSipLsVH7xzgn+vu8MvoDaPzA+vQMcZ6D82kHPt1nNdA5j1DezLwBfN9RsG/o5hBBCiBHgdPv01xg76rUrqxbbyC5qHJDAeJg6uvtkjONkF1u7gjXG6Tp3gQLs+wM46tiYf1vcGmYAZp4L3W3QelL/95ikdtW0YTIo/OyTK3F6fHz3xYNkWEz9x0AfehEMJph/2aDnDztjPHMtKAYpbRFCCDHuJFRj7GwEW8Gkn3oHEhgPWyhjbDEZwlNkoh+bWMaYQADefwAKKzidc27c8wNQsES7bdiv73tMYjtPtVNeZGd5aRYfW15Mt9fP2rm5vdMJ/V7Y+weYczGkZg96vn24GePULJi+QuqMhRBCjCv+gIqrx59AV4r6KVFGARIYD1u7y4vZqJBvt8QvpejqIS3FiFXvlJkjL0HLMTj/n7ClmnH2+FDjtWLLW6hlKRsP6vwJJid/QGVPbTsrZ2QB8E+XzsdmMXHV0j7jLI+8rP1jX/2ZiOewWU04PT4CgWG0v5t9oVau4e4c+jmEEEKMKVVV8Q/nvWCc6erREm023V0pGiQwFvp0dPeQlZaCzWKOW+rQ5vLqzxarKrx/P2TPgkXXkWExoarg6okzMMJshdy50HBA508wOR2pd+Dq8bNihpYJLs1JY+e/Xcp1y4t7D/rwMW1C3bzBZRSgvWCoau8LyJCUnQ+qXwuOhRBCTEjf++tBln//Nb774gGONzrGejnDFroCrT9j3DAlOlKABMbD1tblJSvVjM1qolNHKYXu+uK6nVowde4XwGAM1wHpqjMuWDzlSyl21bQBsHJGb4lEuIQCoPm4Vvu76g4wRM7g24MTgYZVZzx9hXZbt3Po5xBCCDGmDtd3oqrwzNZqLvnJRn7/YfVYL2lYQrGErhpjr1vr9T8FxkGDBMbD1t7dQ3ZainbZXU9grDdj/OHjYE6HipuA3k91uoK0gsXQVgWeif+pdqh2nmonNz2F0pzUyAfs+LW26S7SFMGg0KjM4dUZZ2tZ/7pdQz+HEEKIMdXW5WXt3Fw2f/Ni5hdk8McdtWO9pGFxJJIxdjZotxmSMQ5TFOUKRVGOKIpyXFGUb0R4fIaiKG8rirJLUZS9iqLEmEIxubS7vGSmmbFZzTg88WuMdQXG3W2w/09QcSNY7UDvL6++jHFwA17j4fjHTlK7atpYMSMbJdIOWm837PotLLxa22UbRaj2algZY4DilXBaAmMhhJioWrp6yElPYVqGhcsWFbKzur3f5NuJJhRL6KoxnkLDPUBHYKwoihF4GLgSWATcoijKogGH/SvwB1VVVwA3Az9P9kLHVO0OqHwv4kPtLq2UIsMSP2Pc2tWjaxw0u5/V+t+u+Wz4rlBg3KUnMM4P/vVM0XKKdlcPJ5u6WBHceDfI4Ze0y0JRNt2F9AbGw3zxm74COmt7X1yEEEJMGKqq9rvie8GCPPwBlc3Hm8d4ZUMXild0Tb4LD/eQUoqQs4DjqqqeVFW1B/gdcN2AY1TAHvz/TKAueUscY85G+O3H4Lcfj5iB1eqGU8I9b6N1jfD5A3S6ffHHQasqbH8cStZA4dLw3RmJZC+zZkCKbcpuwNtVow326Ftf3M+x1yE1B8rWxTyPLRk1xgDTVwLw8qsvc/vjMuxDCCEmkk63D39AJSf4/r2iNAub1cS7R5vGeGVD5wxe4dZVSuEIBcaSMQ4pBmr6fF0bvK+v7wK3KYpSC7wM/ENSVpdk3T1+XtxTh9sbp7NDX698U7v0npIOz9+j9b4Ncnv9eHwBMlPNZFhN+AIqbm/kiXPtwUsucUspqt7TWrSt/my/u20WLUjTVUqhKMENeJM7MA4EVO777Q4efOO5IsHsAAAgAElEQVRYv/ue2VqN2ahQUZI5+EmqCifegjkXgSH2r789+GEk3qbKuIoqAAW1bhebjjfj9ctUQiGEmChau7QZBKHA2GQ0sHbONN492hS/heo4FUr4hGKLmJyNWhvY9GkjvKrxQU9gHGnMycDfhFuAJ1RVLQGuAp5SFGXQuRVFuUdRlO2Komxvahr9T1rbT7XypWd38fZhnZe0j78B+/8I5/8zXPsQnNkDG/8n/HC7qzfYDWcXo9QZt4eGe8TKGKsqvPdjsGbB4o/2eyjdonVOcOq9rF+wGBoPaOecpF45UM/f99dz/xtHefz9SgB+9vZxXj/YwNevWBj5ElHDAehq1IZ6xNGbMR5mKYXFBnkLKHYdwh9QOd3WPbzzCSGEGDWhwLjv+/cFC/I40+HmWKNzrJY1LKEkWyi2iH1wPaTnRe3gNNnoCYxrgdI+X5cwuFTis8AfAFRV/QCwAoM+Wqiq+qiqqqtVVV2dl5c3tBUPw3lzplFgt/CnnafjH9zjgr/9M+TOg3X/DOXXQMXNsPF/wz1pQ5PsstLM2OJ0jQiNg86JlTHe8QScfAcu/lcw9++mkFC7NoCCReDugE4dP+sEFAioPPjGMebkpXP54gJ+8NJBvvviAX7y+lGuX1HMZ8+fFfmJJ97SbmdfFPd7WM0GzEYlZinF7pp2/uUPe+I3fp++gjLPUUClsqUr7vcWQggxPrQFA+PcPoHx+vlaDLNxgpZTdHl8pJqNmIw6wsApNNwD9AXGHwLzFEWZpShKCtrmuhcHHFMNbABQFKUcLTAed78tRoPCR5cX886RRlqcntgH73kW2k/B1feDyaLdd+X/0y4lvPqvoKrhjHGojzEQdQNe6BNnVrTNd22n4LV/hVkXDCqjALCYjKQYDTg9OstAwqOhJ2c5xcv7z3CkwcGXNszjwZtXsGpGNk9srqKiJJP/vH5p5G4UACff1qYDZg6sBhpMURRsVjOdMXYe/+jvh/jTzlrqO92xTzZ9JVmBNopo5VSzBMZCCDFRhDPGfRJbxVmpzMvPmLB1xk6PT18PY9DatUlg3EtVVR/wReBV4BBa94kDiqJ8X1GUa4OH/Qtwt6Ioe4BngTvVcVp487GVxfgCKn/beyb2gXW7IC1Xm1wWkpoF678K1ZvhxJvh8oisvqUUUQLj0LE5kUopAgF44QuAAtc9HLX2NcNqChfMx5Vfrt1Ows4U/mC2eG5+BldXTMdqNvJ/d6zmCxfN4dFPrY4+ctvbDac26yqjCAltqoxk/+kOtpxsBaDJEeeDVnDQR4XhJFUtLt3fXwghxNhqjfL+fcH8PLaebMU1nOmoY8Th9oWvdMflbIjZ2nSy0dXHWFXVl1VVna+q6hxVVf8jeN+/qar6YvD/D6qqulZV1WWqqi5XVfW1kVz0cCwstFNeZOfPO+M0527Yr2VdB2YeV94BmTPgrR/2CYzNffoMRw5cQ/+wIm6+2/WUtunuiv+ErNLBjwfpaQkXZs3U1tlwUN/xE8hL+85wrNHJlzfMw2jQ/n6y0lL46uULKcy0Rn9i9Qfgcw8hMI78d/pYsK4ZoDFexrhwCT6MVBhOUDkgY3y6XWqOhRBivGrr6sFiMpCW0j/pct7cXHr8Afaf7hyjlQ2d7oxxIKBtvpsiHSlgik6++/jKYvbUdnA8WtG83weNh/q1SwszpcCFX4e6XWRVvw4Ea4zjdDBod3mxmg2kDviHhccBb/0AZpwLKz4Vc93pFpP+GmPQ6owbJ1dg7PUHuP/1o8wvyOAjS4sSe/KJt8CYAjPP0/0Um8UcMWNc3+Hmr3vquGqp9mLRGC9jbE7lmFrKUqWSU31qjPef7mDtj95i68kW3WsSQggxekLDPQaW6OXbtERMaL/RROJ0+/S1anO1gOqXUorJ7tpl0zEo8PyuKFnj1hNaZjFUpztQxc2QO5dVJ3+O1QSpZqOuGuOI2eL374euJrj8PwZnpwewJRoY5y2E5mNaoD9JPLe9lsrmLr56+UIMhth/XoOceBtmnKO13tMpWinFbz6owq+qfOWyBShK/FKKQEBlp38OKwzHqWtzhlu2bQkGxBN1Z7MQQkx20abWZqZqJZQTcQKe0+NLbLiHlFJMbvl2K+vm5fHbLdX8/J3j1HcMuAxev0+7LYwSGBtNcNG3ye8+wX+kPIFCb5PsUBDV6HCz9kdvsbdWGzYR8R9WezVs/hlU3ATFq+KuW6sxTjAwDnihrTL+sRNAd4+fB988yqqZ2VxSnuAEHke9Vh6TQBkFaC3bGhxufvC3g1z14Htc+D9v89GHN/Hk5iouX1TI7LwMctNT4maMXV4/7weWYFO6WaIeD7ds21ndBjD4d1AIIcS40OrqITdjcGBsDwbGsTZoj1e6a4ydDdqtZIwnv29cuZD5BRn89ytHOO9Hb3Lfb3dwrMGhPdiwHwxmmLYg+gkWf4y/Z97MxwOvwavfxmRQSDUbwzXG+2o7ON3eHd7kp03IG9CR4o3vaVniDf+ma80J1RgD5AXX3zR4Yt9E9MTmKho6PXz9ioXRu05Ec/Id7TbBwDjfbqHd5eWpLafITjeztESbeLSg0MaXNswDYFqGhSZH7MC2y+Njc2AxAQysN+4Nt2zbeUr74FTXIXXGQggxHkW74muzmFCUiZsx1lVj7Jh6gbHOLYmTT3mRnefuPY/K5i6e217Dbz44xasH6vnYihL+27MPY94CrZ44GkXh16l3gNfFlVseBkcd/2r2MfOkFXaup75dyza/d0ybpd7m8jI9q09v4j2/04aHrP8qZJboWnPCNcZ9A+Pya/Q/bxzqcHn5xTvHuXhhPmfNykn8BCfegrRpUBChbjyG+y6cwyXl+Syenhm120W+3Rq3lKLL46ODDJrti1nXvo99zV3UFXSH27xJxlgIIcan1mCN8UAGg4Ldap5wgbGqqlpgnEgphQTGU8esael87YqF3LVuNg+9eYwnNlfxw8y9pC6In1ns6Pbxl4J/4Mr86bDzKT4W8GNsBV58jpsxMtu8gKbmTNx/KOF2Zw/NKZ/Wnlj5HrzwRShbB+u/pnuttkRLKVLSIWsGNE78jPGmE810un18/sI5iT85ENDqi2dfGHcM9EB2q5lVM2MH4vk2S+/Vhii6gv2nHcXrWN7xC15vaGCnTeuPXZqTKoGxEEKMQz2+AA63L3KrVbTN96GZBhOF2xvAH1D1ZYydjWCxQ0rayC9snJiypRQD5aSn8JXLF5BNJ6mexuj1xX20uXrISrdqgz++XccteX/m7pIX4XPv8ZL9RuyGbhYrVfirPuA29W/848Gb4O/fgN/fCjmz4KanYmelB8iwmHB7A+GNW7rkLYSmI/qPH6dCu35Lc4bwj7NR/xjoocizWWhyeAjEmH7XFexz6Sq9AKOiklG3iV3V7VhMBjYsLKCuo5tx2vpbCCGmrFBb1uwogXFm6sTLGIcSbLpqjB31UypbDJIx7ic9xcgSY432RcHimMeqqkp7t7ffJDubxYTD44OiCu4P3MzCeXez+UQLq/OzOXpkH0/MfI05W3+hXdK/9TlIzU5ofaHLHl0eH1mxRkv3lbcATr4LAf+EnnMeeuEJ7QJOyIm3tds58cdAD0W+zYIvoP0+RMsqdAVfiPzFq+g2pDGjfQtvVp9DRUkmpTlpuL0BOrq9+v9ehRBCjLjwcI8or80TOTDWlzFuANvU6WEMkjHuR1EUlqeEAuPYtajdXj89vkC/QMZm1TbH+fwBalpdzJqWztq5uWw81kSNWsD+c34Mn98Kd78F2WUJry/0SxxtEltEeQvB74G2qoS/33jS4fKSYjJEn2oXy4m3IK8c7NOTvzC0jDFonUii6erRSikyUq3UZq5mpXcnB053sHJGNkXBoSR17VJOIYQQ40mrM8bUWrTOFIO6Urg7wBent/0YCm3iz7DoSDQ5GyAjwS5QE5wExgMsNtbQYcyBjLyYx4VqivpljIM9b890uPEFVMpy0zl/bh5ev3aJPDstBfIXQvbMIa2td7peIoFxcDT0BC+n6Oj2kjWUbPEQxkAnKtTkvbEz+gthKGOclmLCUbyeEqWZ6YE6VszIDk/rq++UzhRCCDGeRBsHHTIoY3z4Jbh/CTx5LfjHZybZEeyepWvznaNhSk29AwmMB5mvnqLaPDvucaGa177BWobFjNPjoyrYimtGbhrr5k0LPx5xwEcC+pZS6JY3X7ttOjSs7z3WOrq9QyujOLVZy5iPaGCsZYxjdaYI/Z2lW0yY5m8AYL1hLytnZjE9U+tWckY24AkhxLjS1hWqMY78/hMKjFW/F17/d/jdJyEtF2q2wJvfG82l6hbKGNvilVJ4HODtmlLDPUAC4/78Xkr9NRw3lMU9tCOcMR5QSuHxUdmsBcZluemU5qQxM1fbMBbtH5Ze4VKKRAJjiw3sJRM+Y9zuGmJgPIQx0InqLaWIFRhrpRTpKUaKyhZxJFDC3SmvkW/Vnm80KJyRUgohhBhXWkKBcZTEVlaqGV9AxfvS12HTA7D6M/CFrdrt5p/Ckb+P5nJ1cfZJ1MQ+sFG7nWKb7yQw7qv1JGa8HA6Uxj20ps0FEL4MDr2fvg7WdWIxGcKZxPPnalnj4WaMQztIExryAdoGvAk+5KNjwEZH3SrfhdKzR7TVTLrFRHqKMWaNsavHh8VkwGQ0MM1m4ceGOyhVz8DmhzAaFApsFskYCyHEONPW1YPdasJsjBwuZaaaWaqcxLzzcTjrc3D1/WCywOX/BYUV8Py90HF6lFcdW3jzXbzA2DH1ehiDBMb9tWqjk4964xea76ntwGY1MbNP+7BQYLy/roOZuWkYDNp0ti9cNJcff2KZvrnkMaQPpcYYgi3bjmr9fCeojm5vePymbj1d0HAAZpwzMovqI96Qj75z6RVF4e4776J73jXw3o+hrYrCTCtnZPqdEEKMK62u6N2GADKtRr5vfgJ/6jS4+Nu9D5it8IkntI14u3474utMhENvKcUUHO4BEhj313oSgIOe3LiH7qlpp6IkMxz8Qu8OzyP1Dmbmpofvn56VysdX6ZtuF0uolCKhGmPQNvz5uqGjethrGCva5rsEM+51u0ENQMmakVlUH3kZljilFD7SLb0dNdaU5ZB69f8DxQgvf40iu1WGfAghxDjT2uWJGRjPq3uBFYbjVK78Blgz+z+YO0d7/zny8givMjFOjw+TQcFiihMChkoppF3bFNZWSY8xnQZfOm6vP+phbq+fI/UOlpVk9bs/9OnL61f7ZZKTJT1lCO3aQMsYw4StM/b6Azg9vsRrjE9v126LVyV/UQPk2S2xN9/1+MN/f2GZxXDhN+DYq9zqfJzGji4Z8iGEEONIa1eMjHF3G2W7/4dtgQWcKPxI5GMWXAlndo+rcoouj48MqwlFUWIf6KgHgznhmQsTnQTGfbVW4kwrBZTBfQn7OHimE19ApWJAYNy3WfbMaekDnzZsRoNCeoox8VKKacHOFI0TszNFZ3i4R4KlKLUfav2i06fFPXS48jLiBMZ9Sin6OefzsOpOzqv/LY8p36ezsWYEVymEEFPD1pMt3PjLD9jw43d4cU/dkJMObV090fcHbfxfjJ52/t17Jx3uKDHDgqu026PjZxOe0+3T16rN2aiVUcQLoCcZCYz7aqvEbdN6DHdG+yVHK6MAWF7aPzC29w2MRyBjDFrwnfDmu9QsSM8Ll4pMNKEekQlPhavdAcWrR2BFg+XbLTg9Plw9kf9uunr8kQNjowmueZDda/6bpUoltl+dBU9dD+/fP64yDEIIMRE43F7ueHwbNz26hVMtXZiNBr707C4+9vPNnGxyJnQuVVVp7eohJyPCe0/bKdj2KN6lt3BInRl9+l3eAsiZPa66Uzg8egPj+inXqg0kMO4V8EPbKXxZWmDc0R09+Nxb20G+zdKvIwX0nyJTlpv8jDFoG/ASzhiD9g9zggfGCZVSdNaBow5KRikwjjPko8vjIz0l+tS+wNIbuabnh9TNuh46T8Mb34UnrtI2EAohhNDl7/vrefdoE/9y6Xze/epFvPSldfzPDRWcaHTyk9ePJnSurh4/Pf5A5HHQb/0AFCPmDd/CaFCiB8aKomWNKzdqfYHHAafbF3/jHUzJ4R4ggXGvztMQ8EL2LO3LGKUUe2rbB5VRQG+NscmgMD3LOujxZLANKzCuTP6CRkF7KDBOpF1bbbC+eBQ23kFvL+MmZ+TA2BWtlCKoKNPKCbWYd+d+XeuBefuL2hjvt344EssVQohJqaq5C5NB4b4L52A1GzEaFD6xupT18/PYHbzaq1fvcI8BgXHdLtj3HJz7eZTMEuxWU/TAGLQ6Y3+P1ld/HHDqzhhPvXHQIIFxr2DQaJqmTb2LVkrR0e3lZFMXy0szBz2WlmLEoEBJdiqmKD0PhyvDOozAuLNWG5E8wXQOJWNc+6E22KNw6Qitqr9Qz+poGWNnnIxxvs2K0aD0dqaYfQGsuQu2/AKqtyZ9vUIIMRlVtXRRmpM26D14eWkWtW3dMfeCDBQa7pHbNzD2eeCVb2nT7db+IxCafhfjfbn0HG0D2zgpp3DGSdQA2jhrV/OU60gBEhj3atMCY2v+XICon/72n+4AiJgxVhSFDIupX6u2ZMuwDKHGGLTAGLQs5AQzpFKK0zu0oNhkGaFV9RcOjCMM+VBVFVe0GuMgo0Eh32ahru/0u0u+C5ml8MIXwCut3IQQIp7KZhezImx+Xz5De8+OmzWu3gq/vACeuRnLvmfIprM3Y+xsgievherNcOkPwGoHesdCR2U0wbzL4egrWsA5xhx6SinCU+8kYzx1tVaCwUxGXnDzXZRf8j212j+qipLBGWOAtXOnccH8vJFZI8OpMdZKRCZinXG7K8HA2O/TLnWN0sY70KYamgxKxGyExxfAF1DjfkIvyrRS39kno2+xwbUPQssx2PFEklcshBCTi6qqVDV3Rdzjs2R6JkaDwu6atugn2PEEPPERcLVA/T7Kt32THZb7WPziR+CVb8KvLoYze7TBHStuDT8tMy2FDldP7MUtuR662+DQi0P74ZLI6fHGL6VwNmi3UmM8hbVVQvZMUlLMpJqNdEbJyu6t6aAsNy1qh4Rf3LaKz5w/a8SWabOYcMTomBFVKGM8AQPjjm4v6SnGqCM5B2k8CF7XqNUXAxgMCtOiDPlw9Wg9sWOVUgAUZaYOHgs952IoWAIHnk/aWoUQYjJq6PTQ7fUza9rgrlCpKUYWFtqiZ4zf/D789cswaz3c+x78036eW/U0D/g+jiktGz58DAI++MzfYfHH+j01bsYYYO6l2vvwlkeG+uMlhc8fwO0N9GsWEFEoMJauFFNYa2V445091USHK3rGOFIZxWgpykql0+0LbwrQLTUbUnMmZGDc7vImWEYR2ng38oM9+sq3Rw6MQ5MK0+J8Qi/MtHKm3T243+ai66BmC3SeSdpahRBisqls1rr4lEWZI7BiRhZ7azoIBAa8xrZXo77/AP6lN8Gtz2nvl4rCAXUWj5tuxPiZl+Ab1fCP+2D6ikHnzUyNs/kOwGCAsz4Htdu0Ur8x0uXREjUZ8UopHFNzHDRIYKxRVS0wDpYbZKaaI26+a+vq4UyHmyXF9tFeYVhFsVbCsTdY65yQCdqyraPbS2YiPYzP7AFrVviDzmiZm5/Bvtp2fP5Av/u7gr2N4126Ksq00u310zlwE8ei67Tbw39L2lqFEGKyqWoJBsZR9vksL83G4fFxYmA/480/w68q/KD7BjD0XtlrdLjJtwf3qZitWq1wBFrM4Is/RGT5JyHFNqZZY4dHi21scUspgjXG6VJjPDW5WqDH0Zsxtka+LHK0QetBOL/ANqrL62tJsLZ5b4JtZ4AJGxh3dnsTm3pXv0/beDfK03ouKS+gzeVlx6n+NWyhjHH8GuNUAOo6BnQOyVugjfU++ELyFiuEEJNMVXMXKUYD07NSIz4eGsq1q+/7Z1cz7PwNrxjWs6W5f5vVxk4PBbb4rVczU834A2r8/T9WO6y4TSuNC2VkR1lojXEzxs56rfOGKcHBWpOABMbQ2983J1RKETljfLRR+5Q5loGx3Wpmdl46e2qHmDHuqNXazUwg7d09ZKXq/Mfp90HDASisGNlFRbB+fh4pRgNvHGrod3/o0lXcGuNg7+v6gXXGoGWNT23q/RQvhBCin8rmLmbkpmE0RE6KzJ6Wjs1q6l9nvPWXqD43P/NcNWiPR4PDTYE9fmej0PtTe5QSzH7OvkerVf7712DvH6Bq06i+J4e6WsXdfOdomJJlFCCBsaYtFBhrG9SiFdIfa3Bgs5goyhyZ4R16VRRnsrd2iBljNQDt1clf1Ajq6E6gxrj1BPjcULhkZBcVQYbFxLlzcnn9YEO/S2r6M8ba79WgDXgA5ddqf3dSTiGEEBFVtUTuSBFiMCgsL81id3Xw/dPj1MY6z7uKw/7pdHR7cQVL31RVpaHTQ749/vu9Pfj+FLfOGLT34VV3alcA/3y3NuH05+fA0VfjPzcJHLozxhIYT22tlYACwXHQdqtpcJ0nWinF3IIMlFG+RD9QRUkWjQ4PDZ0J9rYNdaZoOZH8RY2gdpdX/9S7+n3a7SgN9hjokkUFVLW4+tWwhS5dpafEfiHKy7BgUODMwFIKgILFkDMHDo59qx8hhBhvAgGVUy2uiB0p+lpemsWRBocWAO95FtztnFnyufDjocRER7eXHl8g3KM+llDiJtbE3H6ueQC+eRq+uF1r/aYY4Zkb4ZmbtJZuIyiUMY5fY9wwJYd7gATGmrZKsE/XiuvRfskdbu+gnavHGpzMzx+7MoqQZcGpe3sSrTMehZZtgYDKXU9+yMv7ktNBwe314/EF9GeM6/eBwQzTFiTl+yfqknJto8LrB3tLHsLt2iyxSylMRgMFdmvkjLGiaOUUlRvB1Zq8BQshxCRwptONxxeI2pEiZHlpFv6Ayv7TnXDyHciZTXXaot7zBIcsNQSnmBboyBhnJpIxDrFkwLR5Wuu3+4IDQ068Bb+5bkRf4516rmCq6pQdBw0SGGv6tGoD7bJIQAVnT2/WuMXpoaWrh3kFGWOxwn4WFWmNyvcmWmeclgOWzBENjLefauONQ41sq0zOP+yEx0HX74P8hWO2YaAoM5WlxZm8frB3Y4WuF6Kgwkxr5IwxwIIrQfVD5btJWasQQkwWVcFWbbPiTJ6dF0xuVTU7oXoLlJ7TbzBTaPNzaIqprsA4bQiBcV+mFFj7Jbj5GWg8DL+5dsSC43CNcaxSiu428PdMyeEeIIGxpqMGsmaEv7RbB18WOdow9hvvQlJTjMwvsIWn8OmmKNoGwxEMjJ/fdRrorasdrvahBMZjsPGur0vKC9hV0x5+sXX1+DAaFCym+P/cpkca8hF+cKX2webEW8lcrhBCTHjxehiHFGRaUBRw1R8FVzPMOLtfYDwwY5xIKcVQAuN+Ld7mXQq3PAPNx+C314MvwXkFOjj0lPaFp95Jxnhq8vvAcQYyi8N3RSqkP9Y49q3a+lpWksm+0x3x+yYONIIt23p8gXAJRah8YLhCfwdZemqMHQ3Q1ahNihtDly4qQFXh7cNaOUWXx09ailFXbXphppX6jghDPkDroTlrHZx4R7vUJYQQAtAyxlazgcI4GV6LyUhehoW0+g+1O0rPodHhIS3FyLSMlPAVu9AennwdXSnSU4yYDEo4kaNXd4+fc/7rTZ7fVdt759xL4OP/B3W74L3/Teh8ejjdPtJTjFE7dwDQqSW4sBdHP2YSk8DY2aDt9rf3DYy1T1J9N+AdbXBgs5p0tW4ZDRUlWbS7vFS3uhJ7Yu4crSuFf4iXfGJ450gjHd1eTAYlPNRiuEITCHVljBvGduNdSHmRDbvVxO5gRr/L44vfGieoKNOKqyfCkI+QORdDR/WE20AphBAjKdSRwhAr4AuanpVKfttubRDUtPk0OTzk2SwU9bli19jpxmY1kRZn0zSAoij6xkIP8GFVKw2dHracGFA2UX4NLLsFNv6vFiAnUZfHF78jRWeddmufntTvPVFIYBzhk1F4h6m7fynF/ALbmHekCKkIDvpIuJ9xzmytTnUEWra9sLuOnPQUVszIwuVJTsY49AlcVx/jcEeKsc0YK4rCwiI7R+q1qwxdPT7S4vQwDgkN+TjTGaXOeM5F2q2UUwghRFhlc+xWbX0VZ6Uyy70fSs8Gg4FGh5u8DAtFffZ4NDo8uuqLQ4YSGG860Qz0XpHu54ofae3Snr8PvAl2oIrBqSdR0xncPG8rStr3nUgkMO4IXsLoW0ph7V9KoaoqxxoczB8HG+9CFhTasJgMiU/AC20yDPVuThKH28sbhxq4pqKIzFRz8jLGidQY1++DzBnanPsxVl5o40i9g0BApcvj150xLgz1Mm6P8kKYMxuyyyQwFkKIIH9ApbrVFbe+OGROhoeZgVrU0rMBaHJ4yLdbmJ6V2qfGWN9wjxB7qll/u7agzcdbADjW6BxcPpeaBdf+FJoOweaHEjpvLA6PjwxrnPfTztOQnjclp96BBMYRLxmEdpiGfsmbnT20ubzh3azjgdloYH6BLTyNT7fsMu22rSqp63llfz0eX4DrVhSTlmJKXo2xqwdFAVu8Sz/QOwp6HFhYZMfp8XG6vZsuj0/X5TiA6VkxhnyEzLkYqt4bkXIYIYSYaBxuL16/qmujHECFekR7XsFqQAuMQxljh8eHw+3VhnvoGAcdkmjGuN3Vw/66DgrsFhxuX3izXz/zLtFqjnc8CYGA7nPH4nR74/cw7qybsmUUIIGx9snInK7VGgVlpJhQlN7A+FjD+Np4F1Kak0ptojXGGQVgsiY9MH7nSBPFWamsKM0i3WJMWleKjm4vdqs5ft1Yjwtajo95GUXIwkLtd+XQmU66evy6WrVB75CP+mgt2wBmXwQ9Tqj9MBlLFUKICS2hK4vA7O799KhGaqwLcXv9dLp95NutFGVppWx17e5wFlmvrDSzvpHQQVtOtqCqcNvZ2mCxiOUUoNUad9bCqfd1nzsWfaUUdWDeYvMAACAASURBVFN24x1IYKwFxvbpWiuzIINBwWYx0Rns93c0HBiPn1IKgNLsNGrbugcNIonJYNAm/CU5MK5t72Z2XjqKoiQ3Y6x3HHTjIW0T5TjJGGv16HC43hHcfKevxthkNJBvs1IXK2M8az0oBimnEEIIejfK23UGxgXtu9mvzqLWqYZbtYUyxgCH6zvp8QcoSGLG2Onx9evvv+l4C2kpRm5YXQJoA8QiWvgRSLHBnt/rXkssTrcvfqLGIRnjqa3jdL/64pDMtN5f8qONTjJTzeTpvEwzWkpy0ujxB2hwJFiYn12W9MC4vqM7/KKSnmKkq8eXeCu5CNq7vfpatdXv1W7HSWCcbjExIyeNI/Xa6NE0nRljgKIsrWVbVKlZULwKTsqgDyGESChj7POQ1ryX7YEF1LV30xgKjG29gfGuam3vTqKb7zojTMwN+f2HNdz4yw94aa+2sW3TiWbOmpVDod1KdpqZY9HKIs2psPg6OPgX7croMDk8vtiliT0ubcDHFN14BxIYBy8ZlAy6227VCun9AZX3jjWxtDhz3HSkCJmRo82Er2mNcdk9kuyZ0HYqab1wff4ATQ5PuH9kmsWEqoLbO/yaKN0Z4/p9YLFr2fBxYmGhjUP1nfouXfVRlGkNT1+KavqKYJZc+hkLIaa2UGAcarUaU91uFL+HvYoWGDf1CYwL7FYUBXYHN7UnUkqRmWpGVXsHaAz6tu3aa/o3/rSXbZWtnGzqYu2caSiKwrx8G8ejlVIAVNyslc8deVn3eiJRVTX++5Ej2JFCSimmKL8PnPURLxmEPv29caiBmtZuPnn2jAgnGFul2Vo9VE2idcbZZeDp1D4VJkGT00NAhcJgq7H0YGuyZHSm6Oj26rs8Ftp4N44+vCwstFPV3IXbG9Ddrg20lm1Rh3yE5M6DHkfvhCIhhJiiQq1VdSVRarYAcMa+TKsldvZOuDMbDeTbLBys6wRIqJQi9D4VrTNFo8NDbnoKigKf/vU2AM6bmwvA3IIMjjZE6EwRMnMtZJbCnmd1rycSV48fVY0zDjrcwlZKKaYmxxmtLjVCKYXdqpVSPP5+JcVZqVy2qGAMFhhbcXYqigI1bUMIjCFpLdtCHRRCl6FCHRiS0cu4w+UlK96LXSAADQfGfOLdQOVFNkJX1RLNGLt6/OEa94imzdVum48NY4VCCDHxJVRKUb0VcmaTml3E6fZumjrdKArkpGutyYoyU+nxa1c7E80YA1E34DV2upmdl85/31BBV4+fnPQUygvtAMzPz6Cj2xsO0gcxGKDiRm1fiaNe95oGCm2Kj/l+1CkZ46kdGIdbtUWoMU41U9XsYmtlK3ecNxOTcfz9UVlMRgps1iGUUpRpt0mqMw7Vw4Z68KZbkpMxVlVVXylFWyV4u8ZNfXHIwuCLHqC7XRv0GfIRq5wid5522yKBsRBiagtNXE01x7kyp6pQsxVKz2F6llUrpXB6yE23hN/jQwmezFQz1njn6yOUwIm2Aa/JobV/u2JJEV+9fAGfv3BOuNvSvGDHq+PRNuABLPm4lsg7/qbuNQ0UKvOIWWMczhhLjfHU1Bkc7hEhMLanmujxa5fAb1o9/sooQkpzUhPPGIfqcJMUGIcyxuEa41DGeJiBcUOnB19Ajb8BYpxtvAuZkZMWfqFO19mVAvoM+Yi1Ac9eDKZUaD4+rDUKIcRE1xlMoMTdB9RyAlzNMONspmel0ujwUNvW3W9jfSgxkchwD+idfxArMA59ny9cNJe71s0OPzYvX+t4FXUDHkBeOVgzoXZbQuvqy+nWkzGu075Pir5hKZPRFA+Mo88DD02/u2FVSfgXfjwqzU5LvMbYkqFNtUlSYNzQ6cZiMoS7R4QzxsMspThQp427XjTdHvvA+n1gMEHewmF9v2QzGBQWBPsZpyeUMY4z/U47uVZO0Xx0WGsUQoiJTvdelGB9sZYx1gLg/ac7+gXGoSFLiQz3gN5SikiBcXePH4fHF7U0I89mwW41Re9lDNprfvFqqBl6/3qnrlKKqd3DGKZ6YNxxGlIytE9HAxRnp2I2Ktx5XtnorysBJTlp1He68fgSDEKT2LLtTIebokxr+NN6sjLGB+s6URQoL9IRGE9bAObEXshGQ3lRMDBOoMY436ZjyAdo5RRSSiH+P3vvHd7YWeb9f466ZUu23LuneWqml/TeE2oIIYTQQmi7sMCywML77rs/ll4WlrC0hbALbAgltIQkpLdJm5ZML57u3i3Z6uX8/nh0ZMtWOZI9M7b1fK6LS0E6R3rskaXvuZ/v/b0lkgLHE4joE8anX4EiF1QupSEujId94aSJeVrFOBd/MWQWxn3xSNV0YltRFFprHOmzjDWatkDfAQi4c1qbxqhWMc5kpSjwDGModGHs6RRXRim2X960tp7nP3sli6pm11CPyTS5ilBVMaknJ2ZQGPe4/Yntfxivjk6/YuxhQUVx9sa1nn2zZuLdZJbVaMJYv5VCG/KR0UoBUNkKI6chkqZhQyKRSAoA3bGe7a9C0/lgMCQqxkCylSJeMc4lwxigyGzEbFTSCOPxSLh0LK0p4WgmKwUIYYwKnTtzWpuGVjF2WDP8rjxdBZ1hDFIYp70yMhkNiSvH2UxTIss4j2QKdwdE9Y+wTEe3O5DwFwPY4yJwuhXj/d3u7DYK74C4wp1l/mKNG86r420bGnMeJ95UXsSx/iwfkhWtohlj6Pg0ViiRSCRzG48/jDNTFRTAOyisZ03nA+OWNSCpYtzksmNQoNGV2/e/oiiUFllw+0NTHuvzjEfCpWNJtYNBb4jBdMkUIKwUKHnbKcbisXZpCzWREIz1SSvFuV7AOSXN1Lu5RGLIRz6RbWoM3O3Tev1YTKXPE0xkGMOEivE0xkK7/WHah/ys1GOjgFkrjGtLbfz7bWtz6m4GWN/sYl+nh0A4w+9QRrZJJBJJovkuI+2vitvmCwCwmY1UloiItomV3CqHlT989CLetmHq4K9slBaZUlaM+xNWivTCWJtLkHH31+aE6hV5N+AlPMbpLiLGegBVWinO9QLOGdGwGI4wx6+Mapw2zEblnEW2DflChKKxpKtvm9mAooAvzQQgPRzsFgHrq/Q03gHUzE5hnC8bW1yEojH2dWbwklXEhbH0GUskkgJFd6xn+ytgMIupoXE0O0VVSbJgXd/syrmYAcJnnM5KYTIouOyWtOdqHunRQJZd3MbNomIcy32y7GgwgsVowGpK87PJDGOgkIXxaDfiymhuvwGMBoWGsqL8rBQwbWE8OcMYxJZSscU0rYrx/i5NGE9tjExewF7xb1hckfdrzUY2NLsA2Hkqw3RCqwMc9TKyTSKRFCy+UJRITM3efHf6VahfB+bx3c36RKPdzDRuZxLGlSXWRG5xKrRs4YyDnUD4jIPuvBKJxgIRnVPvpMe4MMkw3GOu0VRuz91K4agDo2XawnhyhrGG3WKclsd4f5ebaoc1Y7MCAL37Zt3Eu5mgymFlQYWdHZmEMcjINolEUtDoGgcdDUP36wl/sUaiYpzte0YnmYRxtpQLLSI2a8VY+xnysFN4g5HsUW0grRTnegHnDHd8uMcc9xgDNOaTZWwwQlnz9CvGnuRx0BrFVtO0UikOdHmyN96FfNB/COrW5P06s5mNLeXsOjWMqqrpD9Ii2zIdI5FIJPMUTYhqwjIlfQcgEkiyUQC8dX0Df3/lYootudsmUlFaZE45ElpMvdMnjLNWjCuWiMi59tyF8ZgeYWy2g60s5+eeTxSuMJ5HV0ZN5UUM+8IJY71uZiCyrcftx2RQqJjk0ZpOxTgQjnK0b0yfv1iNQf2GvF5ntrNpgYtBb4gTA970B1W2ikxL78DZW5hEIpHMEjx+8T2TsWLcuUvcNiR/V6xuLOUz1y/PPjFPJ6V2C6OBCNFYcqGifzRAVZaBIZrFIWvFWFHiPuPchfGoHiuFsz5lhG0hUbjC2N0OVmfK4R5zjSZXnpFt5Ytg8Pi0qo3d7gA1ThvGSd6pYsukivHgMXj0c/D798Mv3gSv/zrtc7b1jhGJqdn9xV2vidtJVYD5wqYW4TPOaKeoaBW3sgFPIpEUIFrFOKMw7npNVEFdC8/oWkpTNNBFojEGvaGsdg2jQaHEakoI/Yw0boaBwxDw5LS+sWAER6aK8Wh3wWcYQyEL46ET4w1ocxwty/jUYI7CuHoFhEbFkIg86XEHUs6Ut1snVIxVFf5wN+z4b+jZI6rUD34cul5P+ZzaKOisFeOuXVBSO28bBRZXlVBaZGbnyQzCWEa2SSSSAiZhpSjKIPi6dokCyhmuhKaafjcwFkJVM0e1aThspuwVY4C6deJWS2XSyVgwS8XY3Tkv+q6mS+EK4+ETUH5mrx7PFktrSnBYTTy8tzu3E6tXidu+A3m/do8nkHIQSlIqxZHHxAfTzd+Gj++EDz0L9kr400dSTm3b3+WhxGpKVMLT0vXalK2x+YTBoLCxxcWOU0PpDyptAqMVho6dvYVJJBLJLMGTrWIc9kPvgbPyXZFKGPePZh/uoeG0mRNjmzNSt1bcdu/OaX1jgQjF6SrG0YioGJfmnt883yhMYRyLwvCpM76tcrawW0y8Y3MTj+ztpnMkhzzj6hXitndfXq+rqio97kBSVNv4mowix1hV4dmviur82nfGHyyHN30f+g/CM1+Zcu7xgTFaa0oyRtsQ8Igq6Ty1UWhsbHFxrN/LsHfqNCVANFGWNow3k0okEkkBoYlQR7rmu569oEbPSi9KKmHcpw330BEJ57CZEikbmQ+sEbulPXt0ry0aUxkNZLBSjHaL31NZk+7nnK8UpjD2dEIsPG8qxgDvu3gBqqryy5dO6j/J5hTJFL35VYw9gQi+UHRKIgXEUylCUTj8iLiqvfxzYJzwwbX0OtjwXnjxnvHGiDjD3jAVxemD0IH4B4I674Wx5jPedTqDnaK0EUamN8FQIpFI5iKeQBiH1TSlzyXBWexFKbOL77iJyRR98Yqxnkg4YaXQ2bRetyanivGejhFC0RirGtL07iSSumTFuDCF8dAJcTtPKsYgIttuXF3Hr7edzi2doua8vK0U2nCPmhRXwnaLEX8oBM98DcoXw+rbpj7BdV8WYeuv/SrpbjHFKIswnueNdxpLqkuALI2Vpc2yYiyRSAoStz+cebhH5y4oqTkrCVQpK8aeuDAu0WGlKDLr8xiDsFP0HxKxpTp4oW0ARYFLllSmPiAhjGXFuDCF8XBcGM+jijHA3ZcsZDQQ4fc7cqgeVq8UloQUXl8QdomTA17+9FoHzxzuS4qhOR0Xa+kqxutjh6B3L1z2GTCm2L6xOWHp9XDgQeFvijPiCyWuvNPSuUsIwuI0f+TzBGt8LGkommH8Z2mj2AaL6vxAlUgkknmCJ5sw7tolbBRnIYIspcd4LIDLbsZiyi63hJVCb8V4rYgr1VnYev5IP6sbSilPtxvrjusG2XxHhvbEeczQCTEzfZ69AdY3u9jY4uLnL57gPRcuSL+1NJGalcJX1H94yqCM54/086nfvs7gBH9rQ1kRb9vQwIFuD88c7sdoUGiumNokZ7cYWWuIjypeen361191C+z/E5zaCouuIByN4Q1FKcs23rPrNTHec55jjX+YhiJZhDGqyOZ2tZydhUkkEskswOOPUJoukULrRTnv1rOyFpvZiMVkSDQEgqgYV2fJMNZw2ETFWFXV7NnKiQa816FxU8ZDPYEwr7WP8JHLF6U/yN0uBodYS3StdT5TuBVjV4toXJpn3HlBM+1DfvZ1uvWdoI1TTnHVufXoAKOBCF9962oe/cSl/OCODTSX27nn6aPs7XTzwUsX8finLkv5R19sMbHacIKIs0k026Wj9VowFwtxzIRMykwVY/+w+Dec5zYKAJNBQVEgmFUYI+0UEomk4HD7w+mn3nXvBtSzml40eSx032hQ98hpp81MOKpm/rxPvFCTELLd2RvwXj42SDSmcllrVfqD3B3SRhGncCvG88hfPBHtjf9CWz9rm3SMdSxfLOK+UiRTDI6FqCyxcMf5zQCsqHNy85o6BsaCuOyWjBVpu9XISuUk/or1ODK9vrkIlt0o7BQ3fTvRtJA5rD2ef1wAwlhRFKwmQ5aKcfzDTApjiURSYIielDTfF13xxu6z+F0xeSx0/2iQRZXFus51xDOGPf4wNnOWwp2iQK2+BrwX2vopthhZ3+xKf5C7Y97qolwpvIqxqooBE/PMX6xRUWJlVb2T59t0jgg2mqBqacpkiiFvkPKSqX6kyhJrVpuGEz+LDD2Mlp+XfQ3n3QL+ITjxPG6/sG2U2TM03yUa7+a/lQLAYjRkqRjHLUFumUwhkUgKC08ggzA+/SqUtZzVXpSyCRVjVVXpHw1SlWIIVioSwjgXn3HfAYikifOM8/yRAS5cXJHZ5+zukIkUcQpPGPuGIOiZ11dGl7ZW8drpYf3pFDXnQe/+KXcPeUOUF+v7g55M5dghAIZLV2Q/ePHVYHHA/j8lrrQzeoxPviBGIRdluPqdR1jNxszC2FwkBqbIirFEIikgwtEYvlA0dfNdJAQnnoPFV53VNU20Urj9YULRmG6PsTPFSOmM1K2FaEikU6Th1KCX00M+LluawUYRcAtdJIUxUIjCeJ4mUkzkstZKwlGVV48P6juheiWM9YiLhgkMekPZ84TT4HIfBKC/eHn2g802WH4THHwIj9cLkD6Vwj8MJ56H5Tfnta65iMWYxUoB4gNNCmOJRFJAZJx61/4KhMZEH8tZZKIwPjUokpvqUyQ3pcKZc8U4vmuawU6h7R5fmslfrOXgS2EMFKIwnocZxpPZuMCFzWzgBb12ipr4aOhJVWNRMc5PGJcM7aNLLWfEoMPnDKJrODCC4/QzQAaP8eG/QSwCK96U17rmIlaTgWAkmvkgKYwlEkmBoQlQZ6pUirYnRPrUwsvO6pqcReaEYN95SgxmWtes73tQm96nu2JcvggsJRmF8YttAzS6iliQIj0qgcwwTqLwhLFWMZ7HsVZWk5HzF1bwQlu/vhNSCGN/KIovFKUihcdYD0WD+9gXW4g3pPPKd/FVUFLDgo6/oCgZxnsefFDE7J3FLuNzjSVb8x2IDzR3u/DQSyQSSQHgzlQxPvoUNF8A1ozt3zNOaZGZ0WCESDTGzlPDNJQVUVdapOtcZ0IY6/zeNBhEA57Wd5OC19tH2Njiyhz/pvWnyHHQQCEK46ET4KgXvsx5zKWtlRzr99I14s9+cEkN2CvEMI44g14x8CMvK0VwFOPQUfbGFuILZql0ahhNsOYdLBzaygKrL3VzX3BMfNiteONZCWufLYiKcRZhXNYktg0DI2dnURKJRHKO0SwHU4SxuxP69p91GwWM2wA9gQjbTw6xaYH+XpiJqRS6adgAPXtSNuD1egL0eAKsbcxSsXZ3iOp6cbX+153HFJ4wHj4xr/3FGprRfqseO4WiCK+SFoOGsFEA+TXf9exFQWWfmkPFGGDdHRiJ8jbLS6kfb3scosGCslGA2AHQ5TEGaaeQSCQFQ8JKMXmH8dhT4nbJ2RfGmkjf3+WmbzTIphb9wthuMWI0KPorxiCGe0RDSYUtjd3tolCytqk083O4O0S6kaHwJGEqCu+3MI8zjCfSWl1CjdPK83rtFA0bRexLSDS/DSaEcR4V47jf6ahxCb6QzooxQPUKjluW8Ybo06ktAQcfEukLzRfkvqY5jEWvxxikMJZIJAVDWitF2xNiZ7haRyrSDKOt5amDfQBsbMkw4GoSiqLEx0LnUjGOT73r3DXloT0dbowGhZV1eoSxtFFoFJYwDvlE+kL5gnO9kjOOoiisbijlWL9X3wkNG8Xc9fgUnaExIYzzslJ0vQ4ltfitFXj1RsbFecJyNQuiJ8XW0ETCAVExXn7zvJxYmAmryUAoqsNjDFIYSySSgsGTaL6bIIyjYTj+LLRec04sdwlhfKgXh9XEstrcPM4Omym3inFpo7BAdOyY8tDujhGW1TgosmT5znS3y0SKCRSWMB4+KW4LoGIMyd2xWdGa2Tp3AhOsFPk033Xvhvp12C2m3CrGwMPqRYQVM+z8n+QHtv9MeGhXFpaNAuIV43AWYWyvFBMM5ZAPiURSIHj8YSwmQ/KUuFMviUzec2CjgHFh3D7kZ32LK+swrMk4rGb9qRQgxH/jpsR3t4aqquzpcGe3UUTDMNotK8YTKCxhHPGLQOzK1nO9krPC5JntGSmpFn8Y8RGag94QZqOCw5rj1HDvoAgbb9iI3WLMuWLc7rey23UD7Pg5PP8tYanY90d4/P/Csptg0dkNa58N6KoYGwzCIyYrxhKJpECYMvVOVeHZr4sK6pKrz8maJq4nF3+xhrPIhMef2/cmDRtgsE3k/Mc5NejD7Q9nb7wb7Ra7xbJinCBH1TPHadgIH37+XK/irFFaZGYsHhtjMuq4BmrYkLjqHBwLUlFszRzxkoqjTwIqLLmG4gOBnCrGsZiK2x/mhS2fZ5PPDk9/GXr2weFHhK/41p8XZHOArooxyCxjiURSULj94cRQDAAOPwqnX4KbvwOW4nOypom2jlwSKTQcNjPtQ77cTtJ8xl2vJSb97e4QjXdr9CRSgBTGEyg8lVFAaFeuuqfo1G8QdhPvYP7DPdoeF1frdetExTiHVIqxUISYCo7iInjLj+H8j8KBP0P5Ynjn/fM+Yi8dVpMxe8UY4lnGUhhLJJLCYMQ3oWIcjcCT/woVrbDhPedsTTazEZvZgNGgsK5J54CrCeTsMQaoXy9uO8btFLvb3djMBpbWlGQ+Vw73mIIuYawoyg2KohxWFOWooij/nOaY2xRFOaAoyn5FUX49s8uU5IP2gaHbTtGwUdx2vSbGQefqL45FRcW49VowGCi2mPTnGANu34QOY4MBbvga3PF7eN9foSj3K+/5gq4BHyCu+Ee7hWdMIpFI5jl9ngB1jrgwfu2XMHAErv0iGNMMiDpLlBaZWVXvxG7JfVPeaTPnlkoBUFQGlUuTfMa7O0Y4r740+27xyGlxW9qQ40rnL1n/1RRFMQI/AK4FOoDtiqI8qKrqgQnHtAKfBy5WVXVYURSZEj0LyFkY168DFOjcyZB3Cy2ZRkimomO7GDARD1W3W3OrGI/EhXGZPS7IFQWWXpfbGuYhukZCgxDGagw8XfN6sqNEIpEw2su3Rj/L+tHD8CWLKMw0Xyh6Uc4x776gheaK/KwcTpuJsWCEWEzFkEvjXsNGUZhSVSIxlf1dbu7YouN7YOiEaN4+R9aT2Yiey5ktwFFVVY8DKIryG+DNwIEJx3wQ+IGqqsMAqqr2zfRCJbmTszC2OqBqGXTtYsi7LncrRdvjoBhh0ZUAomKcg8d4xC+SMLTJQRKBxWQgHFWzf1BOzDKWwlgikcxXevcTu+82lqn97Gp5HxuayiAShE13zYqpqB+7Kv8Gf2eRGVUFbyiCY/Lgkkw0bITd94O7nSO+MgLhWPZECoCe3VC3Ju/1zkf0WCkagIkZUB3x+yayFFiqKMqLiqK8oijKDTO1QEn+5CyMARo2onbuZCwYzj3D+Mjj4oq9SPiq7NbcUim0dZalmntfwFhM4s80q8/YGf+zHO0+wyuSSCRnC1VV+dUrp+jzBM71UmYHJ56He68nFo3w9tD/48Tazwj7xI1fh6ql53p10yYxFjpXn3FjvAGvYzt79DbeRYLQd1CkdUkS6BHGqS6/Jo8lMwGtwBXAO4GfKYoy5V9EUZQPKYqyQ1GUHf39OieySfImL2Fcvx7F208DA7mNg/Z0iZGUE2bTF1tMBCMxInoaxxi3UkyZYlTgWE0iozOYzWfsqBO3nq4zvCKJRHK2OD7g5V/+vI+/vC7/rjn9Kvz6dihtYM8Nf2C/upAap+1cr2pG0arEOWUZA9ScB1YnHH+Og90eii1GWsqz2CH7DkIsIoXxJPQI4w5gYrtiIzD5L7QD+IuqqmFVVU8AhxFCOQlVVf9LVdVNqqpuqqqqynfNEp1osTHphnxEYyqB8CSrQ+NmAM43HMzNStH2hLhden3iLnt82o5v8mukwZ1qipEkUTHO6jO2OcFSIoXxDBEIR9lxcgg11XhyieQssbfDDcCQL3SOV3J22NMxwg+eOTq14bjrNbjvVnDUwnsepD0qGrJrnDkUcOYAzoQwzrFibDTDosvh6JMc7PawrNaR3aPcvVvc1korxUT0COPtQKuiKAsVRbEAtwMPTjrmz8CVAIqiVCKsFcdncqGS3LGZjVhNhrQV4+8+cYS3/ODF5Dvr1hIobuANxleo1JtK4R+BV34EZc1QtTxxd3F8OIjeZIoRX4giszF5ipEEq2al0JNM4ayHUSmMZ4I/7urk1h+/zNcfPaRfHLs74eUfQPv2M7s4ScGwt1MI42FvYQjjLz98kG89dpj3/nxbIqmI0R741S1gK4P3PgiOGvo8QQBqSudbxThupchlp1djybXg6STcs58Vdc7sx3fvFlXmApkGrJeswlhV1QjwMeAx4CDwO1VV9yuK8m+KomjzeR8DBhVFOQA8A3xGVdXBM7VoiX5KM4yFPtTj4VDPKGMTfcCKwum6G7jUsJdK41j2F4gE4bd3wuBReNP3kxoftIpxjyfA1x89xMd+vWtqhXoCI76wbLxLgTVRMdYhjB11smI8Q5wa9ALwk+eP84U/7SUayyCOO3bCL94E310Fj30B7r8dxmQPsmT6aBXj4QKoGJ8a9LLtxBCXtlay49QQt/zoRTHs4vlviTHPd/4h0WTc6wlQZDbmPp11lqMJ45wrxgBLrgFgU3gXy/UK49o1BTk4KxO6fhuqqj6iqupSVVUXq6r6lfh9/09V1Qfj/62qqvqPqqquVFV1taqqvzmTi5boJ9NY6N74FfeJfm/S/fvKr8WsRKlpfyzzk8di8KcPw8kX4C0/gkVXJD1cHM9wfMdPXuYnzx/jr3u6+cwDe9JW39z+sPQXpyC3inEDeGTz3UzQMeJnQYWdj125hPu3tfPx+9Nc2A2fFFu8/Yfh8s/Bux6A4Cg8+HExolYiijk29AAAIABJREFUyZNoTGVflyaM538++R92daIo8M1b1/CrD5xPryfIzx96Bnb+jxjaMaG5rnc0SI0zj+mssxzNSqh5jEd8IX1xnQClDYyVLuUKw25W1DoyHxuNQO9+6S9OgbxMmOdkEsZ9o6LL+fhAcmX4qLKAo2o9tsN/yvzkz34V9v8Jrv0SrHn7lIfry8SkuvXNZTz0sUv47A3LeGh3F99/+mjKpxuRwjglllwqxs46GOsRmZ6SadE57KfBVcQ/Xb+M/3PTCh7Z28MdP32FwbHg+EHBMbj/DlCj8P5H4MrPiwbUa78IR/4mvtAlkjw53j+GLxTFaFAYmcMV4/1dbm77ycsZG8piMZU/7OzgkiWV1JUWccGiCja2uLi086dgMMFln006vtcdoHqeNd5BcipFIBzluu8+z7f+dlj3+Ucc57PZcIhl2WZiDbZBxC+j2lIghfE8J50wjsZU+kfFF/yxvmRhPOQL85TxUpRTL6Xflj/yuNjeWn8nXPTxlIesrHey7f9czf0fvIDzGkr56OWLuWV9A9954giP7++ZcrxbWilSoqVS6KoYO+pEl7FXpr5Ml64RPw3xi7sPXraIH75rA/u7PNzyo5fE9m4sBn/+KPQfhFv/GyoWj5+85cNiB+WxL8CQbLeQ5IfmL17XVMaQd+5WjLefGGLbiSF2nBxOe8wrJwbpHPFz68bGxH0bi7q5IvQsbPmQuOifQO9oYN4lUoD4vLeYDHgCYR7d103faJCXj+t3pr7AOixKFEf3K5kP1BrvZMV4ClIYz3PSCePBsSCaZfLYQLKVYtAbYlvxlYAqKsKTGT4Ff/wg1K6Gm76dMVC92mFLbHUpisJXb1lNU3kRv9nePuXYEX+IsqIcs5MLAN2pFDCeZSx9xtMiGInSNxqkoWw87uim1XXc/6EL6HEHuHfrCTj0EBx8EK79N1hydfITGAzw5h8KK8Vz3zzLq5fMF/Z0uLFbjGxqcTHiC83ZhBTNBrLzVHph/MDODhxWE9evqk3c98bB/2ZMteHZ9LGkY1VVpdcToMYxvxIpNJw2Mx5/hF+/KsY1H+oZxa9zWNbDIy0EFBscfSLzgd27wVQEFfkPI5mvSGE8z3GmEcZ98Wqx2ahwfJLHeMgbwu9cKK4kd/8Gwv7xB4dPiWY7VYXbfgnmopzWYzMbWVbjpHPYP+Ux2XyXmtw8xjLLeCboHhE2o/qy5IrUhmYXaxvL2N0xAq//WlToL/i71E9S2gCbPwB7fgeDx870kiXzkL2dbs6rL6WixEIkpiY3Ss8hNBtIOmE8Fozw6N4e3rC2fjyVqGMnCwee4aeRmzntT/47FDaDGLXzLJFCw2kz8drpYbafHGbLwnKi8RHP2fCHohwdDNHh2gJtT2bucejeA7XngXF+NS/OBFIYz3OcRWZGA5EpHfW98SlK65rKODEwRmzC44NjQSpKrGK8Zs8euGc9bL8Xnvka/GCLSKC45b+gfFFea2p0FdE54k+qfgTCUYKRmMwwToElF2HsqBe3cvrdtOgcERduDa6pF35rGkvp6WpHPfokrLkNDBniBS/6B5Ev+sJ3ztRSJfOUSDTG/i435zWUUmYXO2kjc7QBT6sYv94+knLg07f+dgh/OMrtmyeMTHjqi0RsFfw8eqOwLk1AmwI4Hz3GIHzGh3pGsRgNfOnN5wHid5eNI72jxFQILLwW3Kfh4EOpD4zFxHe7tFGkRArjeU5pUeopOlrF+MJFFQTCMbrc4xXcQW9IjIPe+D5438NQ1gIP/yM893VYdhN8bDssy3/qd0NZEWPBCB7/ePUjMQ5aVoynoHvyHUBxlWhUkRXjaaEJ48ayqZOjVjeWcr36IkosAmtuz/xEjhrY+H7Yfb9Ir5BIdHK0f4xAOMaaxlJccWE8NEezjEfin+/+cJRDPaNJjz11sJdfvHyKuy5eyNqm+MDc48/CiecIX/QpvBTRPpwsjLVEpXlrpYh/b9+4upZltQ4ayop0CeNDPR4ASra8G+rWwUOfEBnQkxk+IeLv5GCPlEhhPM9JNxZaqxifv6gCgGNxO0UoEmM0EBmferfgErjrb0Ig3/00vP2/EzmS+aJV4TpGxj/stEqI9BhPJaeKscEgs4xngM5hP4pCyq3atY1l3GJ8gSHnCqhZmf3JLv6EuFiRVWNJDmj5xasbSykvFp/jczXLeMQXorW6BEi2U/R5AnzmgT2srHPyuRuXiTtVFZ76EjgbKbrwg5QWmWkfSrbead9f87H5DsaTKd51fgsgdnb1COOD3aPYLUaaq8rglp8KG+Rf/j7ZUqGq8OS/gmKElovOyPrnOlIYz3PSC+MgFcUWltaIrMPj/SKZQvvgTRoHrShCIDdunJE1aZ3+E33GmgdNVoynYjHm0HwHQhjL6XfTonPET7XDmrgomUhL9BRrDCd4qfhafU/mrIMN7xaeZN/QDK9UMl/Z2+mmxGpiYUXxPLBShFhV76TGaWXXaSGMVVXl07/fjT8U5Z53rk/sjHH4EejcAVd8Dsw2msqLOD3JStGTsFLMz4rxuqYyLl5SweYFInNtbVMpHcN+BiZGRaYgaRR01VK47ktw9El48T+EfQLghW8Li8V1X4JK2XiXCimM5znphHH/qMiArCyx4LSZOBYXxh3xLSvd46DzQKsYa9vVML7VJnOMp2I155BjDGIstKwYT4vO4fGotskoe35LFAP3+zfrf8LVt0EsDMefmaEVSuYzLx8b5KHdXaxtKsVgUBJWijlbMfaGKbNb2NjiSlSMH93XwwttA3zhpuUsiVeTCftFxGHlUlh7BwDN5fYpVoo+TwCHzYTdMj8bxz502WLuu/uCRKLTuiYhkHdnqBqrqsrBbg/LaydMvNt8Nyy9EZ78/+DHF8MzX4WnvyI+j9I1DUukMJ7vZKoYVzvE1KBFVSWJZIrf7+jAZjZw4aLKM7amimILNrMhqWLslsI4LeMV41yEcbecujYNutx+GlxT/cVEI7Dnd5wsu5BX+0wZR5wn0bgJbGWiU1wiSYOqqvzshePcee+rlBdb+Ld441VpkRlFgeE56DEOR2OMBiO47BY2NLvoGPbTPuTja48eZHmtgzvidgEAtv6H8OLf9O1EWkKTy07HkD+pQbzXE6R2ntooUnFegxOjQclop+gfC+IJRFhWUzJ+p6LAO/5X2CpiEXjuGyJm9Y3fyxizWuhIYTzPSSeM+0YD1MS3oRZXlXCsfwy3P8yfX+/kLesaKD2DlgZFUagvK0qqGPe4xdZYxRmsVM9VcoprAyGMw14IZI/3kUwlFlPpHgmkrhgf+DOMduFeeQeRmKjQ6MJghMVXiW3NmM5/R0nB8dvt7Xz54YNcs6KaP//9xSyuEiLHaFAoLTLPybHQmv3DVWxmY4uofH7qt6/TPuTnX96wEqMhLtAGj8HW78J5t8KiyxPnN5XbCUVjiYZxmL/DPdJht5hYWuPIKIwHRsVF05SkDqNJpOf83Stw5x/g3X8CS4qLfkkCKYznOZownpgAoU290z5YFlUV0+sJ8suXThIIx7jzgpaUzzWTNEwSxvu73CysLJ63W2PTQVEULEaD/oqxI55lLCPb8qJ/LEgoGqNhUoYxqgov3QMVS6jd/FZgfDKZLlqvBW8f9O6dwdVK5hOvHB+kxmnlx3duxGFLLk647JbUVgpVhb99QUwiDei8UJsB3L4w7773VU5MGhA1mfH+EQur6kuxmAzsODXMNSuquXhJfGdSVeHRz4LRAtd/Jen8pnIh4ib6jPs8wXnrL07HuqYydrePJFXOJ6IlliT1B03EYIQl10DxmdsNni9IYTzPsZkNWIyGpIqxNvWu2jFeMQb40XPH2NBcxnkNpWd8XY2uoiQrxf4uDyvrnRnOKGysJkNuFWOQPuM86RhOk2F88gUxLerCj1FXZqeyxMLu9hyE8ZJrxG1blolUkoLlUM8oy2udCW/pRMrs5tTNdydfgFd+AE9/Gb63RlRdYzotPtPgtfZhXmgb4Fcvn8p4nNY/4rKbsZgMrG0sxWRQ+MJNK8YPantC7KZc+QVw1Cad3xwXxlqWcSymxnc8C6diDLCuqRRPIMKJwdQXIoNeUVGvSCeMJbqRwnieoyjKlOl32paUtuWyuKoYAF8oynsuXHBW1tVQVsSgN4Q/FMXtD9Mx7GeVFMZpsZgM+lMppDCeFl3acI/JGcYvfR/slbD2dhRFYU1jGXs6skcoJSipFoH6R6XPWDKVcDTGsf4xltc6Uj5ebrekzjF+6fsiv/z9f4PGzaLR6vX7zuxiGa/gPri7K+XQDg3NF601EH7uhuXc8871LIoXZMTY9G9AaTNs+eCU8+vLbCjK+OsN+UKEo+q8zTBOh1awOtQ9mvLxrBVjiW6kMC4ASotMeCYIYy0DUqsYN1fYMRoUKoot3Li6NuVzzDQTkykOdIntv5V1UhinI6eKsbRSTOH7T7WxtW1A17GaxSdpHHTfQWh7HLZ8KDEGfXVDKUf7x/DmMqZ3yTXQvg38OQhqSUFwYsBLOKqyLI0wLrNbEraEBIn35Yeh5UK443diaMOL95xxL/vpQSFUB8aCvHRsMO1xiYz6eN/KpgXl3LS6bvyAY0+LeLZL/1FMiZyE1WSkzmlLJFPM9wzjdCQq55MSOjSGvCEUhUS0nyR/pDAuAErTVIy1Dxarycgb19TxiWtax7MkzzBaNa5zxJ+YAb+q/sxbOOYqFpOBUIaqTBImK9grwNN5Zhc1RzjWP8a/P3GEz/9pD2Edv8POYT9OmynZ4/niPWAqEvFHcc5rKEVVxRhW3Sy5FtToWYtt+92Odn72wvGz8lqS6aFNhEsnjF32FM13L/9n/H35AfH/FQUu+SQMtsHhh8/kcjk15KOlwo7DZuLPr6f/rNF80a5Ugk1V4blvgrMB1t2R9jkay+0JK0WfNvUuxfCd+YzDZsZlN0/JdNYY9IZw2S3jzYySvJHCuACYLIy1K+6qCVtR/3H7+rNmowDhMQYhQg50eah2WJPWI0nGajISDOdQAdIi2yT8fkcHAO1Dfv6wsyPr8Z0jk6La+g7Cnt/ApruguCJxt/Z+zWlMb+NmsJWetdi2n289wf++ktkDKpkdHO7xYDQo45m+k3AVW/CHo+MRgaM9sOd3sP5OsJePH7jizeBaIKLPzmBkY/uQj9bqEm48r5bH9vXgD6W2eg37wpiNCnZLiqLLyReg/RW45FPigj4NzeX2xPQ7rdmv0CrGIBoR29MI42FvSNooZggpjAuAVBXjimILZuO5++evcdowGRQ6R3zs7/JIf3EWcqoYAzjq5fQ7IBKN8cddHVyzopq1jaV8/+mjWS0pXSOThns8+UWwlMCln046zmXXxvTmEKFlNMGCS+HUi/rPyZNAOEpb3xh9o0FUmWk96zncM8bCyuK0u3ZThnxs+ylEw3DhpEENRhNc9HFhTzhD7zNVVTk95KO5vJi3rGvAG4ry5MHelMeO+EKU2S0pGwp57ptQUgvr353x9Zpcdno8AXa3j/CdJ46wqt5ZUDnGGpmE8aAUxjOGFMYFwBRh7AlMzTo8yxgNCrWlNo73eznaPyYTKbJgzaX5DkTF2C2tFM+39dM3GuTtm5r45DVL6Rzx80C8atw54k/ZPNc57E/saHDqJTjyKFz8iaRqMTBhTG+OQxcaN8PwCfDq8zzny6GeUaIxFV8oylguPmjJOeFwryetjQImXIh5w6ISvPd3wrNevmjqweveJRrytv7HGVnrwFgIXyhKc3kR5y+qoMZp5S9p7BTDvlBi7Ukcf05UjC/5JJgzfx81lYu/x3ff+yolVhP3vndzQVoGmlx2Okf8RFNEtg15QzKRYoaQwrgAcBaZ8QTCifxDberduaahrIitbQNEY6r0F2fBkkvzHUBpI/iHIJQ5Y3S+8/sdHVSWWLhqeTVXLKtiXVMZ9zzVxrt+9gqXfONp3vyDF3l077jl5Fj/GKPBiGi8U1V44l9FRSvF+FSnzYTRoOQ+prdpi7jt2D6dHy0r+yZkLE8cjiCZfYwFI7QP+Vlek14YJ12I9eyBkdOw8s2pDzYXiffs0Sfg5NYZX+/pIfG50lJRjNGgcPPqep470p/Swz/sC09tCFNVES/nqIeN78/6elrjWSSmcu/7NlFbYP5ijeZyO+GoSk/cDjmRIVkxnjGkMC4ASovMqCqMxqtGE6fenUsaXEWJNUkrRWZEXFsOwrgsPqRlpP3MLGgOMDgW5MmDvbxlXQNmowFFUfj0dUvp8QRoH/LzyauXsr6pjE/89nW2nxzicM8ot//XK5TZzVy7shYOPQwd2+DKz6ecFKUoCmX5TCOrWweK8YwLY62pFcYbliSzE62BM2PFuFhUXYd8ITj4ECgGWHZT+ie94KNQ2gR/++cZzzXWGsC04RuLq4sJR1UGxqa+z9y+8NSKcdsT4m/r8s9krRYDLK9zsqnFxQ/etaGgiyha5XyynSIaUxn2yYrxTCHHjBUAzsT0uzAlVlPS1LtzSWPcx+mwmmhyyRGVmcgprg3ApQnj01C9/Mwsapbz59e7CEdV3r6pKXHfpa1VvPqFq6l2WFEUhfdc2MLbfvwSd/9iB4oCFqOB3334Qha6rPCbL0JFK6y7M+1riKELOVaMLXaoPe8sCGMPNU4rvZ4gfaNTK0yS2cPheCLF8tr0BYLyhMc4LIRxy8VT7D1JmIvg2i/CA3eJXOMN75mx9Z4a9KEo403U1Q7xfdLnCVJXmjwYZ9gXYr29bPyOWAye/pJoEMziLdYosZp44KMXzcja5zLNE6YAXrBo/N9+xBdCVWWG8UwhK8YFgDYW2u0PM+hNnnp3LtGyjFfUOTEUoF8sFywmY44V42ZxO1K4iQSvnR6mudw+pQpX47QlGoFcxRZ+8f4tWEwGnDYzD3zkIpbWOISQGDgC1/yraGZKg8tuEZ7PXGncAp27ztiEsnA0xqHuUa5cVg3IivFs53DPKHaLcdzbngLNjqD0H4H+Q7DiTdmfeNUt0HQ+PPWlGR0XfXrIR63Ths0sGgW175P+SZYdVVUZmWylOPSQsIJc8fmUucWS9NSXFWFQoGNSxTgx3KPk3H+vzwekMC4AJgpj7QvyXDffwXiWsWy8y07OFePiajBaRcW4QHH7w1SUZK+gNJXbefJTl/O3T15Kc4UdQj549muiSW75GzKeW2a35O4xBvHcoTERBXcGaOsdIxSNceHiCmxmg6wYz3IO9XhorXFkLBBYTAaKLUbqu+MjxZffnP2JFQVu+Bp4++DFmWvEOz3oS1QvAarj1rzJXnZfKEooGhu3UsSi8PRXoHIZrH77jK2nUDAbDdSVFk3JMh6MC2NppZgZpDAuADRhPOIL86fXROdw3SxoXlhUJRo3Ni1wneulzHpyGgkNYDBAWVPBC2PtvZ+NUrsZuyVeGX71x2Jq4DVfFMIiAy67OTHZKycaN4nbM2Sn2Bf3F5/XUEq1wyab72YxqqpyuGc0Y+OdRpndQuvQs9CwCUob9L1Aw0ZY8UbY8XMIz8wFkohqGxfGFcWaME5+fu2iUZt6x94HYOAwXPkFMJydYVLzjabyItqH/Un3yXHQM4sUxgWAJg6+9NcD3Lv1BLdtamR1w7lvYKgvK+LZf7qCmyeOB5WkxJpr8x0IO0UBWylGfPqFcYKAW0RcLb0BFlyc9XBXcZ4V4/JFYjphx47cz9XB/k43xRYjCyuKqXZYE0N9JLOP/tEgw75wxsY7jWW2EZoCh2Hlm+gfDSaGXWRl893gH4YDf5nmasEfitI3GqSlYlwYW0wGyostU6wU4+OgLSJz+dmvQu1qfTYQSUqaU2QZy4rxzCKFcQGgiYO+0QD/8oaVfONta1KHrZ8Dmsrts2Yts5mcUykgLowLu2Jclqsw3v0bCLrh8s/pOrzMbiYYiaWd+pUWRRF2io5tuZ2nk/1dHlbWC+9+jVNWjGczR/vHAIS3PQuXsROA6NKbec/Pt/HR/92p70UWXAbli0XVeJq0DycnUmhUO6xT3mdJ46Bfvw+GT8JV/yJ2tCR50eSy0zcaHJ+ACAyNxX/PUhjPCPLdWQAUW018/Kol/OKuLXzgkoVSiM5BrCYjoUgstwlmZc3gG4Tg2Jlb2CwlFlPxBHKsGKsqbL9XbD03bNB1ypRpZLnQuEk0+PmHcz83A9GYyoFuTyLWqsphpV82381aTg4IobmgMnsyz5rofvqUCn5/3MzBbo/+nQCDQYw0b38FevdPZ7mcGhTrbakoTrq/KoUw1irG5ZaomHLXuBlar5vW6xc6zfFKfcfweNV4yBvEaTOd02m28wn5WywQPn3dMi5trTrXy5DkidUk/lTD0VyEcTyyzV14WcajwQiqOh5VqIuTW4X/cfPduk/RKtJ5N+ABdOqs+unkxIAXXyiayAavdloZDUbwheT0u9nIyUEvlnhTVUZUlVb/HrbFlvPtJ9oAsSsSSzEFLSXr7hANudOsGmuNX82TKsbiAixZqGtRhnV7fwieTlEtloWZadHoGo9s0xj0hqiQiRQzhhTGEskcQBPGOTXgJSLbCs9O4Y5XqnKqGG//GdjKYNVbdZ8yPo1svAHvnqfa+P5TbSmngCXRsBFQoH36DXiBcJQ3fn8rF37tKW77ycuAaLyD5IzZKUQj4J86Flty9jgx4KW5wp59xPHgMRyRQV6MLGdgLMhb1tUTU8ET0Nn8aS8X7+3dv53WLtLpQS8Oq2nK0I5qh43+sWDSrtawL8xlht3YX/muyANfdHnerysRaBck7UPjDXhD3jRjtyV5IYWxRDIHsMSFcU6RbYUsjP0Tmn70MNoDh/4K6+8UgxF0ok0j0yrGqqryX88f59+fOMKtP36Zk5mao6wOqF45I8kUHcM+9na6WVBRzDUrqvnYlUtYFves1qSJ0kJV4ffvhW+0wH9uhgc/fsbi4yTpOTXoZcEkW0LqA18EYFtsObesb+DyZWIHMN3kxaN9Y/w5nkKUYNNdEBoVA0Ly5PSQL2VvSLXDSjiqJl0kRkfa+Z75hyjVK+Gmb+X9mpJxKkssFJmNSRVjMQ5aVoxnCimMJZI5wHjFOAdhXFIDJltBJlNowlh3xXjXLyEWEcIhB1wTp5HFX3csGOGq5dWc6B/jpnte4Fh/hupc4ybo3CGmgU0D7ef9yBWL+eata/mn65clMnETFePJWcYv3SMuBta8A1wLYd8f4Td3QCQPW4gkL2IxlVODPhbq8Bdz6iXCtkpstcv5zA3LEhd9WlTXZL712CE++4c9yXc2bobiKjj+TF5r3do2wN5OT1IihUaVY9IFWCzGm4/+P6xKGG77Zcqx6pLcURRFRLZNtlLIxrsZQwpjiWQOkFfFWFGgtDCzjEf8QizoEsaqCrt+BYuuhIrFOb2Ols86EhcnHfF80ds2NXLf3RfgC0XZ05HBqtC0RUTEDR7N6XUnk4jFSvHzalPJkqwUJ1+EJ78IK98Mb/0JvOt38Pb/gaHjsP2n01qLRD/dngDBSGxKI1tKTr2IedHFPPyJy6grLUqMiE41kjwQjvL8kQFCkViypcdggIWXw/FnxfteJy8dHeCa7zzHnfe+SjQW4/YtzVOOmTL9rv0VFvn3cm/xB6Fyie7XkmSnyWVPVIxVVWXYG6JcxzAjiT6kMJZI5gBWkwjDl5Ft+hi3UugQxh3bwX0a1t6e8+tYTUbsFmOiYqwJ44YyOzWlQih4gxl84VoD3jRj20YyeKrL7GYsRgO9WsXYOwAP3AWuBfCm/xxvhmq9FpZcA89+A7yD01qPRB+n4labhZVZhPHIadFE2zKerT15t2IiLx0bwB+P8/JNjhJcfCWM9eZkm/ntjnb6x4J89x1refnzV3P50qmN3No01cTOxIEHCWFmT9nVul9Hoo+meJZxLKbi8UeIxFRZMZ5BpDCWSOYAFmMeFWMoeGGsq2K89wFhOVl2U16v5bJbElW7zhEhjBtdRYlJehnTICpawVaa1Wc85A3xgf/ZTl+aeK5MFwKKoiRHtm39Lnj74bZfgG3SOPbrviJGVT/71YzrkcwMJwaFMF6QTRifFP7iicK4rFibaDq1YvzEgb7Ef095/y2MN8Adf1b3OvtHg7RWl/DW9Y3YzKkn1lVPtFKoKhx8iG3G9RSVlOl+HYk+1jaV4g1F2dPpZtAr/q7l1LuZQwpjiWQOYMknlQIKNsvY7QtjMRnSfokniEVh/59EtupkkaiTMrs50XzXMezDbjFSZjdTFH/tjBVjg0GM980yAe+Ftn6eOtTHtpNDKR8fiQtjhy31hUC1M54xO9YvsprX3CYmkE05cDls/oCI9Oo/knFNkulzcsCLxWSgLl5tTcupF8UFVPXKxF0OqwmTQZkSFRiLqTx5sDfRlzClYlzWBBVLcvIZD4wFEx7idBRbTdgtRmHZ6dwFng4ejW6RaQlngKuW1WA0KDy2v0eOgz4DSGEskcwBrPl4jKFgkyncfp3DPU5uBW8fnPe2vF/LZbcktrM7h/00uopQFAWjQaHIbExsaaelcTP0HYDgaNpDDnR5AOhNM6jD4w/jtJnSRn4lxkK//H2IBuHST6dfz2WfBRTY85vM65ZMm5ODPlrK7YlGybScegmaL0qaGKcoCmV2M0PeZCvFnk43/aNBrllRA4Av1YXZoitEFVpno2X/aJBKHTm51Q4r/WNBOPgXVIOJvwbX6E+Gkeim1G7mgkXlPL6/Z8I4aJlKMVNIYSyRzAEs+aRSwPiQjwIUxrrGQe97ACwl05rGVWY3J6wMnSN+GsrG496KrUa8wSyDNRo3gxoTVbY07E8I49RWihFfKKMAqXbYCHn6YdvPYNUtUNmafj0lVdByERx6OPO6JdPm5IA3u41itBeGjol/k0mUTbDxaDxxoAejQeGm1XVAGivPoisg7BWJKFkIR2MM+8I6hbGNPrcfDjxIsOlS3GoJFbIp7Ixw/apajvV72RHfRZLNdzOHFMYSyRxgWs13UJDCOGvFOBKCAw8Kb/E0oqRExXg8lUKbTAVQZDFO3cq8vl/WAAAgAElEQVSeTONGcZvGZ6yqYsQzQI87jTDO8vPWOK28PfIgatgHl/1T5vUArHgj9B+CgemlZUjSE4upnBryZW+8a39V3DZfMOWh8gnvPY0nD/SxeYGL+jJhz0j5/ltwCSgGOJbdTqFt1WezUgBUOa043Ydg+AQHy4SXeUOzK+t5ktzRdgT+sEtkVcvmu5lDCmOJZA6Qt8e4pLogs4xHfDqE8fFnIDACq2+d1mu54hVj7X8NrgkVY4spe8W4yAWVS9MK415PMCFO0lWM3f5wxgSOBluI9xofx7/kZqhekeUnYrwR8dBfsx8ryYtuT4BQJJZ9uEfHNjBaoG7tlIfK7OakgRrtQz4O945y7craRPOnN1XFuMgF9et1NeBp8Wt6KsZVJVY2+V4AxcAffespL7awsi4/774kM/VlRaxpLGXIG8JuMWbvp5DoRgpjiWQOkLfHWFFE1Xj45Mwvahbj9ocpzdb0s//PoqFp0ZXTeq0yu0U04ceruhOtFHaLDo8xCDtFx/aU2bL7u9wA1JXa0gtjXxhnhguBNb1/xKH4ObnyI9nXAqJBq26ttFOcQbSpiAtSDMtIon2bELGmqcLUNalirL1XNi9wYbcIoZR2x2LRFdC5U+RoZ6B/TAhjPRXjaqeVK9RtRJsu5NETES5eUpndPy3Jm+tX1QKy8W6mkcJYIpkDJIRxNI8JaRVLYPDYDK9oduPJZqWIRuDIo7D0BjBN70tFGwu9r1MIjMaJFWOrjooxCGHsG4T+w1MeOtDlQVHgimVV9HqCqCnEc0ZPdThA85Ff8Hx0NafMOQxaWP5GUa0c7dF/jkQ3JwZ0RLVFgtD1mhgEk4KyYjPD3nDiPaHlaDe67BRb43GB6d5/Cy4FNQqnX824zoF4xbhKR8V4gXGAZYYOjpRdxsBYkEtbK7OeI8mf61YKO4W0UcwsUhhLJHOAhMc4nKcwHjouosnmEO1DvpQZrdmIRGOMBiOZhXH7K+AfhuU3T2OFAq3pTRPGE60Udj0eYxDWBcUIu3895aH9XR4WVBSzqLIEfziKJ5AsdFRVZSSTlWLPbzH7+/lJ9A1pK84p0X43hx/Rf45ENycHvFhNBmozRbV174ZoCBpTC2OX3UIoGku8x7pGAhSZjbjs5kTF2Jvu/de0BQwmOLU14zq1inGlI7v4ah0Recv3DS8HkML4DLOkuoTW6pKkzxzJ9JHCWCKZA1imWzGOBsXkrDmCqqrc/l+vcNf/bE9ZIc2EJhwzplIcehiMVlg8/alc2gSyfV0erCZDUmXNbjGl9nhOxlEjqtev3w/R5PitA90eVtY5qXZqo52Txa03FCUaU1NfCMRi8NI9qLVraLNv5N8fP8LvdrTr+51Wr4DyRdJOcYY4OeijpSJLVFt7fCJimoqxlhE8nBgw46MhHhdoNRkwKOBPJ4wtxVC/QUTBZWBgNESxxZjwLGeirvdZjsXq+O1xC0uqS6grlYLtTKIoCv979/l8+S0pMskleSOFsUQyB0g03+VTMdaiueZQwsCpQR+dI352nR7hoT3dOZ2bmHqXroKqqqKpbPGVYC2Z7lIT4uRY/xgNZUKUaNgtxtQ5sqnY8G6Rqdz2eOIuTyDM6SEfK+udicpizyRhrFXVy4pSVPQOPwKDR1Eu+SQPfPRiVtQ7+ewDe/jgL3cQyOZ9VhRRNT7+XMaMZUl+nBz0Zm+8a39VRC46alM+rF2UaQ14E+MCFUURzZ+ZLswWXCysGiFv2kMGxoJU6vAXExyluPsVnoptIBxVZbX4LFHjtEmP8QwjhbFEMgcwGhRMBoVQNA87REXcVzo4d4SxNuGt1mnjG48eyi7iJpB1HHTvPhFfl+cI6MloVgpVZcqWZrHVpM9KAbDkWiipgV2/Stx1MJ5fvLLeSU1cGE8e8qGJoikXAv2H4ZHPCGG14s00V9j5zQcv4J9vXM6TB/t4dJ+OC47W6yEWhhPP6/sZJLoIhKOcGPDSWpPhwkxVhTBOUy0GcMUFkVYx7hoJUD+h+bPIYkxfMQZouQRikfFIuBQMjOkb7sGxZ1CiIZ5TNwBwWWtV9nMkklmIFMYSyRzBajLkVzEurgJrKQy2zfyizhDbTgzhspv5zjvW0jni596tJ3Sfq1VQ0wrjQw8DCiy7cQZWStLEucZJwlhLpYjGdFgXjCZY+05RMY43vGn5xavqJgrj5IqxJ9WFQMdO+Pn1ornq9vvEcwMGg8KHLl1EfamNv+7WIYybzhcDUI4+mf1YiW6O9I4SjamcV1+a/qCR0zDWK/4N0qDtVgx5Q/hCEYa8oanNn5mEcfP5wtt+8sW0h/SPBnU13nHkMbCVctK+GrNR4fxF5dnPkUhmIVIYSyRzBIvJkJ/HWFGgYvGcqhhvPznE5gXlXLS4kutX1fDDZ47SN6qvcWy8Ypxme/HQX4XYKKmekbUqipLwM08c7gEkGqB0RbYBrH+3ELOviya8/V0eKkusVDttFFmMOG2mKUM+RuI/b6L5bv+f4RdvFFF0d/0NapP9hwaDws1r6ni+rT/xu0qLyQILLxfCOEevtyQ9+zrjFzyZhLGWa52hYlw2wUrRNSISKSbHBaZNpQCwOkQs36n0wlhYKbJs1cdi0PYYLLmGpionFyyq0OVJlkhmI1IYSyRzBEu+FWOYU5FtvZ4ApwZ9bFkoKk6fvm4Z3lCUJw/06To/ZQVVY/gk9OydkTSKiWiidKIoARLiIKM4mUjlErpL19P19I95389e5NnDfaysHx+QUDs5y3jwGLb2rVTipkzxwR8+CL9/L1Qtg7seE81zKbh5TT3hqMoTB3qzr6n1GlG9nEMXVrOd/V1uHDYTTeUZmtPaXwVzMVSvSnuIdkE27AslotpyTkVZcLHIMw77pzykexx01y7w9sPSG/jBHRv4z3duyHy8RDKLkcJYIpkjWE3G/CrGIBrw3O0Q8s3sos4A204If7EmjJdUlWC3GGnr09cAlvDcphLGr90HKLDyzTOyVg2tCWqylaLYmmXIwiRUVeXffTdTr/ayZfhhwlGVq5ePV7ZrnBOE8dGn4EcXcdW2u9lh+yg1P1kF+/4AV3wePvB42oYtgLWNpTS6ivjrnq7si9KSO9qe0PUzSLKzr8vDqnpnUqPmFNq3QcOGhA0mFSajAYfNFK8Yi/dFcsXYhC9bKkrLJSISLsXkxcExneOgjzwmRkwvuYaKEmv24ToSySxGCmOJZI5gMRlyHwmtUbFY3A4dn7kFnSG2nRii2GJMjJI1GBSWVJfQ1jum63y3P4zdYkwkeSSIhmHXL2HJNeBqmdE1a1vak5vvMo7lTUFb3xgPjK6gz7WBv1MeYPfnL+a9Fy1IPC6EcVAIkftvh8pWfrvsO3w5+h7YfDfc/QRc8c9gzCxMFEXYKba2DWTPina1iJHV0mc8I0SiMQ51ezL7iyNB6N0vhHEWyovF9LvOER9Gg0L1BBFbbDVm9hgDNF8AKCl9xgNjOsdBH31CDKmxS1+xZO4jhbFEMkewmgy5j4TWqIhHts2BBrztJ4fY0OLCZBz/eGqtduiuGLvTTb078jcY64FNd83UUhO47GbMRoVqR/KwhqxjeSfx1ME+QMF47RdF49WrP056vNZpY4X3VdTfvAuqV8J7HuR162b+Ynszyo3fgIaNutf8htX1RGIqj+3XMdluyTXCh5piu12SG8f6vQQjMVY1ONMf1LtfpIHUr8/6fGV2C0PeEJ3DfmqdtqS/myKzKXMqBUBRmfChp0ge6dcjjMf6ReTbkmuzrlUimQtIYSyRzBFExThfYRyvGM9yn+iIL8ShnlG2LEiuPLXWlNDrCWZvFkM0o6UUxjv+Gxz10HrdTC03wds2NvKpa5cm0ik0EhVjnR7jpw/1sqreScXKy2DpjbD1e+AbSjy+xNjL90zfJ1KxDN7zF7CXM+LLMA46A+c1OGkut/NXPTnRS66GSCBjeoFEH/u7xITEjBXjrtfErQ5h7LKbE1aKqXGBRn27FUuvF9Mgx/qT7u6Pj4OuzmSlOPa0uF0y/WE5EslsQApjiWSOYJ2OMLYUg7Nh1g/52HFyGBj3F2ssjee9HtVRNU5ZMR46Ib7AN7wno2czXy5YVMHfXbFkyv25eIyHvSF2nhoe9xRf/S8Q9MAD7wdPF4R8XL33n4ihcPSqH4lKH+LnTTsOOgOKonDZ0kp2t49kP7jlYjDZpJ1iBtjX6cFmNrCoKkOGcffrUOQSGdRZcNk1K4WfxknNn0V6R5KvfAuoMTj0UNLduqwUR58EeyXUrcv+OhLJHEAKY4lkjmAxGRNWiq1tA/oapyYyByLbdp4exmxUWNtUlnR/a7UDQJfP2JNKGO/6hYit2/CeGVurHoq1VAod4uS5I/3EVLhqRY24o2YVvOE7cPpV+MH5cN+tlLiP8Mnw39Oh1iTOG/GlqZDrwGW3MBaMEMuWs2wuguYL4eQLeb1OIaOqKu1D402v+7vcrKhzTtldSKLrNVEtztScF6fMbmZwLESPJ3m4B4j3XygSI5ytabdmFZQvFlF/E9DGQRfFLUFTiMXg2FOiWmyQckIyP5DvZIlkjjCxYvz1vx3k248dzu0JKlqFx3gW59H2egJUO2zYzMlfxA1lRRSZjRzRIYynVIxDPjFNbukNUNow00vOyLjHOPt29lOH+qgssbCmYcIW+6a74O9egto1cOpFvBf+E8/G1iWNhRY/b34jYR02EzFVZ3Ng4yboO5hxfLBkKs8c7uPSbz7D77a3E4upHIgnUqQl7Be/Zx02ChAXN9oQmanNnzp3LBQFVr1FXPh4BxJ3948FMydSdL8GvkHpL5bMK6QwlkjmCBaTgVAkitsXZn+XhyFvljSByVQsgYBbfJHNUlJWe5mQTKHDSjGlgrrrF+AbgIs+PpNL1cW4xzizMIlEYzx3uI8rl1VjmFxJLF8E730IPrKVomu+gEGBXvdkYZxfxdhhE+eNBnQI44ZNYvhI9568XqtQefmY+Hv7v3/ZxyP7uhkNRjL7i3v3izHNeoVx8fhFUboc7awNeDDBTvHXxF0Do1nGQbc9CSiw+Cpda5VI5gJSGEskcwStYrzt5BCqCp5AJPsW6UQqtWSK2WunGPGl98u2VpdwtC9zxTgUieEPR8efIxyAF78nslpbLprp5WbFZjagKNkrxrtOj+AJRLh6RZppfAYD1K7GaDRQ5bAmsozD0RhjwUheHmMQFWPQK4zj0WGdO/N6rUJl56lhltc6qCy28MnfvA5kmXiXQ+MdjI+FhqlxgZrHXdeOQO1qcC1MslMMjGURxkefFO+L4gpda5VI5gJSGEskcwQtrk2rQIGYeKWbinhz2MCRGV7ZzDGSoZGstcZBtzuAJ5A+mcI9eerd6/8Lo91w+WdmfK16UBSFYospa8VYE/yTvdWpqHXaElYKz+Rx0DkyXjHOnvZBSTWUNkthnAOBcJR9nR4uX1rFj+7ciEFRMBkUltZmaLzrek00szn12X604TKQafKijoqxZqc48Tx4xWdMRiuFbwg6d4goP4lkHiGFsUQyR7CajAQjMV45Poi22z7s1SFoNMpawFQEfYfOzAJnAGGDSO2Xba3WkinSV43dfnGh4CwyQyQEW/8DGrfAwstnfrE6sVuM+MOZK3aD8e7/iuIsgxSAaqeNPo84fiTT+GsdOHOpGIOoDkphrJv9XW5C0RgbWlysbSrjnneu4x+ubsVqStPMBjk13sH4RVFFsWWKNz8XjzsQt1NE4eCDhKMxRjKNg971C2G9WPFGfc8tkcwRpDCWSOYIFpOBsWCEgz0ezl8oti4HvUH9T2AwQNVS6D94hlY4PVRVxe0PZagYC2Hc1pveZ6xVjMtsBtj6XTEG+/LP6hYZZwK7xZi1YjwwFsRpM02d1peCWqeNbrcfVVUzj7/WgVYxzlSFT6JhI4ycSmrQkqRn1ykRhbeh2QXADefV8Q9Xt6Y/IeSF/kO6bRQwXjGebKOA3AfMULdWDI7ZcS+D8QzjSkeKC9VwAF7+ofAW167WvVaJZC4ghbFEMkewGA1EYyqqCjetqQNyrBgDVK0QHe+zEF8oSjiqph1W0eiyYzMbMka2ub0BbjU+xwWP3ADPfhUWX33Ot3rtFlPWit2AN0Rlpu7/Cayoc+IJRDjUM5qwUky3YuzRXTGOT9br3JXX6xUaO08N01xuz5zsMJGevaIKm48wLpsqjIut+uICXzs9zLYTQ+IC8vwPQ89evG0imq8qVcX49fvA2weXfEr3OiWSuYIUxhLJHMEaryZaTQauig+BGMqlYgxQvVx4bv06hjqcZab4gydhNCgsrirhSDorRSxKy9bP8G3zT1CsDnjHffCuB85ptRji08eyVYxHg1TqsFEAXLeqBoMCj+ztZiRuHSmz5xvXloPH+P9v787DG73Le/+/v9otWV7Hy6yeySQzmSXLJJOFhIQABcJStlKWAgUKDadAS0t7zqHntEDTXzco5YJC+UFPUwplLWsOW4CwZIGETJKZJDOZJLOPZ+zxbtmWtVnP+eORZHvGsh7Jki1Zn9d15Xps6ZH19USWbt26v/cNdkbRuOzaUlmUZVk8fGqUq3tand/orL05j3XOh2U0+Ny0h3y5UqN513mdbb77yF1P8aE7D9rfXPZaaGglfOBfAS58wzaTgl9+wu5Ssvkmx+sUqRUKjEVqRPZj9r2bW3MjWkeKzRh37rSPg9VXZ5wtC1hsI9m2rjBHFiqlSM/Ad97D1rPf5dOu1+H9g3tgx8uqYuhA0Ochmlw8MB6eSiz8kfUC1jT6uW5LO997vG/236zEjHHA68LjMs5rjP2N9qcOqjMuqHd0msGJOFcVExj37YfGLmhaV9R9/d8/fDZ/sODkxezmu8X//45MJXKdTvAF4eq3sqb3J2wwg3Q1BeaffOjbMHrCzhav8JtOkUpY+VcNEXEkmzG+fks7XreLpoCn+Ixxx6X2sQrLKbLZz8WGVWzrCnN2PEb/nD6+WBZ894/hwJf4nP932Lfp9zFVEBBnBX3ugoHJ0GTc0ca7rJdcvpZjg1O5EdpNJQbGxhiaGrzOM8YwuwGvigfFVIOHT9r/b67eVExgfMDOyhdpXUvDgtPpcjXGBd6YjUWTDE8liKcy513zDsDwu+4f5d6EAzCThHs/Cmu2wfaXFL1OkVpQPa8eIrIof+Zj0WdttTfetYV8jESLzBg3bwRvqCozxuMOMsYvuawbgC//+tTshacfhEc+T/y6P+SvIi9z1PJsOdk1xvkDk4K7/xfwol1dGAM/OtRPOOBZfLxwAeGAh8i0w4wx2HXG06Mwerzk+6wHD58cJeRzs7077OwGiaj9d1lCYJyP3+PCZQq3a8u2fcx2O6F5A08038zrPT/HmxifPfHej8LAIXj+B6vi0xiRStAjW6RGPH9HJ//9RdvZk8lAtYV8xWeMXS7o2F6lGePCgXFPe4ibt3XwlYdOzQ43+dUnoaGVR7fchmU56wW8nEJ+96I1ntkJhu2NzuuEO8MBrt3cZm9WLLGHcVY44CkyY5zZgNercorFPHxylD2bWp2/aRk4ZG+8K2NgnO2jvdgbs1hyJjdqfmBi9pOYbzS8hgbi8IVX2XsSzu6Hez5i1yDveFnZ1ihSbRQYi9SIznCAdz/34twLrR0YF5kxBujcUZUZ49l62cUDxDddt4lzkTh3P3kORo7Bk9+Fvb/Ho/327S9fv8hUsRUQ9HkWzdgNZttiFZExBnjJZXZnklI7UmSF/V7nNcZgP37cPujXaOh84qkZDvdHuLKYN2l9mY13a51vvHMi6Hcv2hVl7pCg/vHZN9oPxjbx6a4PQf8T8IVXwrf+mz145CUfLuv6RKqNAmORGtUW8jE6VcTku6yOS2HynD25qoqMTyfxuV0EvIs/LT3v0k7WNQf4zwdOwYOfAZcHrvl9Dpweo6c9SGuotA4NlRLyuUnMpPOO7x7O/D9cU0TGGODW3d0YU/iNRCF2xriIwNjttR9D5w4u6X5XszOj06Qt2LIm5PxGZ/dDQxs0byjrWoI+D1OLZIzH5pRjZScqZr8eWHsLvO4/7eB48El4+T9DQxE10yI1SIGxSI1qDfkYmUpgFbsJqnOHfayyrPH4dILmoBdTYKe7x+3iDddu4rEjJ0g/8nm47DXQtJYDvWNcsaG6yiiA3KaofB9nD5WYMe5qCvDKK9dz7Za2Ja0vHChy8x1A124Fxos4PToNwMa2oPMbZTfelbnTQ9DnZtphxnggExjHkjOMRZN0NwVg+63w5m/ZQfG2F5Z1bSLVSIGxSI1qC/pIzKQXzQYtqEo7U4xFk47bjr3u2o280fMzXMkoXP8uBiIx+sZjVVdfDHOHLCwcnGSnFxZTY5z1sdddufgkNQeaGjzOB3xkde2CyX5NwMvj9EgUgI1tFw7dWFAqbv89lrG+OKvQ5MXxBTLG2dZt3c2Z9W+5Ca763bKvTaQaKTAWqVFtmZKBkckiyymaN4AvXJ2BscONZJ3hAG8OPchD6e0csjZzoNfeOX/lxuqqL4bZlln5gpOhyQR+j4vGTAC93MIBL5PxFDPpIj556NplH5U1XtDp0Shet6EzHCh8Mth/i+lkhQLjxScvjmYC441tDbk2iH2ZY/f5PYxF6oACY5EalQuMo0UGxsbYnSmqrJRibDq5aA/jeaaGWBc/xoOevbzva/t56MQIbpdh59rqC4xDvsUzxkOTcdY0+guWkFRKdiz0ZIFey/N07baPCowX1DsyzfqWBucdKfoO2McKBMYhv3vRrhTZ/uHbu5oYyJT1zGaMiyvvEVkNFBiL1KhcYFxsyzawR0NXWcZ4PJpw3nrsxL0AXPfcV3C4f4I77jvO9q7wgkMOVlqwUI3xZKLojXflFM4ExkXVGTd2QKgTzj1RoVXVtt7RaPH1xf4maN1S9rU0eBdv1zYWTeL3uNjcHqR/PIZlWbnM8QVT70TqgAJjkRo1GxiX0rJtJ0SHYHKgzKsq3fi08xpjjt8LvjDX3PB8Xrd3I6m0VZX1xQDBAjXGQxNx2ovceFdO4YD9b15UZwqA7t0KjPM4PTrNhtYSNt5VYGhGqEC7trFogtagj66mANPJGSKxFH3jMRr9ntxjQ6SeKDAWqVHZwLiklm3dl9vHKulFm0jZmwgd9+Q9fg/0PAvcHv7iZTt47vYOfvOKtZVdZIlCBWqMh6fiVZExjkwX25liFwwchpkiA+pVbiqeYmQq4Xzj3UzKfoNRgTIKKNyubTRT29/VbGeHByIxzkVidDWpjELqkwJjkRrV6PfgdZtcH9yidF9mH7O1jSts3MHUu5xIHww/A1tuBuyM57+/7Vpu2Lqmkkss2WIZ43TaYngysaIZ46ZSM8Zdu2EmDiNHK7Cq2nV6NNORwmnGeOhpSMUqGBi7SaTSpPL00R7PBMbZjXb9kRj9kRjdzSqjkPqkwFikRhljaA2WOOSjoQVaeqCvOjLG45kNQM1BB5nTTH0xm2+q4IrKJ+jNX2MciSVJpa2iexiXU67GOF5CxhhUTnGe0yN2D+MNrQ4zxrmJd5ULjAGiyYWzxqPRBC0NvlyGuH88Rv94jO4mh+sXWWUUGIvUsLaQb8GMsWVZpAu131p7edWUUsyOg3aQMT5+DwSaZ7PeVS7ozx8YD01mh3usZClFiRnjNdvsqYPqTDHPbA9jhxnjvgPgDUL7xRVZT66Pdp5SnrHpJK0hb26jXd94jIGJuDpSSN1yFBgbY241xjxljDlijHn/Iue9xhhjGWP2lm+JIpJPW8g3b3JV1r/8/Ci3fvyexW/cfQWMHINYpEKrcy4XGDsppTh+j50tdlVfB4qF+NwuPC7D1ALt0IYms+OgqyBjXGxg7PHbwbEC43l6R6dp8LppdzqavO+A/SavQo/nXB/tBUp5LMtiLJqgucFHwOumJejl0NkIM2lLPYylbhUMjI0xbuBTwIuBncAbjDE7FzgvDPwR8GC5FykiC2vLjIU+3w+f6OeZgcnFhzaszWzAq4KPwnM1xoX6GI+ehLGTNVNGAXbJS9C3cC/ZbMa4lKl35RLwuvG5XUSKHQsNdjmFAuN5To9G2djW4KwvdTptlzNVqIwC7M13ANMLPP6iiRmSMxatmTekXeEA+0+P2V8rMJY65SRjfC1wxLKsY5ZlJYCvAK9Y4Ly/Bj4MxMq4PhFZxEKBcSSW5ODZcSyrQKeBbGeKKqgzHsuss2BXihP32cfMxrtaEfJ7Fs4YT2RLKVb2Y+twwENkuoTuEl27YPw0TI+Wf1E16vRI1PnGu5GjkJyCtVdWbD2zXVEu/P+b/bQp+0lNV3MgNxZ6bbNqjKU+OQmM1wOn53zfm7ksxxizB9hoWdZ3y7g2ESmgLeRjfDo5b8f5vhMjZBPFC5VZ5IS77SENVdCZYjyawJjZj/XzOv0ABFqg49LlWViZNPjcC25+Gp5K4DLQ6mTTYQU1NXiLG/CR1Zn58HDwqfIuqEZZlkXv6HQRG+8qN/Euq2GRzXezJUz24697Tou2LtUYS51yEhgv9HlQ7vNZY4wL+BjwpwV/kDG3GWP2GWP2DQ4OOl+liCwo18s4OhvUPHBsJPf13MsvYEzVbMCzx0F7cRUaodv7MGzYW5FBCJUU8nmILlhjHKct5HM+OrhCwgFP8TXGYNcYgwLjjLFoksl4qoiNd/vB7bdHtFfIYpvvzt/0mi2f8LgMa0IKjKU+OXl16QU2zvl+A3B2zvdhYDfwc2PMCeB64M6FNuBZlvVZy7L2Wpa1t6Ojo/RViwgwm2mcmxl+4Ngw4cyLYbYNWl7dl8PgYUiVMFa6jMaiDqbexSdg4BBsuGZ5FlVGQZ97wSEL9jjolQ9A7MC4hIxxyybwBOxevJLrYex46l3fAbscxV25CXOLbb4byzw/tGbeYGcD466mQOE3qSKrlJPA+CHgEmPMFmOMD3g9cGf2Ssuyxi3LWmNZ1mbLsjYDDwAvtyxrX0VWLCI52Z3vw5nuBpFYkifOjPO8HZ0AjBYaF732ckin7IBzBY1NJwv3MDXGe4IAACAASURBVD7zCGDZGeMaE/J7FhzwMTQZX9GNd1lhv3dexnjRTZtzudzQfokyxhnZHsaOpt5Z1uwo6ApabPPd6HkZ4+5cYLzyb9ZEVkrBwNiyrBTwHuAu4Enga5ZlHTTG3G6MeXmlFygi+bWG5meMs/XFL97dPe/yvKpkA974tIOMce9D9nH91ZVfUJk1+NwLfpQ9XEUZ42xXitMjUXZ98Ic8fHKkwK0yOrbBkAJjgN7RInoYj56A2PgyBMb5M8bj0exgnfmlFJp6J/WswE4Xm2VZ3we+f95lH8hz7i1LX5aIOJHLGGc6UzxwbASf28XN2zpwmdkawrxat4AvvOIb8MajCXoKBRO9++zsZEPr8iyqjEKLtGtrr4JaTnvznR04/eroMLFkmoNnI1zd01b4xmu2wxPfhEQUfA5LCFap06NRmhu8uTHbi1qGjXcAfo8Ll1m4xng0miToc+P32MFzdsOdWrVJPautHSwiMk92N/lDx0eIJWd44NgwV25qIejz0NzgzdUQ5uVy2S/MZx9ZhtXmNzadXHy4h2XBmX01WV8M9sfZ52fsIrEk0cQMa8JVUEoR8BBNzJCaSfPIKbv12rmIw86bHdsAC4aPVG6BNeLkcNRZGQXYgbHLM9vZo0KMMfbmzwXemI1Fk/M6oqwJ+bnx4nZuumRNRdckUs0UGIvUMJ/HxWv3buDOA2d5/kd/wRNnxrl+i53law36Fu9KkbX5RvtFenqswqtdWDptFS6lGDsJU4M1WV8MEPLbGWPLmq3dffiEHYBesaFlpZaVkx0LPRlPzQmMHW7IXJPpqFDnG/Asy+LQ2QiXdjc5u0HfAejcAd7KZ2eDfveCNe721LvZvzuXy/DFd1zP8y7tqviaRKqVAmORGvfh11zBF99xHU0NXtIW3LTN7vjSHPQyVqjGGOxhGVYaTt5f4ZUubCKWwrJYfPNdb2Yvb41mjEN+DzNpi4k5Ldt+eXQIn9vF1T0rXxqS7R99ZmyaZwYmgSIyxu1bwbjqfgNefyTG8FSCy9Y3O7zBY/ZY9mUQ8i3cjm80mqA1VLmOGCK1SIGxyCpw48Vr+O4fPpu7//Q5XLN5NmNcsMYY7GDT0wDHflHhVS4sW+6xaMa49yHwBiv+sXOl7NloB7/3PTOUu+yXR4e5qqeFgNe9UsvKacoExvc9M4Rl2d8POM0Ye/x2rXqdb8B74kwEgN3rHWSMp4btT0A6d1R4VbaLOxt54uz4BZePTScLj2EXqTMKjEVWCbfLsLWjMfd9S9DrLDD2+KHnWXD8ngquLr/x6ez0rQKB8bo94Ha0X7jqXLO5lbaQjx8+0Q/A6FSCQ30RbtxaHbWc2VKKXzw9iDHw/B1dnJtwmDEGe9DHYH2XUjxxZhxjYMdaB4Fx9k3EMk1w3Lu5lZPDUQYn5r/ZGYsWqO0XqUMKjEVWKbvG2EEpBdjlFINPwsS5iqxlsb6433u8D4COcJ7uDKk49D9es/XFAB63ixfu7OKnhweIp+xNkpYFN1zcvtJLA8h1UXjoxAjbu8Js7QgxFk0SW2CM8II6ttmb72ZKmJ63Shw8O87WjsZc3+BFDR62jxWceDdXtrvI3BZ86bTFWDSx4uPIRaqNAmORVaqlwUs0MUM85SC42fIc+3ji3rKv42eHB7jy9h9xYmjqgus+/fOjfOYXx3j9NRvz12aefRRmEjVbX5z1ot3dTMZT3H9kiF8eHSboc3N5FWy8g9ka4+SMxZ5NLXRm2nU5LqdYsx3SSbs3b5164kyE3escbrwbOAy+RmjeUNlFZexe34TP42JfZsMnwEQ8Rdoq8EmNSB1SYCyySrVkehyPOymnWHsFBJrh2M/Lvo6jg5NMxFL804/nf9T++V+d4B9+eJiXX7GOv3nVZRiTZwTtifvs46Ybyr625XTD1nbCfg8/fKKfXx4d4totbXjd1fEUnA2MAfZsas31sXVcTpHNfNZpnfHgRJz+SIzdTjfeDR62y0/yPebLzO9xc8WGZvadnA2Ms88LLcoYi8xTHc/KIlJ2rZlMkKOWbS43bL4Jjpd/A14kU0N854GzPHHG3gD008Pn+OCdB3nBzi4++torcLsWCRBO3m9vugtVR9lBqfweN8/f0cn3H+/n6OBU1dQXw2yNMcBVm1pzI4Edd6ZYc4l9rNPOFNmNbc4D46eWrb446+qeNg6eHc+Vx2TLrApOnBSpMwqMRVapbO2g8zrj58DYqbJ/HB6JpQj63LQEvXz4rqc4MjDJe7+8n51rm/jE6/csnjWdScKpB6HnxrKuaaXcmimnAHjW1uoJ9H0eF36Pi+YGLxetCdEVzmSMnZZSBJohvLZuexkfzLzh2+mklGJ6FCb7l62+OGtvTyvJGYsDp+1+5WOZN6xq1yYynwJjkVUq27jfUWcKsDfgQdnLKSLTSdobfbz7lou55+lB3vCvD+DzuPjs7+6lwVegVdnZ/ZCcsoeQrAI3b+sg4LUD0J1Ouhcso+YGL3s2teByGVqCXnweFwNOM8aQ6UxRpxnjMxE2twedjYLOdu9YplZtWdl+2Q9nBrhke5w3q12byDwKjEVWqdZMjbGjIR9gZ7BaeuDQnWVdRySWpCng5c3P6mFtc4DRqQT/8sarWN/iYHTuyUx9cc+zy7qmlRL0eXjHsy/iLTdsxrVY+cgKuP0Vu/izF9pZTGMMXU1+56UUAG0X2RMK69ATZ8eLqy+GZc8Yt4Z8bO0I8fCJUSzL4p6n7Z7a7SEFxiJz1WZTUBEpqKgaY7A3Au3+Lbj/4zA1BKHy1MBGplM0BbwEvG7ueOs1jE8nue4ih2UEJ+63Ox40dpRlLdXgz160vAGRU7fuXjvv+65wwHkpBUDTeogOQzK2LGOOq8XoVILe0WnedH2PsxsMPmUP1GneVNmFLWBvTxt3Hern739wmG880ss7n3NR7g20iNiUMRZZpRq8bnxul/OMMdiBsTUDh75TtnVEYkmaGuz34DvWNnG906B4JgWnHlg1ZRS1pqspUFzGuHm9fYycqcyCqtTBs5mJd+uKyBh3bAPX8r/8Xt3Tylg0yWfuOcabrt/E+29d3g2AIrVAgbHIKmWMcT79Lqtrl52hfeKbZVtHZDrprPbyfP2PQWJi1Wy8qzWdxZZSNK2zj5GzlVlQlTrcbwfGjjbeQSYwXpmA9PqL2vG6Da++aj23v3x3/haJInVMpRQiq1hR0+9gtpzi539nBzjZYGcJIrEUTaW0hDp5v33cvDrqi2tNV1OAqcQMk/EUjX4HLxVN9ZkxPj40RUvQS5uTkoRYxP73Web64qxN7UHuf//z6Gj0KygWyUMZY5FVrOiMMcDuVwMWHPz2ku8/NZNmMp4qLWN84n5o2wrh7iWvQ4rXnR3y4TRrnMsY11dgfGJ4ip72kLOTs+3sVihjDNAZDigoFlmEAmORVawl6GVsuoiMMdjDGrovh4NLL6fI9uzN1hg7lkrYGeMtNy15DVKazmKHfPhCEGipu1KKE0NRtrQHnZ2c60ih2l6RaqXAWGQVs0spiswYg11O0fvQbM/VEkWmM4FxsRnjk/dDPALbbl3S/UvpsmOhB4rpTNG8AcbrJ2McS85wdnzaecZ48DC4/dC6uaLrEpHSKTAWWcVagj7Gogksyyruhle+Ebwh+PnfLun+IzE7KC+6xvipH4AnYE/jkxWRDYz7i92AV0elFL2jUSwLtqxxGBgPH7X7PbsKDLYRkRWjwFhkFWsJeknOWEQTM8XdsLEDbngPHPwWnH205PuPZMbONgWKKKWwLHj6B3DRLeBz+BG1lF2j30PI5y6yM8X6ugqMjw9FAehxWkoxcgzat1ZwRSKyVAqMRVax2SEfheuMkzNp/vybj/HTw+fsC571Hmhog7tvL/n+S8oYDzwJY6dURlEFupoCxZVSzB3yUQdODk8BDjPG6TSMHIe2LRVelYgshQJjkVWsJZgdC124zvibj/Ty5V+f5rbPP8yPD52DQBPc9Kdw9Kdw/J6S7j9XY1xMYPz0D+yjAuMVpyEfizs+NEVzgzf3d7aoyBmYidulFCJStRQYi6xirZkX7EIZ40QqzT//9Ai71zexa10T7/riw3bm+Jp32FnAH3/QzngVKZcxLqaU4qkfwro90LS28LlSUV1Nfs5NaMhHPieHo2x2Wl88csw+tqmUQqSaKTAWWcVaMqUUhTLG33ikl97Raf70hdv5/NuvY8faJv7bFx7h3DTw/A/A2Udg/xeLvv/IdBKXgZDPYWA8OWh3w9j24qLvS8rPzhjHnW/erLMhH8eHpthcTH0xKGMsUuUUGIusYrOBcf6McSKV5pM/PcKVG1u4ZVsHzQ1e/vJlO0nMpDnUF4HLXwebngU/+SBER4q6/0gsRTjgxeVyOFDgmbsAC7arjKIaXLo2TCKV5pdHh53doI6GfGRbtW122qpt5Kjdqi375kFEqpICY5FVrKUhW0qRP2P89Yd7OTM2zR//xiW5iVg9bXYW7PRI1B4T/ZKPwPQo/Oxvirr/yHSyuOEeB78FzRvtASOy4l68ey1rGn3ccd9xZzeooyEf2VZtm9c4zRhnNt659LIrUs30Fyqyivk8LkI+96KlFF956BSXrW/mOds6cpd1hP00eN2cHLbbUdF9GVx7Gzz0b0W1b4vEkoT9DjfeRfrsjX6Xv84OxmXFBbxu3nhdD3cfHuD40JSzG9XJkI9sqzbnGeNjKqMQqQEKjEVWudaQj5GphVtuTcVTHDwb4TnbOnLZYgBjDJvagrOBMcAtfw6hNfC9P3O8ES8ynXKeMX7sq2Cl4crfcXa+LIs3Xr8Jn9vF5+53mDWukyEf2VZtjgLjXKs2BcYi1U6Bscgq19MezJvtO3B6jJm0xdWbWy+4bmNbkFMjc27X0AIv+Gs4s8/xRrxILOlsHLRlwf4vwcbrNAChynSGA/zmFev4r4d7GZ92MF68ToZ8ZFu1tYYctGqb6IPUtHoYi9QABcYiq9wlnWGODEwu2Flg38lRjIGrNl0YGPe0Bzk1Ep1/uyteDxuvd7wRz64xdhAYn3kEhp5StrhKve3GzUQTM3ztodOFT66TIR9q1SayOikwFlnltnY2MpWYoW/8wkDl4ZOjbOsM07xA8NrTHiSWTDMwMacMwxh46T863ogXiaWcZYwPfAk8Adj1qsLnyrLbvb6Zyzc08+MnzxU+uU6GfKhVm8jqpMBYZJW7pLMRgCMDk/MuT6ctHjk1ylU9F2aLATZlOlPMqzMGeyPeNb9vb8S741b4xjvggf//grrj1EyaybiDGuNkDB7/Ouz4TQg0F/GbyXLqaQ8x4GQKXh0M+Yinim3VdgzcPntjoohUNQXGIqvcxZnA+JnzAuOnByaYiKXYmycw7sm86J8aiV545fP+Aq55Oxg3nHoQfvg/4XvvmxccT8Yz46ALZYyf/gHExuCKNzj9lWQFdIb98z89yKcOhnw8c24Sy4KLOoroYdy6GVzuiq5LRJauiAajIlKL2kM+WoNejgxMzLt834lRAPYusPEOYH1LAy4Dp4YX2LgXaIKXftT+2rLg7r+C+z4GxmVfbgyR6UxgXKjGeP+XIbwOLrqlmF9Lllln2E80McNkPEWjf5GXjjoY8vHr43Z9/TWb25zdQB0pRGqGAmORVc4Yk9uAN9fDJ0dZ0+jPlUycz+dxsba5gZMLZYzn3wE8/4OQnoFffgL8YXjBXxGJ2R0MmgKLPM1M9MORn8CN71U2rcp1NvkBGIjEaOxozH9idsjHKu5l/ODxYTa2NbCupaHwyZZll1JsubnyCxORJVMphUgd2NrZyDPndabYd3KEvT2t8/oXn6+nPXhhjfFCjIEX3A5XvQXu/zicuJ9IprXXohnjx74G1oy6UdSAznAAwGE5xTq7RdkqZFkWvz4+wnVb2p3dYPIcJKPKGIvUCAXGInXgks5GxqJJhqcSgJ31Oz0ynbeMIivbss0RY+DWv4PWHvjOu5iaHAcWqTHO9i7ecA2sucTx7yIroyOcyRg7CYzDa1ft5rtnBiYZjSa5dovDMorho/ZRgbFITVBgLFIHLunKbMA7Z5dTPHzSri/O15Eia1NbiJGpBBMxB4MdwP4Y/ZWfhtGTbH70HwHyd6Xo2w+DTypbXCM6w7OlFAWt4ozxg8eGAbjeacZYrdpEaooCY5E6kO1McWTQDoy/vf8MLUEvu9ct3h6tpz1Py7ZFb3QDXP8HXHLyS9zkeix/KcX+L4HbD7te7fxny4ppbvDi87gYdFpKMTkAMw7fUNWQB4+PsLY5wMY2B/XFYAfGLg80b6zswkSkLBQYi9SB7qYAjX4PR85NcGo4yo8OneON123C51n8KSC7Me+003KKrOf9JUPBrfyz959pnDx14fXjZ+Cxr8KlL7VHTUvVM8bQ0eiwZVt4LWDZ9bWriGVZPHh8hGu3tC1amz/PyDFo6QG39rqL1AIFxiJ1wBjDxZkNeP/xqxO4jeHN128ueLtN2YzxeYHx/UeGuP5v7+ap/omFbga+IP+55R+wjAvXV94AscjsdckYfPVNdheLW95f4m8kK6Gzyc/ARDFDPlZXOcXxoSkGJ+LON96B3cO4XaOgRWqFAmOROnFxZyNP9kX46kOneenla+luDhS8TVPAS2vQO6+U4uDZcd75hYfpj8Q43B/Je9tT6Q4+4PvvMHwE/ust0P+4veHuu38CZx+BV38WOraX5XeT5dEZ9jMQcZoxZtX1Ms72L3a88c6y1MNYpMbosx2ROnFJZyNff7gXgLfduMXx7Ta1hzg6OMlkPMVYNMHb/v0h/B4Xk3EYyXS5WEgkluRs6Cq48R/hu++Do8+GUCdMDcAtf26XUUhN6QwHeDATHC4qmzFeZRvwHjw+wppGP1udTrybGoTEpAJjkRqiwFikTmQ7U1zd08qVG53X9W7tCPHNR86w+4N3AfbAjq++81m85BP3MrpYYDydsjtS7P092P5SeOYueOoH0NgFN/+Ppf0ysiI6w37GokniqRn8nkUGsgTbwe1bdS3bHusd46pNLcXVFwO0qZRCpFYoMBapE7vXNxP2e3j3c4t7kX7/rZdy/UXtjEUTjE8nefHutexY20Rr0Jfri7yQSCw5O1Uv3AVX/a79n9Ss7PS7wYk4G1oXnpgI2D2tw2tXXcb4XCTOTZd0OL9Broex809oRGRlKTAWqROd4QCPfeiFzrNd2ds1BXjt3gtbTbUGvYxGF8sYJxefeic1Z+70u0UDY7DLKVbR5rtoIsVkPJV7c+DIyDEwbmjZVLmFiUhZafOdSB0pNiheTHvIz/DkYhnjVP6pd1KTctPvnG7AW0Wb77K/c/bNgSMjx+yg2K2/A5FaocBYRErSGrowYzwZT9E/HiM1k2Yynso/9U5q0mwpRRHT7yyrwqtaHtn+zdkJgI6MHFOrNpEao1ctESlJW8ifGy2d9Y7/eIgHjo3Q4LU3ZiljvLq0h/y4DM6HfKRiMD0KQYftzapYduKf41IKy7ID443XVnBVIlJuCoxFpCRtIS+j0STptIXLZZdoHBmY4qpNLVyxsYVzkRg3bytio5JUPbfL0N7osJfx3JZtqyAwzg426Wh0GBhHhyEeUas2kRqjwFhEStIW8jOTtojEkrQEfSRn0gxPxXnjdZv4kxdsW+nlSYV0hkuYfte1q7KLWgYDE3E8LkNr0OfsBrlWbQqMRWqJaoxFpCRtIbtMIjvkY3AijmVBV1MRm5Ok5tiBcf1NvxuIxOkI+3OfjhSkHsYiNUmBsYiUpC1kf6ScDYz7I3YWsbu5iM1JUnM6w4HiAuNV0st4YCJW3Ma74aNgXGrVJlJjFBiLSEnaMh8pZwPjc+N2YKyM8erW2eRneDLOTLpAtwmPD4JrVs30u8GJOB3Ftmpr3mj/O4hIzVBgLCIlaWs8LzCOKDCuB51hP2kLhqccbsBbNRnjePHDPdSqTaTmKDAWkZLkMsbRbClFHK/b5C6X1SmbNXXcmWIVTL9LzqQZmUo470gBdmCsjXciNUeBsYiUpMHnpsHrZmRyNmPcGQ4435wkNWl2yIfDOuOJ2i+lGJossofx5CDExrTxTqQGKTAWkZK1hXy5jPG5SIzuZpVRrHbZDWiOW7ZFhyHp4NwqVvQ46MHD9rHz0gqtSEQqRYGxiJSsLeSb15Wiq5gaTKlJbaFsbXmy8MmrpDNF0eOgs4Fxx44KrUhEKkWBsYiUrC3kY3ROVwptvFv9Ah43xsB0IlX45KZsL+PaLqfIZscdl1IMHgZ/M4S7K7gqEakEBcYiUrK2kI/hqQST8RRTiRm6FRivei6XIeh1E03MFD65pcc+jp+u7KIqLFtKscbp5ruBw9CxHYzq7UVqjQJjESlZNmPcrx7GdSXo9zDlJDBu3mgfR09UdD2VNjgZpy3kw+t2+JI5eFj1xSI1SoGxiJSsLeRjKjHDqZEpQIFxvQj63ESdlFJ4AxBeB6MnK7+oChqIxJ3XF08NQXQIOhQYi9QiBcYiUrLsRqwn+yYA1JWiTgR9HqbiDjLGAK2baz9jPBGjo+iNdwqMRWqRAmMRKVlrZpjHob4IgLpS1ImQ04wxQGtPzQfGAxPx4lu1KTAWqUkKjEWkZO2N2YxxhHDAQ9DnWeEVyXII+j3ONt+BnTGe6KvZXsbptMVgMeOgBw6Dv8nu4SwiNUeBsYiULJsxPjE0pY4UdaS4jPFmwKrZzhSj0QSptFVcD2N1pBCpWQqMRaRk7Zka47SljXf1pMHndl5jnG3ZVqMb8AYz46CLqjHu2F7BFYlIJSkwFpGSNTd4cWUSYwqM60fI5ykyYwyMHq/YeiqpqHHQU8MwNaiJdyI1TIGxiJTM5TK5coruZm28qxdBv8MBHwCNXeD21+wGvKLGQQ89ZR+18U6kZikwFpElac2UUyhjXD9CPg/xVJrUTLrwyS6X3ZlirDZLKYoaBz3wpH3UcA+RmqXAWESWpE2Bcd0J+twARJOrv5fx0ESCoM/trOPK4FPgC0PT+sovTEQqQoGxiCxJW7aUQoFx3cgGidFiNuCNngTLquCqKmM0msi9+Sto8Eno2KaOFCI1TIGxiCxJW2O2xliBcb0I+e2M8VQxG/DiEZgerdyiKmRkqojA+Nwh6NxZ2QWJSEUpMBaRJdnQ2kCj35Nr3SarXzZjPF3MkA+oyXKK0Wgit8F0UZMDEB2Crl2VX5SIVIwCYxFZkt+7cQs/eO9NeNx6OqkX2RrjqXgRY6GhJjfgOS6lOHfQPipjLFLT9EomIksS8LrZ2BZc6WXIMsptvnOaMc4N+ThRmQVV0OhU0lnGeOCQfVRgLFLTFBiLiEhRQn67lMJxjXGgCYLtNTf9Lp6aYTKeoi3kLXzywCEIdUBjR+UXJiIV4ygwNsbcaox5yhhzxBjz/gWuf58x5pAx5jFjzN3GmJ7yL1VERKpB0RljyHSmOFGZBVXIWDQJQIuTjLE23omsCgUDY2OMG/gU8GJgJ/AGY8z5f/2PAnsty7oc+Drw4XIvVEREqkMo167NYcYYarKX8chUAqBwjXE6DYOHtfFOZBVwkjG+FjhiWdYxy7ISwFeAV8w9wbKsn1mWFc18+wCwobzLFBGRatGQ3XxXTMa4bQuMn4aZZIVWVX6jmcC4YI3x6HFIRpUxFlkFnATG64HTc77vzVyWz9uBHyxlUSIiUr38HhdulyHqtMYYYM02SKdg5FjlFlZmI1GHGWNtvBNZNZwExguN8FlwfJEx5k3AXuAjea6/zRizzxizb3Bw0PkqRUSkahhjCPrcTDmdfAfQsd0+Dh6uzKIqYDRTY9xaaPPdwJOAgc5LK78oEakoJ4FxL7BxzvcbgLPnn2SM+Q3gfwMvtywrvtAPsizrs5Zl7bUsa29Hh3buiojUqpDP43zAB9gZY4DBpyuzoApwXEpx7qBdQ+0LVX5RIlJRTgLjh4BLjDFbjDE+4PXAnXNPMMbsAT6DHRQPlH+ZIiJSTYI+t/N2bWAHjS2baipjPDKVIBzw4C00vGbgkDbeiawSBQNjy7JSwHuAu4Anga9ZlnXQGHO7MeblmdM+AjQC/2WM2W+MuTPPjxMRkVUg6HcX164NYM12GHyqMguqAEdT75IxGD6q+mKRVcLj5CTLsr4PfP+8yz4w5+vfKPO6RESkigV9HucjobM6tsOJeyE9Ay53ZRZWRiNTicI9jIeeAmsGOncsz6JEpKI0+U5ERIoW8pWQMe64FFIxGKuNCXij0QRtwQIb785lOlKolEJkVVBgLCIiRQv6PMW1a4M5nSlqo5xidCpJa6FSiv7HwdMAbVuXZ1EiUlEKjEVEpGjBUjLGuc4UtbEBz84YFwiM+w5A925wO6pMFJEqp8BYRESKFvKXUGPc0ALhtTXRsi2WnCGamFk8Y5xO24Hx2iuXb2EiUlEKjEVEpGglZYzBLqeogYzxqJOpd6PHITEBa69YplWJSKUpMBYRkaIFfW5SaYtEKl3cDddsh6GnwVpwgGrVGHEy3OPso/ZRgbHIqqHAWEREihb02TW1JW3AS0xC5EwFVlU+o1OZcdCLdaXoOwBun1q1iawiCoxFRKRoIb/dh3iqlJZtUPXlFI5KKfoO2G3a3AVauolIzVBgLCIiRctljEsZ8gFV37ItGxjn3XxnWZmNdyqjEFlNFBiLiEjRSs4Yh9ZAsB0GnqzAqsonW2Pc0pAnGzx2EmJjCoxFVhk1XhQRkaI1eEusMQa7/ODcE2VeUWmmEzPcffgcdz85wH1Hhnj3LVt5641bGJ1K0NzgxePOkz/qO2Af1apNZFVRxlhERIqWzRhH4yW0bFu3B/qfgFS8zKsq3u3fPch7vvQoP39qAIAv/foUACPR5OL1xWf3g8sDnTuXY5kiskwUGIuISNGyNcZTpWSM1+2BdBIGDpV5VcXrG49xaXeYfX/xAt51y1aePjfJ0cFJRqcShTtSdOwAb2D5FisiFafAWEREipbLGJcy5GPdHvt45pEyrqg0E7EUbSEfbpfhRbu6AbjrYD8jU4n8GWPLgr79sE71lyNd8wAAFyFJREFUxSKrjQJjEREpWjBXY1xCYNzSAw1tswMyVtBkLEU4YP8u61oauGJjC3c90c9YNEFLvuEe46chOqz6YpFVSIGxiIgUrcGXrTEuoZTCGDtrfHZ/mVdVvIlYkkb/bMnErbu6OdA7zrmJeP6M8Yn77eOm65dhhSKynBQYi4hI0XweFz63q/h2bVnr9tg1xsnp8i6sSBPx2YwxwK277XKKmbSVfxz0ifugoRU6dy3HEkVkGSkwFhGRkgT97tLatYEdGFszdneKIn3650f5oy8vvQwjnbaYPC8w3rImxKXdYQDaQnk23524B3puBJdeQkVWG/1Vi4hISYJeN1OltGuD2Q14RdYZf++xPv7hh4f53uN9pGbSpd13RjQ5g2UxLzAGcpvwFswYj56EsVOw5eYl3beIVCcFxiIiUpKg38N0ssSMcdM6CHUWFRgfOhvhz/7rAAGvi5m0RX8kVtp9Z0zEkgDzaowBXn3VenasbWLX+uYLb3TiPvu4+dlLum8RqU4KjEVEpCQh3xIyxrkNeM5ato1PJ7ntC/toavDwd6++DIDTI0urT56M2UH9+RnjnvYQP3jvTaxvabjwRifus0dad+xY0n2LSHVSYCwiIiUJ+jyl1xgDrL8KBp+C+GTBU+8/MkTv6DT/+NtXcNWmVgB6R6Ol3zcQyRMY52VZcOJe1ReLrGL6yxYRkZKE/EvIGEOmztiC/scKnjo4YY+PvrS7ibXNDRgDp0eXljHOllI4DozHTto9jFVfLLJqKTAWEZGSNPg8TCeXEhhfZR9P/7rgqUOTcVwG2kI+fB4Xa5sCS84YT8azGeNFRj/Pdfxe+7j5piXdr4hULwXGIiJSErvGeAmlFI0d0H4JnPpVwVOHJuO0hfy4XQaADW1BepdYYzyRKaVo9DvMGJ+4D0Id0LF9SfcrItVLgbGIiJTErjFeQsYYoOcGOPkrSC/+cwYnEqxpnG2ftqG1YekZ42JqjJPT8MyPYMtz7I2DIrIqKTAWEZGShPxuphIpLMsq/Yf03AjxcTh3cNHTBifjdIT9ue83tgbpi8RIpErvZTwRS2IMhHwOAuMDX4HpEdj7eyXfn4hUPwXGIiJSkgafG8uCWHIJgzY232gfT/5y0dOGJuKsaZwNjDe0NmBZ0DdeejnFRDxFo8+Dy1UgA5xOwwP/AmuvsDPcIrJqKTAWEZGSZDOtS2rZ1rwBWjbByfvznmJZFkPnZ4zbgsDSehlPxFLOyiiO3g1DT8Oz3qMyCpFVToGxiIiUJOhzAyytZRvY5RQnf2n3CV7AZDxFPJW+oMYYltbLeCKWpNFJYPyrT0J4Lex8Zcn3JSK1QYGxiIiUJNvmLJLpB1yynhsgOgRDzyx4dbaH8dxSiu6mAB6X4fQSAuPJeKpwq7ZzB+HYz+Ha28DjW/xcEal5CoxFRKQkLUE7qByfXmpgnK0zXricYmgyAcwPjD1uF2tbAvQuYcjHRCxVuFXb/Z8AbxCufmvJ9yMitUOBsYiIlCQbGI9FlxgYt10Ejd2LBMZ2xnhujTHYnSlOjywhY1yoxnj4KDz+X3YnimBbyfcjIrVDgbGIiJSkpcEuLRibTiztBxljl1OcuH/BOuNsYDw3YwzZXsalZ4wjhQLj+/4JXB644Q9Lvg8RqS0KjEVEpCRlyxgDbLkJJs5C/+MXXDU4MTsOeq6NrUEGJuLEShxLPRlP5q8xHj1p9y6++q0Q7i7p54tI7VFgLCIiJQl43fg9rqXXGIPd8cETgIf//YKr7HHQvtw46KwNbXZnijNjxWeNkzNpYsl0/hrj+z4GxgU3vrfony0itUuBsYiIlKwl6GUsusRSCrBreHe9Gh77GsQn5l1lj4P2X3CTja3ZXsbF1xkvOg56vBf2fxGufCM0ry/6Z4tI7VJgLCIiJWsN+spTSgFwzdshMWlveJvj/OEeWRsygXEpdcYTucB4gVKKn3wIMHDT+4r+uSJS2xQYi4hIyZobvIyVo5QCYP3V0H0ZPHTHvE14Q5PxBTPGnWE/PreLg2cjWOdv2sszLCQr23v5glKKUw/YgfmNf2RP5BORuqLAWEREStYS9DJeroyxMXZrtHOPQ+8+wB4HPTgRnzf1LsvlMjxnewdf/vUpfv/z+zibrTW++6/hw1vg/o9DMrbgXU3G7Yxx09xSinQafvA/IbwOnv0n5fmdRKSmKDAWEZGStTT4lt6uba7Lfht8jbDvDmDuOOgLM8YAn37jVfyvl1zKfUeGeOHH7mH47o/Dvf8IDa3w4w/AJ6+Bx79uB71zZEsp5o2EPvBl6NsPL/gr8IXK9zuJSM1QYCwiIiWzN9+VKWMM4A/D5a+Fg9+E6MiCU+/m8rhd3HbzVr78+9dzc/I+2u79IFz6MnjPPnjztyHQDN94O/yf58PJX+ZuNxm315yrMR49aQfSG661g3MRqUsKjEVEpGTNQS/xVLrkXsIL2vt7kIrBga/knXp3vos4w8e8n2ag5Qr4rf8DLjdsfS688xfwyk/DRD/8+4vhq2+GkeOzGWO/B6Ij8J+/BekUvOJTdkmHiNQlBcYiIlKy3PS7cmaNuy+zM7f77mAwYtcI58sYZzXdeztxPHx7+4fB2zB7hcsNV/4O/OHD8Nz/DUd+Ap+6lssPfphbXPtpip2Br/wOjJ2EN3wZOraV7/cQkZqjwFhEREqWm35XzjpjsLPGw8/gPnUfAGvCF26+yzl+L+aZu/g38yr6ko0Ln+MLwnP+hx0g73o1V/R+ic/5Poz/U3vg1K/gVZ+xx1KLSF1TYCwiIiVraSjjWOi5dr0SAi1sPv5VjIG2YJ7AOJ2GH/8lNG3g/wZeTqRQ67imdfDqz/D/7f4+b3fdDi/+CLzpG7D71eVdv4jUJAXGIiJSsuZghQJjbwPseRMXD/+MS4JRPO48L1cHvwlnH4Xn/QXBUGPensqx5Awv+Kdf8NPD5wAYSjVwpOFyuO42uPg3yrt2EalZCoxFRKRkLcFsjbHzUoqpeIpEKl34xKvfhpsZ/sB9Z54fNGx3kui+DC5/Hc0NXsbzBMYnh6M8MzDJD5/oB+x2bRcM9xCRuqfAWERESpYrpXA4/S6RSvPyT97HX377icInr7mY7wdeyqsSd8Kh84LjmRR8/a0wNQQv/2dwuewpfHkC9NMjUQAePTUGwGQsRTigwFhE5lNgLCIiJQv63HjdxnEpxVcfOsXRwSmeHphwdP5HeAsnAzvg2++CoSOzV/zkg3D8HvjNj8O6PQA0N/gYn04t+HNOZQLjZwYmGZ9OEoklafR7Ha1BROqH3i6LiEjJjDGZgLRwKUU0keLjd9vB7bnxhUc1z2VZFn1Tab5z5d/yR0feDl96rd05YnoUDn8Xrn0nXPmG3PnNDV4i00ksy8Kc14s4GxgDPNY7xmQ8NX8ctIgIyhiLiMgStTqcfnfHfccZmozzrIvaGZiIk05bi57fOzpNLJmmdf1WeM0dkIrDMz+GgUNw+evhRX8z7/yWoJfETJrpBYaN9I5GWd/SgDF2OcVELDV/HLSICMoYi4jIEjkZCz06leAzvzjGC3Z2cdMla/jVsWGGpuJ0hgMApNMWo9EE7XMGeTxyahSAqza1wLrnwvsOLnofzZl65/HpJEHf/Je3UyNRdq5rIuR388ipUSbjqjEWkQspYywiIkvS3OAruPnuS78+xWQixX9/0Xa6muxg+Nx4PHf9dw6c4Ya//ykDkdkSi0dPjRH0udneFXa0jnw9lS3L4vTINJvaguzZ2MpDx0eYSVuEA6oxFpH5FBiLiMiStAS9jBdo13ZscIp1zQ1s6wrTnQmM++cEwYfORoin0vzi6cHcZY+cGuXyDc35exifZ27GeK6hyQTTyRk2tjawZ1MLUwm71ELt2kTkfAqMRURkSVoavAUzxv2Rabqa7DKJ7uYLA+PTI9MA3PPMEGAP5Dh0NsKeTa2O19GUJ2Oc3Xi3qT047+eplEJEzqfAWERElqQl6CWamCGeunDTW1bfeIy1zQ0ArGn043aZeZ0pssHrvc8MMpO2ePzMOKm0xVVFBMYtmSl854+F7h3NBMZtQS7ubMxlihUYi8j5FBiLiMiSNGem3+WbOmdZFv3jsVym2O0ydIb99GUCY7sGOEpH2M9YNMnjZ8Z5NLPx7sqNLc7XkRs2Mr+s49SwHRhvaA3idhmu2NgMoBpjEbmAAmMREVmS7Ka38TydKSKxFNHEDGszgTFAV1OAc5lSivHpJBPxFK/duwFj4J6nB3n01Bgb2xroCPsX/JkLafR7cLvMBQH66dEonWE/Aa8bgD0bW3Pni4jMpWcFERFZkmwJQ7464/5MZrh7TmDc3RTgyOAkMFtGcfmGFi5b38w9Tw9yejTK9Re1F7UOe9jIha3jTo1E2dQWzH3/0svX8tCJEXrag+f/CBGpc8oYi4jIkrQ02KUU+XoZ943bG+vmZoy7mwO5GuPc5ri2IDdf0sHDp0Y5F4mzp4gyitm1eC/MGI9Ms3FOYLxjbRNffeezLuh1LCKiwFhERJYklzHO07JtNmPckLusqynARDzFVDyV60ixsS3Izds6sDID8a7qcb7xLqvpvMA4kUrTNz4/MBYRyUeBsYiILElzcOH+wVn9kRjGQOeceuHuZn/uulMjUdpCPhr9HvZsaiHs9+D3uLi0u6n4tZwXGJ8dmyZtMa+UQkQkH32OJCIiSxLObHrLV0rRPx5jTaMf75xBHbPT72KcHonmMrpet4uXXbGO8ekEPk/xuZuWoJcTw1O570/PadUmIlKIAmMREVmS3Ka36YVLKewexoF5l82dfnd6NMpl65tz1/3dqy8reS3nb77L1i9vbGvIdxMRkRyVUoiIyJK1BC/sBpHVPx7LBcJZ2Q4VZ8emOTM6XbaMbkuDl0gsSTptFyqfGonic7voCgcK3FJERIGxiIiUwULdILL6xqcvyBgHfR6aAh72nx4jlbbKFhg3NXixLJiIpQDoHZlmQ1sDLpcpy88XkdVNgbGIiCxZS9DH6AJdKabiKSKx1LyOFFndzQEeOmFPuCtX14js9LtskH50cJIe1ReLiEMKjEVEZMk6Gv30jcWwsr3WMvoz0+3OzxiDvQEvG8CWrZQiM556bDrBdGKGZwYm59Uvi4gsRoGxiIgs2aVrwwxPJRiciM+7PNvDuKvpwsA4W3fsdpkFA+dSzM0YH+qLMJO2uGxD8YNCRKQ+KTAWEZEl27XOzsoePBuZd3nfeP6McXYD3rqWAB53eV6OWub0VH68dwyAyzcoYywizigwFhGRJduxNgzAob75gfG5SHbq3cKlFFDeHsPZjPFYNMljZ8bpDPsXzFaLiCxEgbGIiCxZOOClpz3IwbPj8y7vG5+mNegl4HVfcJvuCgbG49NJHusdV7ZYRIqiwFhERMpi59omDp1XStE/HluwIwXMZpE3tJYvMA543fg9Ls6MTXN0cJLL1qu+WEScU2AsIiJlsWtdEyeGo0zEZvsZLzT1LmtrRyPXbmnjOds6yrqO5gYvvzo6jGWpvlhEiqPAWEREymLnuiYADvdP5C7rH4/lrfFt8Ln52jufxe4yt1NrCXo5PjQFUPafLSKrmwJjEREpi2xnimw5RSw5w/BUomyt2JzK1hmvaw7QEfYv632LSG1TYCwiImXRGfbTHvLlNuANROyexgt1pKik5gZ7yMdlKqMQkSIpMBYRkbIwxrBzXVOuZdu9RwYB2FjGzXVOZDPGl2uwh4gUyVFgbIy51RjzlDHmiDHm/Qtc7zfGfDVz/YPGmM3lXqiIiFS/neuaeLp/klPDUf7++4e5/qI2rtvStqxryA750MY7ESlWwcDYGOMGPgW8GNgJvMEYs/O8094OjFqWdTHwMeAfyr1QERGpfrvWNZOYSfO2z/2aGcviw791BS6XWdY1dIT9uF2G3esUGItIcTwOzrkWOGJZ1jEAY8xXgFcAh+ac8wrgQ5mvvw580hhjLMuyyrhWERGpcjvX2p0pjg5OcfsrdrGpfXnLKAB+57pNXLeljdaQb9nvW0Rqm5NSivXA6Tnf92YuW/Acy7JSwDjQXo4FiohI7diyJkQ44OH6i9p403U9K7KGpoCXPZtaV+S+RaS2OckYL/QZ2PmZYCfnYIy5DbgNYNOmTQ7uWkREaonbZfjWu26gqymw7CUUIiJL5SRj3AtsnPP9BuBsvnOMMR6gGRg5/wdZlvVZy7L2Wpa1t6OjvJOORESkOlzcGSYc8K70MkREiuYkMH4IuMQYs8UY4wNeD9x53jl3Am/JfP0a4KeqLxYRERGRWlKwlMKyrJQx5j3AXYAbuMOyrIPGmNuBfZZl3Qn8G/AFY8wR7Ezx6yu5aBERERGRcnNSY4xlWd8Hvn/eZR+Y83UM+O3yLk1EREREZPlo8p2IiIiICAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAigwFhEREREBFBgLCIiIiICKDAWEREREQEUGIuIiIiIAGAsy1qZOzZmEDi5IncOa4ChFbpvqW56bEg+emxIPnpsyGL0+KgOPZZldRQ6acUC45VkjNlnWdbelV6HVB89NiQfPTYkHz02ZDF6fNQWlVKIiIiIiKDAWEREREQEqN/A+LMrvQCpWnpsSD56bEg+emzIYvT4qCF1WWMsIiIiInK+es0Yi4iIiIjMU1eBsTHmVmPMU8aYI8aY96/0emRlGWNOGGMeN8bsN8bsy1zWZoz5sTHmmcyxdaXXKcvDGHOHMWbAGPPEnMsWfDwY2ycyzyWPGWOuWrmVS6XleWx8yBhzJvP8sd8Y85I51/155rHxlDHmRSuzalkOxpiNxpifGWOeNMYcNMa8N3O5njtqVN0ExsYYN/Ap4MXATuANxpidK7sqqQLPtSzryjmtdN4P3G1Z1iXA3ZnvpT58Drj1vMvyPR5eDFyS+e824NPLtEZZGZ/jwscGwMcyzx9XWpb1fYDM68rrgV2Z2/xL5vVHVqcU8KeWZe0ArgfenXkM6LmjRtVNYAxcCxyxLOuYZVkJ4CvAK1Z4TVJ9XgH8R+br/wBeuYJrkWVkWdY9wMh5F+d7PLwC+LxlewBoMcasXZ6VynLL89jI5xXAVyzLiluWdRw4gv36I6uQZVl9lmU9kvl6AngSWI+eO2pWPQXG64HTc77vzVwm9csCfmSMedgYc1vmsi7LsvrAfsIDOldsdVIN8j0e9HwiAO/JfBx+x5yyKz026pQxZjOwB3gQPXfUrHoKjM0Cl6klR3270bKsq7A/2nq3MebmlV6Q1Aw9n8inga3AlUAf8NHM5Xps1CFjTCPwDeCPLcuKLHbqApfp8VFF6ikw7gU2zvl+A3B2hdYiVcCyrLOZ4wDwLeyPO89lP9bKHAdWboVSBfI9HvR8UucsyzpnWdaMZVlp4F+ZLZfQY6POGGO82EHxFy3L+mbmYj131Kh6CowfAi4xxmwxxviwN0fcucJrkhVijAkZY8LZr4EXAk9gPybekjntLcB3VmaFUiXyPR7uBH43s8P8emA8+7Gp1Ifz6kJfhf38AfZj4/XGGL8xZgv2JqtfL/f6ZHkYYwzwb8CTlmX905yr9NxRozwrvYDlYllWyhjzHuAuwA3cYVnWwRVelqycLuBb9nMaHuBLlmX90BjzEPA1Y8zbgVPAb6/gGmUZGWO+DNwCrDHG9AIfBP6ehR8P3wdegr2xKgq8bdkXLMsmz2PjFmPMldgfg58A3glgWdZBY8zXgEPYHQvebVnWzEqsW5bFjcCbgceNMfszl/0v9NxRszT5TkRERESE+iqlEBERERHJS4GxiIiIiAgKjEVEREREAAXGIiIiIiKAAmMREREREUCBsYiIiIgIoMBYRERERARQYCwiIiIiAsD/A2y7UA9Yx4moAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x648 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 9))\n",
"plt.plot(y_test, label='actual')\n",
"plt.plot(predict, label='prediction')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"196/196 [==============================] - 0s 2ms/step\n",
"정확도 : 0.0102\n"
]
}
],
"source": [
"print(\"정확도 : %.4f\" %(model.evaluate(X_test,y_test)[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## GridSearch\n",
"하이퍼파라미터를 최적화하면 모델 성능을 향상시키는데 큰 도움이 된다.<br>\n",
"GridSearch는 리스트로 지정된 여러 하이퍼파라미터 값을 받아 모든 조합에 대해 모델 성능을 평가하여<br>\n",
"최적의 하이퍼파라미터 조합을 찾는다.\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"grid_param_LSTM = {\n",
" 'batch_size' : [10,20,30,40,50,75,100,150],\n",
" 'epochs' : [5,10,50,100],\n",
" 'learning_rate' : [0.0001,0.001,0.01,0.1],\n",
" 'optimizer' : ['Adam','RMSProp'],\n",
" 'activation' : ['relu','linear','tanh','sigmoid'],\n",
" 'dropout_rate' : [0.05,0.1,0.2,0.3,0.4,0.5,0.6],\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# module\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"del test['Unnamed: 0']\n",
"del validation['Unnamed: 0']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 지수이동평균 - 종가"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EMA_5 = test['close'].ewm(span=5).mean()\n",
"test_EMA_10 = test['close'].ewm(span=10).mean()\n",
"test_EMA_20 = test['close'].ewm(span=20).mean()\n",
"test_EMA_60 = test['close'].ewm(span=60).mean()\n",
"test_EMA_120 = test['close'].ewm(span=120).mean()\n",
"\n",
"test_EMA = pd.DataFrame({'5':test_EMA_5,'10':test_EMA_10,'20':test_EMA_20,'60':test_EMA_60,'120':test_EMA_120})\n",
"test_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_EMA_5 = validation['close'].ewm(span=5).mean()\n",
"val_EMA_10 = validation['close'].ewm(span=10).mean()\n",
"val_EMA_20 = validation['close'].ewm(span=20).mean()\n",
"val_EMA_60 = validation['close'].ewm(span=60).mean()\n",
"val_EMA_120 = validation['close'].ewm(span=120).mean()\n",
"\n",
"val_EMA = pd.DataFrame({'5':val_EMA_5,'10':val_EMA_10,'20':val_EMA_20,'60':val_EMA_60,'120':val_EMA_120})\n",
"val_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 지수이동평균 - 거래량"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EVMA_5 = test['vol'].ewm(span=5).mean()\n",
"test_EVMA_10 = test['vol'].ewm(span=10).mean()\n",
"test_EVMA_20 = test['vol'].ewm(span=20).mean()\n",
"test_EVMA_60 = test['vol'].ewm(span=60).mean()\n",
"test_EVMA_120 = test['vol'].ewm(span=120).mean()\n",
"\n",
"test_EVMA = pd.DataFrame({'5':test_EVMA_5,'10':test_EVMA_10,'20':test_EVMA_20,'60':test_EVMA_60,'120':test_EVMA_120})\n",
"test_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv\")\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_EVMA_5 = validation['vol'].ewm(span=5).mean()\n",
"val_EVMA_10 = validation['vol'].ewm(span=10).mean()\n",
"val_EVMA_20 = validation['vol'].ewm(span=20).mean()\n",
"val_EVMA_60 = validation['vol'].ewm(span=60).mean()\n",
"val_EVMA_120 = validation['vol'].ewm(span=120).mean()\n",
"\n",
"val_EVMA = pd.DataFrame({'5':val_EVMA_5,'10':val_EVMA_10,'20':val_EVMA_20,'60':val_EVMA_60,'120':val_EVMA_120})\n",
"val_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"scale_cols = ['close','open','high','low','vol']\n",
"scale_cols2 = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"\n",
"##----test_data----##\n",
"test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv')\n",
"\n",
"test_EMA_scaled = scaler.fit_transform(test_EMA[scale_cols2])\n",
"test_EMA_scaled = pd.DataFrame(test_EMA_scaled)\n",
"test_EMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv')\n",
"\n",
"val_EMA_scaled = scaler.fit_transform(val_EMA[scale_cols2])\n",
"val_EMA_scaled = pd.DataFrame(val_EMA_scaled)\n",
"val_EMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv')\n",
"\n",
"test_EVMA_scaled = scaler.fit_transform(test_EVMA[scale_cols2])\n",
"test_EVMA_scaled = pd.DataFrame(test_EVMA_scaled)\n",
"test_EVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
"\n",
"val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
"val_EVMA_scaled = pd.DataFrame(val_EVMA_scaled)\n",
"val_EVMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"test_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/test_EMA_scaled.csv\")\n",
"val_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/val_EMA_scaled.csv\")\n",
"test_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/test_EVMA_scaled.csv\")\n",
"val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/val_EVMA_scaled.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"recent_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# 데이터 역순으로 변경\n",
"recent_data = recent_data.loc[::-1]\n",
"recent_data = recent_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020년 09월 01일</td>\n",
" <td>309.81</td>\n",
" <td>309.08</td>\n",
" <td>311.15</td>\n",
" <td>307.63</td>\n",
" <td>342.81M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020년 09월 02일</td>\n",
" <td>311.50</td>\n",
" <td>311.69</td>\n",
" <td>312.92</td>\n",
" <td>308.52</td>\n",
" <td>337.04M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020년 09월 03일</td>\n",
" <td>316.43</td>\n",
" <td>314.73</td>\n",
" <td>317.46</td>\n",
" <td>314.45</td>\n",
" <td>342.18M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 2020년 09월 01일 309.81 309.08 311.15 307.63 342.81M\n",
"1 2020년 09월 02일 311.50 311.69 312.92 308.52 337.04M\n",
"2 2020년 09월 03일 316.43 314.73 317.46 314.45 342.18M"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"del recent_data['date']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/45 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
" 93%|█████████▎| 42/45 [00:10<00:00, 3.91it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 45/45 [00:11<00:00, 3.87it/s]\n"
]
}
],
"source": [
"def vol_char_to_num(data):\n",
" for i in tqdm(range(len(data))):\n",
" unit = data['vol'][i][-1]\n",
" data['vol'][i] = data['vol'][i][:-1]\n",
" if unit == 'K':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000\n",
" elif unit == 'M':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000000\n",
" \n",
" \n",
"vol_char_to_num(recent_data)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"scale_cols = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"recent_data_scaled = scaler.fit_transform(recent_data[scale_cols]) \n",
"recent_data_scaled = pd.DataFrame(recent_data_scaled)\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.377298</td>\n",
" <td>0.313141</td>\n",
" <td>0.298986</td>\n",
" <td>0.310670</td>\n",
" <td>0.840077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.454963</td>\n",
" <td>0.440645</td>\n",
" <td>0.393486</td>\n",
" <td>0.354839</td>\n",
" <td>0.825928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.681526</td>\n",
" <td>0.589155</td>\n",
" <td>0.635878</td>\n",
" <td>0.649132</td>\n",
" <td>0.838532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.479320</td>\n",
" <td>0.272594</td>\n",
" <td>0.378537</td>\n",
" <td>0.331017</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.554688</td>\n",
" <td>0.459697</td>\n",
" <td>0.474640</td>\n",
" <td>0.474442</td>\n",
" <td>0.648472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.725184</td>\n",
" <td>0.666341</td>\n",
" <td>0.634277</td>\n",
" <td>0.654591</td>\n",
" <td>0.556765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.559283</td>\n",
" <td>0.531021</td>\n",
" <td>0.552589</td>\n",
" <td>0.572208</td>\n",
" <td>0.486220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.686121</td>\n",
" <td>0.744993</td>\n",
" <td>0.684997</td>\n",
" <td>0.699256</td>\n",
" <td>0.573930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.682445</td>\n",
" <td>0.633610</td>\n",
" <td>0.599039</td>\n",
" <td>0.587097</td>\n",
" <td>0.404468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.890625</td>\n",
" <td>0.838300</td>\n",
" <td>0.836626</td>\n",
" <td>0.852109</td>\n",
" <td>0.432961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1.000000</td>\n",
" <td>0.935515</td>\n",
" <td>0.962093</td>\n",
" <td>0.951365</td>\n",
" <td>0.373302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.951746</td>\n",
" <td>1.000000</td>\n",
" <td>0.995195</td>\n",
" <td>1.000000</td>\n",
" <td>0.359644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.753676</td>\n",
" <td>0.910112</td>\n",
" <td>0.890550</td>\n",
" <td>0.771712</td>\n",
" <td>0.436688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.771599</td>\n",
" <td>0.807523</td>\n",
" <td>0.761345</td>\n",
" <td>0.756824</td>\n",
" <td>0.375975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.656710</td>\n",
" <td>0.755252</td>\n",
" <td>0.817405</td>\n",
" <td>0.665509</td>\n",
" <td>0.406184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.331801</td>\n",
" <td>0.628236</td>\n",
" <td>0.558996</td>\n",
" <td>0.299256</td>\n",
" <td>0.745795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.369485</td>\n",
" <td>0.431363</td>\n",
" <td>0.331554</td>\n",
" <td>0.095285</td>\n",
" <td>0.509637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.040441</td>\n",
" <td>0.106497</td>\n",
" <td>0.076348</td>\n",
" <td>0.028784</td>\n",
" <td>0.443921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.090533</td>\n",
" <td>0.132389</td>\n",
" <td>0.000000</td>\n",
" <td>0.027792</td>\n",
" <td>0.384116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.249540</td>\n",
" <td>0.186615</td>\n",
" <td>0.148959</td>\n",
" <td>0.162283</td>\n",
" <td>0.424133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.360294</td>\n",
" <td>0.358574</td>\n",
" <td>0.309130</td>\n",
" <td>0.354839</td>\n",
" <td>0.339880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.547335</td>\n",
" <td>0.340987</td>\n",
" <td>0.486385</td>\n",
" <td>0.383127</td>\n",
" <td>0.386886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.590533</td>\n",
" <td>0.608696</td>\n",
" <td>0.583556</td>\n",
" <td>0.578660</td>\n",
" <td>0.342504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.723805</td>\n",
" <td>0.472399</td>\n",
" <td>0.634810</td>\n",
" <td>0.518610</td>\n",
" <td>0.304595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.729320</td>\n",
" <td>0.861749</td>\n",
" <td>0.791778</td>\n",
" <td>0.755831</td>\n",
" <td>0.413124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.806526</td>\n",
" <td>0.797264</td>\n",
" <td>0.772023</td>\n",
" <td>0.808933</td>\n",
" <td>0.345079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.826287</td>\n",
" <td>0.897411</td>\n",
" <td>0.848905</td>\n",
" <td>0.774690</td>\n",
" <td>0.310260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.687960</td>\n",
" <td>0.827064</td>\n",
" <td>0.765083</td>\n",
" <td>0.711663</td>\n",
" <td>0.316635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.566176</td>\n",
" <td>0.639472</td>\n",
" <td>0.552056</td>\n",
" <td>0.547395</td>\n",
" <td>0.304546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.470129</td>\n",
" <td>0.573522</td>\n",
" <td>0.529632</td>\n",
" <td>0.411911</td>\n",
" <td>0.309524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.505055</td>\n",
" <td>0.525647</td>\n",
" <td>0.501335</td>\n",
" <td>0.536973</td>\n",
" <td>0.338409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.602482</td>\n",
" <td>0.407914</td>\n",
" <td>0.499733</td>\n",
" <td>0.425806</td>\n",
" <td>0.332181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>0.650735</td>\n",
" <td>0.618955</td>\n",
" <td>0.590497</td>\n",
" <td>0.634739</td>\n",
" <td>0.334952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>0.559283</td>\n",
" <td>0.534929</td>\n",
" <td>0.466631</td>\n",
" <td>0.505211</td>\n",
" <td>0.344564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.606158</td>\n",
" <td>0.601368</td>\n",
" <td>0.548852</td>\n",
" <td>0.542928</td>\n",
" <td>0.485043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>0.548254</td>\n",
" <td>0.633122</td>\n",
" <td>0.599573</td>\n",
" <td>0.593548</td>\n",
" <td>0.418494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>0.468750</td>\n",
" <td>0.431363</td>\n",
" <td>0.447411</td>\n",
" <td>0.455583</td>\n",
" <td>0.397504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.531710</td>\n",
" <td>0.390327</td>\n",
" <td>0.413775</td>\n",
" <td>0.400496</td>\n",
" <td>0.294248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>0.383272</td>\n",
" <td>0.308745</td>\n",
" <td>0.267485</td>\n",
" <td>0.261042</td>\n",
" <td>0.340420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>0.000000</td>\n",
" <td>0.304836</td>\n",
" <td>0.188468</td>\n",
" <td>0.010422</td>\n",
" <td>0.438184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>0.199908</td>\n",
" <td>0.000000</td>\n",
" <td>0.046983</td>\n",
" <td>0.000000</td>\n",
" <td>0.271738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>0.481158</td>\n",
" <td>0.267220</td>\n",
" <td>0.363054</td>\n",
" <td>0.329529</td>\n",
" <td>0.315801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>0.562040</td>\n",
" <td>0.568637</td>\n",
" <td>0.537640</td>\n",
" <td>0.496278</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>0.914982</td>\n",
" <td>0.638495</td>\n",
" <td>0.852109</td>\n",
" <td>0.689826</td>\n",
" <td>0.455789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.942096</td>\n",
" <td>0.979482</td>\n",
" <td>1.000000</td>\n",
" <td>0.954839</td>\n",
" <td>0.352484</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"0 0.377298 0.313141 0.298986 0.310670 0.840077\n",
"1 0.454963 0.440645 0.393486 0.354839 0.825928\n",
"2 0.681526 0.589155 0.635878 0.649132 0.838532\n",
"3 0.479320 0.272594 0.378537 0.331017 1.000000\n",
"4 0.554688 0.459697 0.474640 0.474442 0.648472\n",
"5 0.725184 0.666341 0.634277 0.654591 0.556765\n",
"6 0.559283 0.531021 0.552589 0.572208 0.486220\n",
"7 0.686121 0.744993 0.684997 0.699256 0.573930\n",
"8 0.682445 0.633610 0.599039 0.587097 0.404468\n",
"9 0.890625 0.838300 0.836626 0.852109 0.432961\n",
"10 1.000000 0.935515 0.962093 0.951365 0.373302\n",
"11 0.951746 1.000000 0.995195 1.000000 0.359644\n",
"12 0.753676 0.910112 0.890550 0.771712 0.436688\n",
"13 0.771599 0.807523 0.761345 0.756824 0.375975\n",
"14 0.656710 0.755252 0.817405 0.665509 0.406184\n",
"15 0.331801 0.628236 0.558996 0.299256 0.745795\n",
"16 0.369485 0.431363 0.331554 0.095285 0.509637\n",
"17 0.040441 0.106497 0.076348 0.028784 0.443921\n",
"18 0.090533 0.132389 0.000000 0.027792 0.384116\n",
"19 0.249540 0.186615 0.148959 0.162283 0.424133\n",
"20 0.360294 0.358574 0.309130 0.354839 0.339880\n",
"21 0.547335 0.340987 0.486385 0.383127 0.386886\n",
"22 0.590533 0.608696 0.583556 0.578660 0.342504\n",
"23 0.723805 0.472399 0.634810 0.518610 0.304595\n",
"24 0.729320 0.861749 0.791778 0.755831 0.413124\n",
"25 0.806526 0.797264 0.772023 0.808933 0.345079\n",
"26 0.826287 0.897411 0.848905 0.774690 0.310260\n",
"27 0.687960 0.827064 0.765083 0.711663 0.316635\n",
"28 0.566176 0.639472 0.552056 0.547395 0.304546\n",
"29 0.470129 0.573522 0.529632 0.411911 0.309524\n",
"30 0.505055 0.525647 0.501335 0.536973 0.338409\n",
"31 0.602482 0.407914 0.499733 0.425806 0.332181\n",
"32 0.650735 0.618955 0.590497 0.634739 0.334952\n",
"33 0.559283 0.534929 0.466631 0.505211 0.344564\n",
"34 0.606158 0.601368 0.548852 0.542928 0.485043\n",
"35 0.548254 0.633122 0.599573 0.593548 0.418494\n",
"36 0.468750 0.431363 0.447411 0.455583 0.397504\n",
"37 0.531710 0.390327 0.413775 0.400496 0.294248\n",
"38 0.383272 0.308745 0.267485 0.261042 0.340420\n",
"39 0.000000 0.304836 0.188468 0.010422 0.438184\n",
"40 0.199908 0.000000 0.046983 0.000000 0.271738\n",
"41 0.481158 0.267220 0.363054 0.329529 0.315801\n",
"42 0.562040 0.568637 0.537640 0.496278 0.000000\n",
"43 0.914982 0.638495 0.852109 0.689826 0.455789\n",
"44 0.942096 0.979482 1.000000 0.954839 0.352484"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recent_data_scaled"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"recent_data_scaled.to_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/recent_test/recent_data_scaled.csv')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checking if any null values are present in train\n",
" 0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(\"checking if any null values are present in train\\n\", recent_data_scaled.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
This diff could not be displayed because it is too large.
,5,10,20,60,120
0,258.23,258.23,258.23,258.23,258.23
1,256.754,256.877,256.93850000000003,256.97950000000003,256.98975
2,257.29210526315785,257.284219269103,257.28788509575355,257.29317192852517,257.29484849887734
3,259.0690769230769,258.69619306930696,258.52406546134665,258.4164232389151,258.3905032665555
4,259.29208530805687,258.9700050979393,258.7964194447519,258.6798557059372,258.65086863840196
5,261.59708270676697,260.69204378608526,260.2316641720196,259.93142485971003,259.8578466845241
6,262.9966342884896,261.8626160848444,261.23722983865105,260.81653393223087,260.7122295260482
7,264.43036478984936,263.0609713105013,262.2558247709728,261.7008022811835,261.5620202477884
8,264.6218919200877,263.480660448391,262.6943998471738,262.1168577053982,261.96878874171955
9,266.422486342094,264.8353839813254,263.784018534914,263.0204799487037,262.8259340510115
10,268.0437341732392,266.1417240888236,264.84650666497754,263.89728637930295,263.65518459826257
11,269.3324218111484,267.2882211136662,265.8046664832544,264.6911126380856,264.4051436870139
12,270.8561102708415,268.5819846185956,266.8614371735467,265.5477822216714,265.20906874440567
13,271.90767563779946,269.6302262275009,267.7634820824311,266.2908598462384,265.90726373574864
14,272.1757292346771,270.2192156165635,268.36966821457696,266.82571369413233,266.41558212959524
15,272.74134727532527,270.9108877388209,269.0258041144034,267.3844215842777,266.94196378918895
16,274.03220835491356,271.9824459397944,269.90927528657306,268.0836049384952,267.5896279621581
17,276.5798627468304,273.79268842071457,271.25075685006703,269.0708322411852,268.4875645495612
18,278.2673363720763,275.251698230733,272.4139031106909,269.9491220978547,269.2884863746996
19,279.8453654732943,276.6864078620298,273.57959956579685,270.82845793314755,270.0879826749492
20,282.10402981226264,278.5196211409037,274.99450309507466,271.85687918545244,271.0132795973617
21,283.3661885714249,279.87610004696313,276.16120677842935,272.74215557390914,271.8143350753054
22,284.1741977117665,280.96210381224364,277.1802020754074,273.5410705694583,272.539799709633
23,284.6661610320359,281.8214075035287,278.06715065145215,274.2619911479139,273.1970839732322
24,283.6073987587848,281.7607496804508,278.42222154917766,274.68110874524695,273.5993339084041
25,283.1049192396844,281.82276776919645,278.8005230669489,275.10072116909487,273.9988846490123
26,283.2999495925083,282.1637770946023,279.2996565552836,275.5752496871896,274.44091895561036
27,283.85663959385255,282.6758578166888,279.87456806791425,276.08287661241144,274.9076020938005
28,285.4644389731416,283.77077108913943,280.76188568168936,276.7493769228975,275.50153171530036
29,284.9696234015842,283.8089053517565,281.08438910849867,277.1243953114287,275.8576902693871
30,285.279750012616,284.18986159447525,281.5645957465442,277.5710236666602,276.26906066628356
31,285.1564997227387,284.3210091321792,281.8967071624635,277.93789275657684,276.6145829470122
32,286.2376681524617,285.0636319618075,282.5397194882892,278.4520399743216,277.07504107767113
33,286.9617795142455,285.6727256127865,283.11803956570304,278.933535742782,277.5081372154855
34,287.55452008323505,286.2309090531902,283.6700861061186,279.4004384664283,277.9281874920824
35,288.0363469427568,286.7347473193366,284.1919124157058,279.850813454504,278.33378765448106
36,287.9875646136148,286.9449185802487,284.55301134650165,280.2227266904902,278.6769664602377
37,288.2450431281362,287.27509446274536,284.96281554025643,280.61243116302387,279.0321792822799
38,286.76336188442434,286.64300680401925,284.8497908008599,280.75608794354514,279.19706046758625
39,285.34557446139473,285.8913055289536,284.6228101412712,280.83417396804845,279.3095971835
40,283.71371620920877,284.90171282793114,284.21872576829355,280.81726850727125,279.3476713353614
41,282.42581075437243,283.98301874032524,283.7963449666443,280.7751763277008,279.3641643390049
42,281.240540471154,283.0532126530515,283.3207390866044,280.69314976090726,279.3481996857966
43,279.10702694265575,281.55968268370725,282.50304736828883,280.44370606745224,279.2048183344613
44,277.7580179457045,280.3777806738887,281.78625269415755,280.2165020981733,279.0749775719403
45,277.74867863039555,279.89631883722217,281.3960355446574,280.1125451466407,279.0334590987892
46,277.1657857505117,279.1878404409142,280.8774289931842,279.94214305957877,278.941142291463
47,276.990523833056,278.7245663414591,280.4705290314361,279.8064928209245,278.8720723158253
48,277.95034922429534,278.93283818227656,280.4129084651248,279.8090802051915,278.90162703875194
49,278.6102328172318,279.11414829095656,280.3666065017148,279.81396779207296,278.9316899929241
50,279.0134885452428,279.24248957539703,280.31423077437836,279.8142097673128,278.957332635734
51,279.3156590303725,279.36567690936425,280.27647762052817,279.81842255650537,278.9847830563672
52,280.337106020723,279.9137488319882,280.4778136373262,279.91971731845786,279.08045085164485
53,281.2680706807704,280.49853327661873,280.7315436537873,280.0458123891557,279.1932420871645
54,279.99538045358406,279.9442455767773,280.4177391125502,279.9445103994025,279.1452315983782
55,279.19692030227947,279.5180134947391,280.1483916405203,279.85358274118664,279.1031380344263
56,278.55794686812766,279.1110975654927,279.8742987313099,279.75436585783285,279.05400040056605
57,277.3986312453475,278.378164273592,279.4163189889481,279.57519244830127,278.94799713578936
58,277.09242083021917,278.0330410070653,279.1359051564629,279.45720457897653,278.8828264709683
59,278.1682805535087,278.4488541871277,279.24895491657196,279.4899196348161,278.9204057175347
60,279.35218703569393,279.0436108432477,279.48481886887026,279.57404710743407,278.9929121271494
61,281.4514580238213,280.24477725372435,280.0731668255934,279.80213007520354,279.163722881502
62,283.0176386825602,281.3184575873584,280.6529719577598,280.03929349591215,279.34135839838063
63,284.23842578838,282.2932860587681,281.2279248565423,280.28626789951016,279.52630768539166
64,284.9522838589226,283.03632656504436,281.71933348570985,280.5119060547654,279.69755000784016
65,285.53818923928316,283.7042683721522,282.1952789629318,280.74043929605557,279.87128972869556
66,286.33212615952334,284.4707661420834,282.74115867334405,281.0040838281179,280.0690726155484
67,286.6947507730159,285.006991115415,283.18725665031224,281.2387627754123,280.24826721858705
68,287.253167182011,285.6184478691789,283.68134633223036,281.4986214094514,280.44471012796674
69,287.9621114546743,286.30236698103494,284.22456772128925,281.7847696250833,280.6591882420747
70,288.8480743031165,287.0873916758912,284.8341565128582,282.10442403702376,280.89650859190186
71,289.3420495354111,287.6769571388553,285.3579588988678,282.4010152175927,281.1196372688798
72,288.1680330236072,287.3393284214386,285.4019923789068,282.5238923658799,281.2300276940925
73,287.4086886824047,287.07581406930564,285.4484975465949,282.6444889138027,281.33871344765976
74,286.89579245493644,286.8565750838381,285.48866271350505,282.75969813520555,281.44368462029587
75,286.75386163662426,286.7862886882299,285.58216990738674,282.8918358483666,281.5593558609132
76,285.7359077577495,286.22514518109756,285.4028349429388,282.92053646119297,281.60830140625603
77,284.76727183849965,285.60784595890993,285.157703256296,282.91732995097937,281.63605950941025
78,283.6248478923331,284.831873865227,284.7939785075437,282.8616125292736,281.6293741636507
79,284.67989859488875,285.18789683679415,284.98413915983275,283.00002727812563,281.7452056773765
80,284.75993239659255,285.1391883167629,284.9780288261906,283.0675113727403,281.81604560363274
81,285.21328826439503,285.3175177264475,285.08681766105934,283.17454888571365,281.9115284398975
82,284.69885884293006,285.01796903141303,284.95184933536416,283.19188270375724,281.95032056368666
83,283.56923922862,284.34379281166093,284.6049290754747,283.1261877532023,281.9362727059051
84,282.42949281908,283.58128499790456,284.180564381474,283.0225115587212,281.89729491034666
85,278.6196618793867,281.29377856146,282.92504303998203,282.60456067356387,281.6607595530713
86,276.4331079195911,279.6149096881544,281.8901058776147,282.2387126415058,281.45343991843174
87,273.8620719463941,277.63401697515354,280.63562237126234,281.77056861011016,281.17985864545705
88,272.0980479642628,275.9860138598823,279.48635989205684,281.3142823795561,280.91026872622245
89,271.27869864284185,274.8321931415422,278.5484964554747,280.9114697330266,280.670481356293
90,269.5157990952279,273.22452164240224,277.3523166122152,280.39749793171285,280.3596137028769
91,267.79386606348527,271.6109722373876,276.11387657024613,279.8456550395816,280.0221688941702
92,266.8492440423235,270.4017045483663,275.0515062357263,279.33458404397464,279.70613154453196
93,266.60616269488236,269.62321280729776,274.2008167716572,278.88158657194276,279.4223285478079
94,266.6207751299216,269.08262865767455,273.48163795810416,278.4629115455051,279.1566834963994
95,266.54051675328105,268.59124162689295,272.80524604030745,278.04992109844505,278.8920776185076
96,265.83367783552075,267.8328340556981,272.00660263028925,277.5847022786996,278.5936079687658
97,265.35578522368047,267.2086824074104,271.28212443830927,277.1352843408419,278.30207522769797
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184,0.036436761648741656,0.0724345636090924,0.1352892679452311,0.18378141780805457,0.2592283201845156
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187,0.019681453291206852,0.026165617551101805,0.08053550254039332,0.13896341902085263,0.23073136791762394
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189,0.24292519277645974,0.17041060022667076,0.15982851391723907,0.16388914225404116,0.2416694161538948
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191,0.1708009701165375,0.13870267690050686,0.13788330064337861,0.14423944504604203,0.2283002607978002
192,0.09005078390399202,0.0855262162524888,0.10186673722263317,0.12224264992971001,0.21482342969243984
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207,0.18459977760305368,0.12571578951204665,0.07710986158578037,0.02222773617858964,0.13165719101034323
208,0.2132552581887286,0.15522425891343938,0.0995014338584197,0.02966326691515242,0.13388703822858217
209,0.1992561770934903,0.1560446528442887,0.10448160128252515,0.029475670278793498,0.13203561366311023
210,0.20347588998218402,0.16626440176350177,0.11524271842803469,0.03231510025474327,0.13187068599089313
211,0.150797310468624,0.13552870628670166,0.0993662487797149,0.022692413412081613,0.12493959013909306
212,0.1340381421932323,0.12331697963928723,0.09347599887399971,0.017477976645729587,0.12036837549426149
213,0.09795818804345846,0.0957769401319637,0.07665058747824349,0.006883388895166487,0.11284188354643376
214,0.16378132849926508,0.13656762612359286,0.10291090182229445,0.016668145000708012,0.11639432273070449
215,0.17053158543076208,0.1437801760851003,0.10953169036914856,0.01785596162044545,0.11536402717006311
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217,0.11466508669232511,0.10226840647350177,0.08387507515071801,0.001228226248909614,0.1029076806578555
218,0.14648662148147995,0.12184629416604653,0.09632399471295172,0.004861195595009082,0.10316531474213342
219,0.20524488195170165,0.1643165401287534,0.124916039815967,0.016741591855204963,0.10798105798722935
220,0.21053597155138482,0.17519362938603655,0.13514690455586642,0.020681692377073624,0.10859738525802198
221,0.16901067988489815,0.15235069699397252,0.12360891691185882,0.014452833027378542,0.10373259728132744
222,0.3931918052738208,0.31111506180835447,0.229420250888138,0.06455326017902774,0.1295214748491551
223,0.27425276091470074,0.25191382134783913,0.20127497853756093,0.05320232410109149,0.12230113989750846
224,0.19825660258257627,0.20579905515926833,0.17733175594911588,0.042958531748380935,0.11560572519912915
225,0.20702174985644306,0.2099403315849413,0.18309896287239424,0.046293270549424115,0.11623053028049757
226,0.17628276765119488,0.18755410305415676,0.17143195979349724,0.04136723723830826,0.11241158157397901
227,0.15885772451142266,0.17139941773694156,0.16229198446772708,0.03728639355882679,0.10903024942594008
228,0.21427063319977346,0.20540839945594547,0.1849606181765775,0.04827456899410887,0.11382358589266106
229,0.18065750405962533,0.18352357353358495,0.1729049885328482,0.04318177316948235,0.10999421299919376
230,0.1747314408359686,0.1772308687199986,0.16960525225577672,0.04192855630865999,0.10822523760742353
231,0.1726579272174037,0.17340489076505028,0.16748621342282766,0.04113470996661883,0.10671381194223128
232,0.10786301533843645,0.12559651554755713,0.13630032849383555,0.026238601598600475,0.09756127356886557
233,0.11127579060828652,0.11931973041964139,0.12959752075813502,0.022215717405905444,0.09419985947221088
234,0.4193964355199489,0.3296709932821569,0.2646988837819433,0.08646530492113502,0.12785030915820395
235,0.46900299742629903,0.39200087899866753,0.31501919208889273,0.11389428182134953,0.14210293980378275
236,0.3835818150296635,0.3595130469206478,0.3058559057383001,0.1140246138181027,0.14180326971369617
237,0.4210893281921302,0.39948138950473244,0.3411618795149671,0.13519385584087118,0.15291005074281516
238,0.3661532927153628,0.3758594107365998,0.33620784951315663,0.1378590480105748,0.15417991657348562
239,0.2987615819294924,0.3348546217453985,0.3175245206570336,0.13358236471883345,0.15171649944268717
240,0.2802976484946442,0.3199506649049557,0.31283520272494547,0.1353415561957716,0.15248665712241616
241,0.20443843432535158,0.2629817132657306,0.27926042936572393,0.12288567150375584,0.14558228519487715
242,0.14708940806973025,0.21159649407301528,0.24575562778073468,0.10932886950193721,0.13797944434474774
243,0.14488971463592726,0.19494148555091084,0.23207312060606267,0.10424392015774453,0.1348481215552857
244,0.18476733193396788,0.2104440905372884,0.23877645257524072,0.10853595623071133,0.13675024998566832
245,0.10815245953504815,0.15041736948751838,0.19720847804103991,0.08969767969935116,0.12619292998359333
......
,date,close,open,high,low,vol
210,2020년 01월 02일,290.35,294.19,294.57,289.96,66160000.0
209,2020년 01월 03일,290.74,293.1,294.88,289.04,79770000.0
208,2020년 01월 06일,288.43,287.73,289.41,287.49,69960000.0
207,2020년 01월 07일,291.23,290.01,292.26,289.64,65349999.99999999
206,2020년 01월 08일,289.42,288.94,291.01,287.3,124530000.0
205,2020년 01월 09일,294.41,293.83,294.41,292.1,111790000.0
204,2020년 01월 10일,297.06,294.8,297.17,294.17,94160000.0
203,2020년 01월 13일,300.13,296.62,300.31,296.53,81680000.0
202,2020년 01월 14일,301.53,302.2,303.61,300.69,92120000.0
201,2020년 01월 15일,299.74,299.91,301.13,298.82,71770000.0
200,2020년 01월 16일,302.78,299.93,302.91,298.95,85710000.0
199,2020년 01월 17일,303.3,305.07,306.05,302.51,81540000.0
198,2020년 01월 20일,305.58,304.99,307.54,304.77,78040000.0
197,2020년 01월 21일,302.11,304.94,306.09,301.85,67630000.0
196,2020년 01월 22일,306.08,301.79,306.52,301.16,79330000.0
195,2020년 01월 23일,302.33,303.77,304.72,301.71,86910000.0
194,2020년 01월 28일,292.77,294.98,296.3,291.3,130169999.99999999
193,2020년 01월 29일,293.98,294.38,295.67,292.45,85730000.0
192,2020년 01월 30일,288.37,293.27,294.11,287.09,101540000.0
191,2020년 01월 31일,284.53,290.24,291.47,284.53,101460000.0
190,2020년 02월 03일,285.05,280.17,286.24,279.78,111060000.0
189,2020년 02월 04일,290.68,285.19,291.38,285.11,105450000.0
188,2020년 02월 05일,292.02,293.57,294.26,290.3,102640000.0
187,2020년 02월 06일,300.65,294.74,300.98,294.32,110220000.0
186,2020년 02월 07일,298.21,299.59,300.05,296.01,108110000.0
185,2020년 02월 10일,296.24,294.07,296.73,293.33,72220000.0
184,2020년 02월 11일,299.28,297.91,301.1,297.48,78350000.0
183,2020년 02월 12일,301.54,299.83,302.2,298.09,110630000.0
182,2020년 02월 13일,300.93,302.79,304.43,300.48,115070000.0
181,2020년 02월 14일,303.01,300.95,304.2,299.18,80530000.0
180,2020년 02월 17일,302.76,302.84,304.05,300.99,84280000.0
179,2020년 02월 18일,297.74,300.26,301.29,297.2,84750000.0
178,2020년 02월 19일,298.33,299.68,300.38,295.53,84260000.0
177,2020년 02월 20일,296.65,300.34,301.35,295.01,90010000.0
176,2020년 02월 21일,292.42,292.16,295.24,291.96,100710000.0
175,2020년 02월 24일,281.02,285.51,286.62,281.02,132820000.0
174,2020년 02월 25일,284.24,280.48,284.63,279.77,117030000.0
173,2020년 02월 26일,279.94,278.45,281.98,277.68,119550000.0
172,2020년 02월 27일,277.09,279.46,281.09,276.07,110500000.0
171,2020년 02월 28일,268.02,272.33,273.98,267.23,202610000.0
170,2020년 03월 02일,270.37,269.59,272.6,265.7,163720000.0
169,2020년 03월 03일,271.56,277.44,277.54,270.79,146720000.0
168,2020년 03월 04일,278.13,270.13,278.9,270.13,208460000.0
167,2020년 03월 05일,281.38,280.66,282.05,277.91,147440000.0
166,2020년 03월 06일,275.1,276.74,278.31,274.0,140900000.0
165,2020년 03월 09일,263.11,266.39,267.75,261.76,182670000.0
164,2020년 03월 10일,264.67,261.94,265.57,260.75,173090000.0
163,2020년 03월 11일,257.01,264.63,265.04,255.69,194210000.0
162,2020년 03월 12일,247.62,253.93,255.49,243.76,242470000.0
161,2020년 03월 13일,240.65,231.71,246.19,227.36,298900000.0
160,2020년 03월 16일,232.97,245.21,245.21,232.76,175050000.0
159,2020년 03월 17일,226.89,222.94,234.56,222.46,216730000.0
158,2020년 03월 18일,215.83,228.63,230.04,215.81,208040000.0
157,2020년 03월 19일,199.28,220.72,220.85,196.27,303560000.0
156,2020년 03월 20일,213.67,204.84,214.08,200.54,269350000.0
155,2020년 03월 23일,201.87,200.8,206.66,198.67,210300000.0
154,2020년 03월 24일,220.34,207.65,220.34,205.41,225730000.0
153,2020년 03월 25일,232.89,228.37,233.42,224.74,306110000.0
152,2020년 03월 26일,229.34,231.91,236.61,229.22,290840000.0
151,2020년 03월 27일,233.79,239.11,239.48,227.01,249100000.0
150,2020년 03월 30일,232.45,227.05,234.7,225.09,167230000.0
149,2020년 03월 31일,236.82,235.2,237.47,232.5,240440000.0
148,2020년 04월 01일,226.35,234.23,237.52,226.34,228580000.0
147,2020년 04월 02일,231.84,227.47,232.22,223.6,181790000.0
146,2020년 04월 03일,231.7,232.48,234.45,229.23,181040000.0
145,2020년 04월 06일,240.81,234.4,241.19,233.75,190740000.0
144,2020년 04월 07일,244.87,245.65,247.39,240.68,224450000.0
143,2020년 04월 08일,241.89,243.71,246.99,241.77,173120000.0
142,2020년 04월 09일,245.61,246.13,246.46,243.11,175940000.0
141,2020년 04월 10일,248.0,245.14,248.21,244.02,286110000.0
140,2020년 04월 13일,243.4,246.59,246.9,243.39,195900000.0
139,2020년 04월 14일,247.45,246.2,248.55,244.58,193120000.0
138,2020년 04월 16일,247.1,244.99,247.87,243.61,197540000.0
137,2020년 04월 17일,255.02,252.52,257.37,252.38,287360000.0
136,2020년 04월 20일,252.14,253.85,255.93,251.46,211000000.0
135,2020년 04월 21일,249.4,250.26,251.88,245.11,257160000.00000003
134,2020년 04월 22일,251.88,246.16,252.18,244.56,184500000.0
133,2020년 04월 23일,253.74,253.42,255.22,251.58,163810000.0
132,2020년 04월 24일,250.28,252.47,252.86,249.19,154310000.0
131,2020년 04월 27일,254.84,251.18,255.79,250.61,151360000.0
130,2020년 04월 28일,256.39,255.91,257.34,253.22,172420000.0
129,2020년 04월 29일,258.15,256.58,259.67,256.04,184480000.0
128,2020년 05월 04일,250.6,252.44,253.7,250.34,174040000.0
127,2020년 05월 06일,255.0,253.76,255.01,251.26,133280000.0
126,2020년 05월 07일,254.46,253.88,256.14,253.37,114360000.0
125,2020년 05월 08일,256.62,256.66,258.29,256.23,125220000.0
124,2020년 05월 11일,254.95,257.67,258.87,254.59,144300000.0
123,2020년 05월 12일,253.37,255.33,255.56,250.98,166800000.0
122,2020년 05월 13일,255.85,250.03,255.85,249.95,141250000.0
121,2020년 05월 14일,253.65,253.26,254.39,252.09,167640000.0
120,2020년 05월 15일,253.79,255.34,255.49,252.02,228900000.0
119,2020년 05월 18일,255.44,254.55,256.31,252.95,223000000.0
118,2020년 05월 19일,261.79,261.12,262.34,259.96,253330000.0
117,2020년 05월 20일,262.72,260.48,263.25,260.48,140300000.0
116,2020년 05월 21일,263.74,264.52,264.85,263.07,159480000.0
115,2020년 05월 22일,259.62,263.82,264.07,258.34,166030000.0
114,2020년 05월 25일,262.76,260.95,262.82,259.16,125430000.0
113,2020년 05월 26일,267.31,263.48,267.31,262.95,175420000.0
112,2020년 05월 27일,267.64,266.71,269.17,265.86,247180000.0
111,2020년 05월 28일,268.29,270.17,271.44,264.85,248930000.0
110,2020년 05월 29일,268.32,266.62,269.61,265.23,269470000.0
109,2020년 06월 01일,273.19,269.29,273.39,268.93,171510000.0
108,2020년 06월 02일,276.08,272.65,276.47,272.22,251030000.0
107,2020년 06월 03일,285.91,279.14,287.49,278.98,476500000.0
106,2020년 06월 04일,286.45,291.17,292.9,284.93,364580000.0
105,2020년 06월 05일,290.62,286.3,291.5,284.5,282060000.0
104,2020년 06월 08일,290.77,295.41,295.68,289.71,286660000.0
103,2020년 06월 09일,291.32,293.96,294.87,287.83,268570000.0
102,2020년 06월 10일,291.9,291.03,292.92,289.6,229070000.0
101,2020년 06월 11일,288.62,289.98,292.08,284.29,275900000.0
100,2020년 06월 12일,281.78,276.97,282.54,276.05,232830000.0
99,2020년 06월 15일,267.95,278.61,280.87,267.95,262440000.0
98,2020년 06월 16일,282.59,275.89,282.59,274.0,247890000.0
97,2020년 06월 17일,283.02,282.08,284.9,278.33,337220000.0
96,2020년 06월 18일,281.91,281.74,283.39,280.02,181590000.0
95,2020년 06월 19일,283.37,284.56,284.7,278.53,189130000.0
94,2020년 06월 22일,281.42,281.01,283.71,280.34,139840000.0
93,2020년 06월 23일,281.94,284.64,286.04,279.26,163680000.0
92,2020년 06월 24일,286.7,283.71,288.3,283.3,200570000.0
91,2020년 06월 25일,279.73,282.38,284.36,279.73,152980000.0
90,2020년 06월 26일,283.38,283.36,284.58,280.65,169010000.0
89,2020년 06월 29일,278.04,279.36,281.77,277.06,137760000.0
88,2020년 06월 30일,280.09,282.55,283.98,280.09,179500000.0
87,2020년 07월 01일,280.26,283.1,283.84,279.44,133800000.00000001
86,2020년 07월 02일,283.86,281.3,283.89,280.71,111370000.0
85,2020년 07월 03일,285.89,285.59,286.09,283.36,100220000.0
84,2020년 07월 06일,290.62,286.84,291.14,286.31,128120000.0
83,2020년 07월 07일,286.77,292.74,292.98,286.76,156130000.0
82,2020년 07월 08일,285.97,287.2,288.41,285.18,119450000.0
81,2020년 07월 09일,287.25,287.8,289.41,287.08,140610000.0
80,2020년 07월 10일,285.06,287.87,288.09,283.51,136770000.0
79,2020년 07월 13일,289.84,287.92,290.49,286.86,127050000.0
78,2020년 07월 14일,289.63,288.42,289.63,286.89,126300000.0
77,2020년 07월 15일,292.27,292.94,294.42,290.89,168350000.0
76,2020년 07월 16일,289.25,292.34,292.74,288.46,129449999.99999999
75,2020년 07월 17일,291.57,289.9,292.1,289.9,118640000.0
74,2020년 07월 20일,290.81,292.76,292.76,289.01,161820000.0
73,2020년 07월 21일,295.16,294.03,296.11,293.2,366990000.0
72,2020년 07월 22일,294.04,294.71,296.14,293.39,327320000.0
71,2020년 07월 23일,292.37,293.62,293.62,290.49,250980000.0
70,2020년 07월 24일,290.66,289.84,292.74,289.42,216210000.0
69,2020년 07월 27일,293.51,291.03,295.26,290.82,193240000.0
68,2020년 07월 28일,300.13,296.53,301.34,296.38,301280000.0
67,2020년 07월 29일,301.25,300.57,303.51,299.61,264440000.0
66,2020년 07월 30일,301.85,303.13,304.13,301.49,191760000.0
65,2020년 07월 31일,299.32,303.69,304.16,298.89,201960000.0
64,2020년 08월 03일,299.46,299.57,300.33,297.57,179730000.0
63,2020년 08월 04일,303.04,302.5,304.27,301.48,219870000.0
62,2020년 08월 05일,306.64,303.94,306.67,302.91,210810000.0
61,2020년 08월 06일,311.32,307.88,312.84,307.88,254200000.0
60,2020년 08월 07일,312.57,311.99,314.11,310.0,299120000.0
59,2020년 08월 10일,316.77,312.75,317.84,312.08,261180000.0
58,2020년 08월 11일,321.02,317.63,322.64,317.56,297600000.0
57,2020년 08월 12일,322.68,320.31,322.68,317.3,293140000.0
56,2020년 08월 13일,323.33,325.84,326.22,319.66,285740000.0
55,2020년 08월 14일,319.24,321.59,322.66,316.28,260399999.99999997
54,2020년 08월 18일,312.84,319.57,321.69,311.2,247520000.0
53,2020년 08월 19일,313.54,315.96,316.82,313.18,159930000.0
52,2020년 08월 20일,301.59,310.45,312.77,301.15,249740000.0
51,2020년 08월 21일,306.16,306.17,309.88,303.15,166300000.0
50,2020년 08월 24일,309.33,306.86,310.27,303.39,142210000.0
49,2020년 08월 25일,313.59,312.45,314.14,310.57,197820000.0
48,2020년 08월 26일,314.19,313.61,315.08,309.74,193540000.0
47,2020년 08월 27일,311.38,314.46,315.17,311.37,175570000.0
46,2020년 08월 28일,312.24,313.86,315.95,311.06,256410000.00000003
45,2020년 08월 31일,307.14,315.46,316.11,307.12,312180000.0
44,2020년 09월 01일,309.81,309.08,311.15,307.63,342810000.0
43,2020년 09월 02일,311.5,311.69,312.92,308.52,337040000.0
42,2020년 09월 03일,316.43,314.73,317.46,314.45,342180000.0
41,2020년 09월 04일,312.03,308.25,312.64,308.04,408030000.0
40,2020년 09월 07일,313.67,312.08,314.44,310.93,264670000.00000003
39,2020년 09월 08일,317.38,316.31,317.43,314.56,227270000.0
38,2020년 09월 09일,313.77,313.54,315.9,312.9,198500000.0
37,2020년 09월 10일,316.53,317.92,318.38,315.46,234270000.0
36,2020년 09월 11일,316.45,315.64,316.77,313.2,165160000.0
35,2020년 09월 14일,320.98,319.83,321.22,318.54,176780000.0
34,2020년 09월 15일,323.36,321.82,323.57,320.54,152450000.0
33,2020년 09월 16일,322.31,323.14,324.19,321.52,146880000.0
32,2020년 09월 17일,318.0,321.3,322.23,316.92,178300000.0
31,2020년 09월 18일,318.39,319.2,319.81,316.62,153540000.0
30,2020년 09월 21일,315.89,318.13,320.86,314.78,165860000.0
29,2020년 09월 22일,308.82,315.53,316.02,307.4,304360000.0
28,2020년 09월 23일,309.64,311.5,311.76,303.29,208050000.0
27,2020년 09월 24일,302.48,304.85,306.98,301.95,181250000.0
26,2020년 09월 25일,303.57,305.38,305.55,301.93,156860000.0
25,2020년 09월 28일,307.03,306.49,308.34,304.64,173180000.0
24,2020년 09월 29일,309.44,310.01,311.34,308.52,138820000.0
23,2020년 10월 05일,313.51,309.65,314.66,309.09,157990000.0
22,2020년 10월 06일,314.45,315.13,316.48,313.03,139890000.0
21,2020년 10월 07일,317.35,312.34,317.44,311.82,124430000.0
20,2020년 10월 08일,317.47,320.31,320.38,316.6,168690000.0
19,2020년 10월 12일,319.15,318.99,320.01,317.67,140940000.0
18,2020년 10월 13일,319.58,321.04,321.45,316.98,126740000.0
17,2020년 10월 14일,316.57,319.6,319.88,315.71,129340000.0
16,2020년 10월 15일,313.92,315.76,315.89,312.4,124410000.0
15,2020년 10월 16일,311.83,314.41,315.47,309.67,126440000.0
14,2020년 10월 19일,312.59,313.43,314.94,312.19,138220000.0
13,2020년 10월 20일,314.71,311.02,314.91,309.95,135680000.0
12,2020년 10월 21일,315.76,315.34,316.61,314.16,136810000.0
11,2020년 10월 22일,313.77,313.62,314.29,311.55,140730000.0
10,2020년 10월 23일,314.79,314.98,315.83,312.31,198020000.0
9,2020년 10월 26일,313.53,315.63,316.78,313.33,170880000.0
8,2020년 10월 27일,311.8,311.5,313.93,310.55,162320000.0
7,2020년 10월 28일,313.17,310.66,313.3,309.44,120210000.0
6,2020년 10월 29일,309.94,308.99,310.56,306.63,139040000.0
5,2020년 10월 30일,301.6,308.91,309.08,301.58,178910000.0
4,2020년 11월 02일,305.95,302.67,306.43,301.37,111030000.0
3,2020년 11월 03일,312.07,308.14,312.35,308.01,129000000.0
2,2020년 11월 04일,313.83,314.31,315.62,311.37,209920.0
1,2020년 11월 05일,321.51,315.74,321.51,315.27,186090000.0
0,2020년 11월 06일,322.1,322.72,324.28,320.61,143960000.0
,close,open,high,low,vol
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4,0.726466795615732,0.7048944337811902,0.7055035128805616,0.7267864271457085,0.2610175714766094
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......
,date,close,open,high,low,vol
0,20190102,258.23,263.35,263.77,257.47,69950000.0
1,20190103,255.77,258.19,258.7,255.35,90520000.0
2,20190104,257.89,255.62,258.12,254.55,91200000.0
3,20190107,261.57,261.11,263.13,260.76,92320000.0
4,20190108,259.65,261.29,262.43,259.39,86910000.0
5,20190109,265.6,260.95,266.05,260.87,98750000.0
6,20190110,265.55,265.73,266.85,264.61,99830000.0
7,20190111,267.13,266.58,267.58,265.56,81080000.0
8,20190114,264.99,266.22,266.7,264.23,74930000.0
9,20190115,269.93,265.62,270.02,265.37,87170000.0
10,20190116,271.23,269.96,271.28,269.05,100500000.0
11,20190117,271.88,271.66,273.05,270.81,86750000.0
12,20190118,273.88,273.02,273.95,272.56,78970000.0
13,20190121,274.0,274.99,275.14,272.29,86550000.0
14,20190122,272.71,274.08,274.6,271.45,68870000.0
15,20190123,273.87,271.35,274.51,271.03,99960000.0
16,20190124,276.61,274.1,276.64,273.74,132860000.00000001
17,20190125,281.67,277.3,281.72,277.04,115510000.0
18,20190128,281.64,282.56,283.46,280.47,95970000.0
19,20190129,283.0,281.12,283.0,279.65,130940000.0
20,20190130,286.62,282.86,286.63,282.24,134730000.0
21,20190131,285.89,288.85,288.87,285.89,153500000.0
22,20190201,285.79,287.07,288.34,285.43,114630000.0
23,20190207,285.65,287.54,287.67,284.97,100690000.0
24,20190208,281.49,283.85,284.01,280.95,88710000.0
25,20190211,282.1,281.75,282.19,280.02,79970000.0
26,20190212,283.69,282.12,284.32,281.43,92110000.0
27,20190213,284.97,285.09,285.59,284.13,93290000.0
28,20190214,288.68,284.25,288.68,282.91,112390000.0
29,20190215,283.98,286.42,286.42,282.81,77190000.0
30,20190218,285.9,285.6,287.02,284.64,73480000.0
31,20190219,284.91,285.36,286.12,283.16,62750000.0
32,20190220,288.4,286.55,289.22,286.21,88580000.0
33,20190221,288.41,287.43,289.28,286.05,88240000.0
34,20190222,288.74,287.29,288.74,286.54,70340000.0
35,20190225,289.0,290.19,290.32,287.84,73420000.0
36,20190226,287.89,288.52,289.2,287.06,81330000.0
37,20190227,288.76,288.9,289.41,288.09,72740000.0
38,20190228,283.8,288.13,288.62,283.8,129090000.0
39,20190304,282.51,286.12,286.68,282.16,107550000.0
40,20190305,280.45,281.42,281.89,279.53,78810000.0
41,20190306,279.85,280.09,280.26,278.72,75230000.0
42,20190307,278.87,279.33,280.98,277.44,78640000.0
43,20190308,274.84,277.18,278.0,274.84,71640000.0
44,20190311,275.06,275.5,276.28,273.88,64830000.0
45,20190312,277.73,277.41,278.48,276.94,63960000.0
46,20190313,276.0,276.92,277.12,274.04,61490000.0
47,20190314,276.64,276.79,277.4,274.78,95820000.0
48,20190315,279.87,276.42,280.5,276.31,123130000.0
49,20190318,279.93,280.27,280.77,278.92,60900000.0
50,20190319,279.82,280.3,280.4,278.63,55390000.0
51,20190320,279.92,278.84,280.07,276.34,61170000.0
52,20190321,282.38,281.14,284.39,280.63,97090000.0
53,20190322,283.13,283.92,284.55,282.09,117730000.0
54,20190325,277.45,279.12,279.21,277.19,77320000.0
55,20190326,277.6,278.43,278.49,276.91,65750000.0
56,20190327,277.28,277.01,278.16,274.87,67770000.0
57,20190328,275.08,275.02,276.2,273.9,66920000.0
58,20190329,276.48,276.33,277.01,274.32,82280000.0
59,20190401,280.32,278.41,280.93,278.41,76670000.0
60,20190402,281.72,281.67,282.47,281.0,72180000.0
61,20190403,285.65,281.88,285.65,281.22,91590000.0
62,20190404,286.15,285.29,287.75,284.97,73800000.0
63,20190405,286.68,286.03,287.53,285.93,64890000.0
64,20190408,286.38,287.78,287.83,285.09,64569999.99999999
65,20190409,286.71,286.51,288.02,285.38,66230000.00000001
66,20190410,287.92,285.44,288.01,285.1,75080000.0
67,20190411,287.42,288.33,288.71,286.38,84680000.0
68,20190412,288.37,286.89,288.62,285.83,69060000.0
69,20190415,289.38,289.54,290.74,289.12,80430000.0
70,20190416,290.62,289.18,290.76,288.77,96130000.0
71,20190417,290.33,290.68,291.21,289.84,72670000.0
72,20190418,285.82,290.88,291.15,285.38,89650000.0
73,20190419,285.89,287.18,287.48,285.72,63870000.0
74,20190422,285.87,286.06,287.59,284.52,55190000.0
75,20190423,286.47,285.85,286.83,285.42,67380000.0
76,20190424,283.7,287.82,287.82,281.96,88610000.0
77,20190425,282.83,282.8,284.22,281.9,74580000.0
78,20190426,281.34,280.86,282.47,279.7,76720000.0
79,20190429,286.79,282.0,286.79,281.81,66980000.00000001
80,20190430,284.92,286.25,286.59,282.61,77990000.0
81,20190502,286.12,283.37,286.87,283.14,84180000.0
82,20190503,283.67,285.64,285.95,283.14,67790000.0
83,20190507,281.31,280.89,281.66,278.95,97720000.0
84,20190508,280.15,277.85,281.84,277.84,75000000.0
85,20190509,271.0,278.83,279.63,271.0,124010000.0
86,20190510,272.06,273.37,274.32,269.71,89440000.0
87,20190513,268.72,269.8,271.54,268.55,66870000.00000001
88,20190514,268.57,266.03,270.42,265.55,82190000.0
89,20190515,269.64,268.42,271.0,267.62,77020000.0
90,20190516,265.99,269.83,269.96,265.24,75960000.0
91,20190517,264.35,267.85,268.06,263.94,75100000.0
92,20190520,264.96,265.9,267.65,264.33,64230000.00000001
93,20190521,266.12,265.25,268.55,264.79,81020000.0
94,20190522,266.65,267.01,267.6,263.95,61660000.0
95,20190523,266.38,266.4,267.12,264.5,71660000.0
96,20190524,264.42,264.7,266.11,263.1,67230000.0
97,20190527,264.4,264.18,265.85,263.17,53140000.0
98,20190528,264.7,264.38,266.16,263.27,147260000.0
99,20190529,261.37,264.1,264.1,260.22,82790000.0
100,20190530,263.93,261.58,264.23,261.58,70340000.0
101,20190531,263.89,262.48,265.21,261.53,77790000.0
102,20190603,268.16,262.68,268.55,262.33,83610000.0
103,20190604,267.66,267.84,268.47,266.72,70290000.0
104,20190605,267.99,270.1,270.46,267.46,67510000.0
105,20190607,268.27,267.97,269.63,266.27,67970000.0
106,20190610,272.1,270.32,272.2,268.68,60890000.0
107,20190611,273.21,271.48,273.69,271.24,61240000.0
108,20190612,272.74,273.14,274.32,272.0,66920000.0
109,20190613,271.13,271.96,272.46,268.38,95800000.0
110,20190614,270.48,270.19,271.55,268.96,66709999.99999999
111,20190617,270.09,269.83,271.59,269.14,57060000.0
112,20190618,271.36,269.77,271.87,269.77,65720000.0
113,20190619,275.31,274.4,275.6,274.08,69730000.0
114,20190620,276.01,274.63,276.09,274.43,55340000.0
115,20190621,275.48,276.17,276.26,274.6,77280000.0
116,20190624,275.58,275.91,276.73,275.13,55540000.0
117,20190625,275.28,275.33,277.02,274.19,60510000.0
118,20190626,275.53,275.21,276.36,274.66,58820000.0
119,20190627,277.75,275.47,278.63,275.09,66300000.0
120,20190628,277.5,278.03,278.56,276.58,67600000.0
121,20190701,277.27,279.59,279.68,276.85,74360000.0
122,20190702,275.97,276.06,277.46,274.63,65540000.00000001
123,20190703,272.37,275.57,276.69,271.9,97520000.0
124,20190704,274.54,273.18,274.61,271.93,77520000.0
125,20190705,274.35,274.74,275.23,272.41,60020000.0
126,20190708,268.55,271.38,271.67,268.38,75100000.0
127,20190709,267.72,269.6,270.22,267.72,71880000.0
128,20190710,268.75,269.29,270.33,268.6,67510000.0
129,20190711,271.78,270.58,272.97,270.46,107370000.0
130,20190712,272.68,272.21,273.23,271.28,55660000.0
131,20190715,272.64,272.06,273.38,270.99,83610000.0
132,20190716,274.05,272.79,274.54,272.14,137050000.0
133,20190717,271.48,272.13,272.28,270.29,117950000.0
134,20190718,270.67,271.05,271.63,269.73,80140000.0
135,20190719,274.55,272.71,274.95,272.21,87940000.0
136,20190722,274.55,273.9,275.35,273.57,60910000.0
137,20190723,275.92,274.93,276.56,274.84,95080000.0
138,20190724,273.22,275.12,276.48,272.91,84290000.0
139,20190725,273.04,274.08,274.62,271.25,95650000.0
140,20190726,272.25,271.41,272.56,270.57,89840000.0
141,20190729,267.75,271.21,271.91,267.07,78750000.0
142,20190730,268.95,268.69,269.79,268.33,64530000.0
143,20190731,266.34,268.46,269.14,265.0,96240000.0
144,20190801,265.73,264.97,267.44,264.47,72580000.0
145,20190802,262.99,262.61,263.94,261.75,99660000.0
146,20190805,257.19,261.1,261.4,256.76,131039999.99999999
147,20190806,253.41,250.85,257.04,250.24,123170000.0
148,20190807,252.0,254.46,254.9,251.08,90740000.0
149,20190808,252.4,254.11,255.33,252.37,163710000.0
150,20190809,254.85,255.13,256.15,254.41,117690000.0
151,20190812,255.35,255.57,256.43,254.43,69380000.0
152,20190813,252.83,253.5,254.58,252.63,76820000.0
153,20190814,254.86,255.84,256.82,254.59,82240000.0
154,20190816,253.9,252.76,253.94,251.46,71830000.0
155,20190819,255.11,255.2,255.84,254.25,66019999.99999999
156,20190820,258.05,255.53,258.62,254.74,81690000.0
157,20190821,258.38,257.15,259.49,257.11,65480000.00000001
158,20190822,256.28,258.53,258.73,256.25,67250000.0
159,20190823,256.11,255.11,256.88,254.91,54780000.0
160,20190826,252.19,251.8,253.14,250.99,62610000.0
161,20190827,253.5,253.74,254.96,252.53,93380000.0
162,20190828,255.53,254.2,255.98,253.65,58340000.0
163,20190829,254.31,255.68,256.28,253.6,64849999.99999999
164,20190830,259.0,256.85,259.61,256.72,94160000.0
165,20190902,259.08,258.25,259.74,257.8,54890000.0
166,20190903,258.25,258.55,259.67,257.4,64319999.99999999
167,20190904,261.69,257.89,261.69,257.76,69290000.0
168,20190905,264.44,262.53,266.0,262.27,88990000.0
169,20190906,265.53,266.2,266.36,264.74,59200000.0
170,20190909,267.4,266.11,268.67,265.92,62210000.0
171,20190910,268.76,267.96,269.13,267.35,66340000.0
172,20190911,270.84,270.35,271.35,269.58,103900000.0
173,20190916,272.02,271.48,272.03,269.59,103340000.0
174,20190917,271.68,271.46,272.26,270.9,73740000.0
175,20190918,273.06,271.88,273.72,271.04,67600000.0
176,20190919,274.92,273.11,276.15,273.03,70500000.0
177,20190920,276.44,275.01,276.67,274.46,74030000.0
178,20190923,276.65,275.86,277.11,275.64,60190000.0
179,20190924,277.97,276.41,278.17,275.55,68270000.0
180,20190925,274.42,276.67,277.04,274.12,70840000.0
181,20190926,274.89,275.61,276.38,273.5,89590000.0
182,20190927,271.33,273.32,273.4,270.52,57740000.0
183,20190930,273.55,270.61,273.85,270.06,54690000.0
184,20191001,274.42,273.43,275.38,272.81,52630000.0
185,20191002,268.55,272.39,272.42,268.55,66209999.99999999
186,20191004,267.39,268.14,270.16,267.39,62880000.0
187,20191007,267.67,269.12,269.75,266.83,53570000.0
188,20191008,271.36,268.9,271.51,267.97,75080000.0
189,20191010,268.8,268.79,270.78,266.23,98180000.0
190,20191011,271.3,271.68,272.68,270.58,59640000.0
191,20191014,274.23,274.71,275.75,273.97,71050000.0
192,20191015,274.34,273.85,275.09,273.83,53050000.0
193,20191016,276.32,276.26,277.59,275.24,65379999.99999999
194,20191017,275.57,275.92,276.68,274.62,53870000.0
195,20191018,272.81,275.99,277.06,272.28,71470000.0
196,20191021,273.79,273.87,274.1,272.67,45390000.0
197,20191022,277.41,275.4,278.19,274.93,63880000.0
198,20191023,276.23,276.83,277.29,275.34,65290000.00000001
199,20191024,276.7,277.7,277.84,274.31,73680000.0
200,20191025,277.07,277.6,277.67,275.7,58280000.0
201,20191028,277.78,278.16,279.42,277.29,59070000.0
202,20191029,277.55,279.25,279.49,276.81,57150000.0
203,20191030,275.51,277.49,277.49,274.95,72530000.0
204,20191031,275.82,276.84,279.04,275.82,78340000.0
205,20191101,278.43,276.17,278.55,275.59,58540000.0
206,20191104,282.54,279.9,282.77,279.9,77000000.0
207,20191105,284.38,282.99,284.46,282.27,76360000.0
208,20191106,284.52,284.31,286.43,283.7,77950000.0
209,20191107,284.37,284.04,284.82,283.05,70720000.0
210,20191108,283.13,285.81,286.12,282.57,73680000.0
211,20191111,280.85,283.3,283.3,280.4,61560000.0
212,20191112,283.65,281.9,283.65,280.79,65569999.99999999
213,20191113,281.28,282.28,282.49,280.62,60130000.0
214,20191114,283.93,281.9,283.93,281.1,79760000.0
215,20191115,287.29,283.85,287.72,283.3,71650000.0
216,20191118,287.18,287.1,287.78,285.94,56110000.0
217,20191119,286.23,285.74,286.34,283.73,68170000.0
218,20191120,282.21,284.27,285.29,282.0,73550000.0
219,20191121,278.28,281.89,282.33,276.98,81750000.0
220,20191122,279.43,278.57,279.89,278.14,74350000.0
221,20191125,282.19,281.77,283.53,281.48,64510000.00000001
222,20191126,281.83,283.09,284.87,281.77,119520000.0
223,20191127,282.85,283.56,284.02,281.27,60900000.0
224,20191128,281.23,281.23,282.8,281.05,61620000.0
225,20191129,276.78,282.08,282.1,276.34,74600000.0
226,20191202,277.44,278.27,279.44,276.97,66610000.0
227,20191203,276.19,274.93,276.63,273.88,67280000.0
228,20191204,274.15,274.36,274.68,272.53,81920000.0
229,20191205,273.3,275.65,276.63,272.53,66510000.00000001
230,20191206,276.56,275.19,277.0,274.75,70110000.0
231,20191209,278.03,278.31,278.75,277.02,70520000.0
232,20191210,279.31,276.8,279.75,276.62,56670000.0
233,20191211,280.28,279.19,281.03,278.45,66849999.99999999
234,20191212,285.11,282.62,285.43,282.62,133650000.0
235,20191213,290.11,289.34,290.11,288.32,99620000.0
236,20191216,289.76,289.54,290.8,289.36,73740000.0
237,20191217,294.4,291.76,294.5,290.52,94370000.0
238,20191218,294.22,294.31,295.85,293.05,76910000.0
239,20191219,294.31,295.06,295.84,292.82,70190000.0
240,20191220,295.31,295.58,295.86,293.95,75970000.0
241,20191223,294.87,295.84,296.12,293.81,62090000.0
242,20191224,292.9,295.18,295.25,292.47,60610000.0
243,20191226,294.06,293.12,294.07,291.79,68480000.0
244,20191227,295.2,292.37,296.95,291.28,77510000.0
245,20191230,293.77,294.6,295.49,293.4,54970000.0
,date,close,open,high,low,vol
0,20200102,290.35,294.19,294.57,289.96,66160000.0
1,20200103,290.74,293.1,294.88,289.04,79770000.0
2,20200106,288.43,287.73,289.41,287.49,69960000.0
3,20200107,291.23,290.01,292.26,289.64,65349999.99999999
4,20200108,289.42,288.94,291.01,287.3,124530000.0
5,20200109,294.41,293.83,294.41,292.1,111790000.0
6,20200110,297.06,294.8,297.17,294.17,94160000.0
7,20200113,300.13,296.62,300.31,296.53,81680000.0
8,20200114,301.53,302.2,303.61,300.69,92120000.0
9,20200115,299.74,299.91,301.13,298.82,71770000.0
10,20200116,302.78,299.93,302.91,298.95,85710000.0
11,20200117,303.3,305.07,306.05,302.51,81540000.0
12,20200120,305.58,304.99,307.54,304.77,78040000.0
13,20200121,302.11,304.94,306.09,301.85,67630000.0
14,20200122,306.08,301.79,306.52,301.16,79330000.0
15,20200123,302.33,303.77,304.72,301.71,86910000.0
16,20200128,292.77,294.98,296.3,291.3,130169999.99999999
17,20200129,293.98,294.38,295.67,292.45,85730000.0
18,20200130,288.37,293.27,294.11,287.09,101540000.0
19,20200131,284.53,290.24,291.47,284.53,101460000.0
20,20200203,285.05,280.17,286.24,279.78,111060000.0
21,20200204,290.68,285.19,291.38,285.11,105450000.0
22,20200205,292.02,293.57,294.26,290.3,102640000.0
23,20200206,300.65,294.74,300.98,294.32,110220000.0
24,20200207,298.21,299.59,300.05,296.01,108110000.0
25,20200210,296.24,294.07,296.73,293.33,72220000.0
26,20200211,299.28,297.91,301.1,297.48,78350000.0
27,20200212,301.54,299.83,302.2,298.09,110630000.0
28,20200213,300.93,302.79,304.43,300.48,115070000.0
29,20200214,303.01,300.95,304.2,299.18,80530000.0
30,20200217,302.76,302.84,304.05,300.99,84280000.0
31,20200218,297.74,300.26,301.29,297.2,84750000.0
32,20200219,298.33,299.68,300.38,295.53,84260000.0
33,20200220,296.65,300.34,301.35,295.01,90010000.0
34,20200221,292.42,292.16,295.24,291.96,100710000.0
35,20200224,281.02,285.51,286.62,281.02,132820000.0
36,20200225,284.24,280.48,284.63,279.77,117030000.0
37,20200226,279.94,278.45,281.98,277.68,119550000.0
38,20200227,277.09,279.46,281.09,276.07,110500000.0
39,20200228,268.02,272.33,273.98,267.23,202610000.0
40,20200302,270.37,269.59,272.6,265.7,163720000.0
41,20200303,271.56,277.44,277.54,270.79,146720000.0
42,20200304,278.13,270.13,278.9,270.13,208460000.0
43,20200305,281.38,280.66,282.05,277.91,147440000.0
44,20200306,275.1,276.74,278.31,274.0,140900000.0
45,20200309,263.11,266.39,267.75,261.76,182670000.0
46,20200310,264.67,261.94,265.57,260.75,173090000.0
47,20200311,257.01,264.63,265.04,255.69,194210000.0
48,20200312,247.62,253.93,255.49,243.76,242470000.0
49,20200313,240.65,231.71,246.19,227.36,298900000.0
50,20200316,232.97,245.21,245.21,232.76,175050000.0
51,20200317,226.89,222.94,234.56,222.46,216730000.0
52,20200318,215.83,228.63,230.04,215.81,208040000.0
53,20200319,199.28,220.72,220.85,196.27,303560000.0
54,20200320,213.67,204.84,214.08,200.54,269350000.0
55,20200323,201.87,200.8,206.66,198.67,210300000.0
56,20200324,220.34,207.65,220.34,205.41,225730000.0
57,20200325,232.89,228.37,233.42,224.74,306110000.0
58,20200326,229.34,231.91,236.61,229.22,290840000.0
59,20200327,233.79,239.11,239.48,227.01,249100000.0
60,20200330,232.45,227.05,234.7,225.09,167230000.0
61,20200331,236.82,235.2,237.47,232.5,240440000.0
62,20200401,226.35,234.23,237.52,226.34,228580000.0
63,20200402,231.84,227.47,232.22,223.6,181790000.0
64,20200403,231.7,232.48,234.45,229.23,181040000.0
65,20200406,240.81,234.4,241.19,233.75,190740000.0
66,20200407,244.87,245.65,247.39,240.68,224450000.0
67,20200408,241.89,243.71,246.99,241.77,173120000.0
68,20200409,245.61,246.13,246.46,243.11,175940000.0
69,20200410,248.0,245.14,248.21,244.02,286110000.0
70,20200413,243.4,246.59,246.9,243.39,195900000.0
71,20200414,247.45,246.2,248.55,244.58,193120000.0
72,20200416,247.1,244.99,247.87,243.61,197540000.0
73,20200417,255.02,252.52,257.37,252.38,287360000.0
74,20200420,252.14,253.85,255.93,251.46,211000000.0
75,20200421,249.4,250.26,251.88,245.11,257160000.00000003
76,20200422,251.88,246.16,252.18,244.56,184500000.0
77,20200423,253.74,253.42,255.22,251.58,163810000.0
78,20200424,250.28,252.47,252.86,249.19,154310000.0
79,20200427,254.84,251.18,255.79,250.61,151360000.0
80,20200428,256.39,255.91,257.34,253.22,172420000.0
81,20200429,258.15,256.58,259.67,256.04,184480000.0
82,20200504,250.6,252.44,253.7,250.34,174040000.0
83,20200506,255.0,253.76,255.01,251.26,133280000.0
84,20200507,254.46,253.88,256.14,253.37,114360000.0
85,20200508,256.62,256.66,258.29,256.23,125220000.0
86,20200511,254.95,257.67,258.87,254.59,144300000.0
87,20200512,253.37,255.33,255.56,250.98,166800000.0
88,20200513,255.85,250.03,255.85,249.95,141250000.0
89,20200514,253.65,253.26,254.39,252.09,167640000.0
90,20200515,253.79,255.34,255.49,252.02,228900000.0
91,20200518,255.44,254.55,256.31,252.95,223000000.0
92,20200519,261.79,261.12,262.34,259.96,253330000.0
93,20200520,262.72,260.48,263.25,260.48,140300000.0
94,20200521,263.74,264.52,264.85,263.07,159480000.0
95,20200522,259.62,263.82,264.07,258.34,166030000.0
96,20200525,262.76,260.95,262.82,259.16,125430000.0
97,20200526,267.31,263.48,267.31,262.95,175420000.0
98,20200527,267.64,266.71,269.17,265.86,247180000.0
99,20200528,268.29,270.17,271.44,264.85,248930000.0
100,20200529,268.32,266.62,269.61,265.23,269470000.0
101,20200601,273.19,269.29,273.39,268.93,171510000.0
102,20200602,276.08,272.65,276.47,272.22,251030000.0
103,20200603,285.91,279.14,287.49,278.98,476500000.0
104,20200604,286.45,291.17,292.9,284.93,364580000.0
105,20200605,290.62,286.3,291.5,284.5,282060000.0
106,20200608,290.77,295.41,295.68,289.71,286660000.0
107,20200609,291.32,293.96,294.87,287.83,268570000.0
108,20200610,291.9,291.03,292.92,289.6,229070000.0
109,20200611,288.62,289.98,292.08,284.29,275900000.0
110,20200612,281.78,276.97,282.54,276.05,232830000.0
111,20200615,267.95,278.61,280.87,267.95,262440000.0
112,20200616,282.59,275.89,282.59,274.0,247890000.0
113,20200617,283.02,282.08,284.9,278.33,337220000.0
114,20200618,281.91,281.74,283.39,280.02,181590000.0
115,20200619,283.37,284.56,284.7,278.53,189130000.0
116,20200622,281.42,281.01,283.71,280.34,139840000.0
117,20200623,281.94,284.64,286.04,279.26,163680000.0
118,20200624,286.7,283.71,288.3,283.3,200570000.0
119,20200625,279.73,282.38,284.36,279.73,152980000.0
120,20200626,283.38,283.36,284.58,280.65,169010000.0
121,20200629,278.04,279.36,281.77,277.06,137760000.0
122,20200630,280.09,282.55,283.98,280.09,179500000.0
123,20200701,280.26,283.1,283.84,279.44,133800000.00000001
124,20200702,283.86,281.3,283.89,280.71,111370000.0
125,20200703,285.89,285.59,286.09,283.36,100220000.0
126,20200706,290.62,286.84,291.14,286.31,128120000.0
127,20200707,286.77,292.74,292.98,286.76,156130000.0
128,20200708,285.97,287.2,288.41,285.18,119450000.0
129,20200709,287.25,287.8,289.41,287.08,140610000.0
130,20200710,285.06,287.87,288.09,283.51,136770000.0
131,20200713,289.84,287.92,290.49,286.86,127050000.0
132,20200714,289.63,288.42,289.63,286.89,126300000.0
133,20200715,292.27,292.94,294.42,290.89,168350000.0
134,20200716,289.25,292.34,292.74,288.46,129449999.99999999
135,20200717,291.57,289.9,292.1,289.9,118640000.0
136,20200720,290.81,292.76,292.76,289.01,161820000.0
137,20200721,295.16,294.03,296.11,293.2,366990000.0
138,20200722,294.04,294.71,296.14,293.39,327320000.0
139,20200723,292.37,293.62,293.62,290.49,250980000.0
140,20200724,290.66,289.84,292.74,289.42,216210000.0
141,20200727,293.51,291.03,295.26,290.82,193240000.0
142,20200728,300.13,296.53,301.34,296.38,301280000.0
143,20200729,301.25,300.57,303.51,299.61,264440000.0
144,20200730,301.85,303.13,304.13,301.49,191760000.0
145,20200731,299.32,303.69,304.16,298.89,201960000.0
146,20200803,299.46,299.57,300.33,297.57,179730000.0
147,20200804,303.04,302.5,304.27,301.48,219870000.0
148,20200805,306.64,303.94,306.67,302.91,210810000.0
149,20200806,311.32,307.88,312.84,307.88,254200000.0
150,20200807,312.57,311.99,314.11,310.0,299120000.0
151,20200810,316.77,312.75,317.84,312.08,261180000.0
152,20200811,321.02,317.63,322.64,317.56,297600000.0
153,20200812,322.68,320.31,322.68,317.3,293140000.0
154,20200813,323.33,325.84,326.22,319.66,285740000.0
155,20200814,319.24,321.59,322.66,316.28,260399999.99999997
156,20200818,312.84,319.57,321.69,311.2,247520000.0
157,20200819,313.54,315.96,316.82,313.18,159930000.0
158,20200820,301.59,310.45,312.77,301.15,249740000.0
159,20200821,306.16,306.17,309.88,303.15,166300000.0
160,20200824,309.33,306.86,310.27,303.39,142210000.0
161,20200825,313.59,312.45,314.14,310.57,197820000.0
162,20200826,314.19,313.61,315.08,309.74,193540000.0
163,20200827,311.38,314.46,315.17,311.37,175570000.0
164,20200828,312.24,313.86,315.95,311.06,256410000.00000003
165,20200831,307.14,315.46,316.11,307.12,312180000.0
166,20200901,309.81,309.08,311.15,307.63,342810000.0
167,20200902,311.5,311.69,312.92,308.52,337040000.0
168,20200903,316.43,314.73,317.46,314.45,342180000.0
169,20200904,312.03,308.25,312.64,308.04,408030000.0
170,20200907,313.67,312.08,314.44,310.93,264670000.00000003
171,20200908,317.38,316.31,317.43,314.56,227270000.0
172,20200909,313.77,313.54,315.9,312.9,198500000.0
173,20200910,316.53,317.92,318.38,315.46,234270000.0
174,20200911,316.45,315.64,316.77,313.2,165160000.0
175,20200914,320.98,319.83,321.22,318.54,176780000.0
176,20200915,323.36,321.82,323.57,320.54,152450000.0
177,20200916,322.31,323.14,324.19,321.52,146880000.0
178,20200917,318.0,321.3,322.23,316.92,178300000.0
179,20200918,318.39,319.2,319.81,316.62,153540000.0
180,20200921,315.89,318.13,320.86,314.78,165860000.0
181,20200922,308.82,315.53,316.02,307.4,304360000.0
182,20200923,309.64,311.5,311.76,303.29,208050000.0
183,20200924,302.48,304.85,306.98,301.95,181250000.0
184,20200925,303.57,305.38,305.55,301.93,156860000.0
185,20200928,307.03,306.49,308.34,304.64,173180000.0
186,20200929,309.44,310.01,311.34,308.52,138820000.0
0,2019년 01월 02일,258.23,263.35,263.77,257.47,69950000.0
1,2019년 01월 03일,255.77,258.19,258.7,255.35,90520000.0
2,2019년 01월 04일,257.89,255.62,258.12,254.55,91200000.0
3,2019년 01월 07일,261.57,261.11,263.13,260.76,92320000.0
4,2019년 01월 08일,259.65,261.29,262.43,259.39,86910000.0
5,2019년 01월 09일,265.6,260.95,266.05,260.87,98750000.0
6,2019년 01월 10일,265.55,265.73,266.85,264.61,99830000.0
7,2019년 01월 11일,267.13,266.58,267.58,265.56,81080000.0
8,2019년 01월 14일,264.99,266.22,266.7,264.23,74930000.0
9,2019년 01월 15일,269.93,265.62,270.02,265.37,87170000.0
10,2019년 01월 16일,271.23,269.96,271.28,269.05,100500000.0
11,2019년 01월 17일,271.88,271.66,273.05,270.81,86750000.0
12,2019년 01월 18일,273.88,273.02,273.95,272.56,78970000.0
13,2019년 01월 21일,274.0,274.99,275.14,272.29,86550000.0
14,2019년 01월 22일,272.71,274.08,274.6,271.45,68870000.0
15,2019년 01월 23일,273.87,271.35,274.51,271.03,99960000.0
16,2019년 01월 24일,276.61,274.1,276.64,273.74,132860000.00000001
17,2019년 01월 25일,281.67,277.3,281.72,277.04,115510000.0
18,2019년 01월 28일,281.64,282.56,283.46,280.47,95970000.0
19,2019년 01월 29일,283.0,281.12,283.0,279.65,130940000.0
20,2019년 01월 30일,286.62,282.86,286.63,282.24,134730000.0
21,2019년 01월 31일,285.89,288.85,288.87,285.89,153500000.0
22,2019년 02월 01일,285.79,287.07,288.34,285.43,114630000.0
23,2019년 02월 07일,285.65,287.54,287.67,284.97,100690000.0
24,2019년 02월 08일,281.49,283.85,284.01,280.95,88710000.0
25,2019년 02월 11일,282.1,281.75,282.19,280.02,79970000.0
26,2019년 02월 12일,283.69,282.12,284.32,281.43,92110000.0
27,2019년 02월 13일,284.97,285.09,285.59,284.13,93290000.0
28,2019년 02월 14일,288.68,284.25,288.68,282.91,112390000.0
29,2019년 02월 15일,283.98,286.42,286.42,282.81,77190000.0
30,2019년 02월 18일,285.9,285.6,287.02,284.64,73480000.0
31,2019년 02월 19일,284.91,285.36,286.12,283.16,62750000.0
32,2019년 02월 20일,288.4,286.55,289.22,286.21,88580000.0
33,2019년 02월 21일,288.41,287.43,289.28,286.05,88240000.0
34,2019년 02월 22일,288.74,287.29,288.74,286.54,70340000.0
35,2019년 02월 25일,289.0,290.19,290.32,287.84,73420000.0
36,2019년 02월 26일,287.89,288.52,289.2,287.06,81330000.0
37,2019년 02월 27일,288.76,288.9,289.41,288.09,72740000.0
38,2019년 02월 28일,283.8,288.13,288.62,283.8,129090000.0
39,2019년 03월 04일,282.51,286.12,286.68,282.16,107550000.0
40,2019년 03월 05일,280.45,281.42,281.89,279.53,78810000.0
41,2019년 03월 06일,279.85,280.09,280.26,278.72,75230000.0
42,2019년 03월 07일,278.87,279.33,280.98,277.44,78640000.0
43,2019년 03월 08일,274.84,277.18,278.0,274.84,71640000.0
44,2019년 03월 11일,275.06,275.5,276.28,273.88,64830000.0
45,2019년 03월 12일,277.73,277.41,278.48,276.94,63960000.0
46,2019년 03월 13일,276.0,276.92,277.12,274.04,61490000.0
47,2019년 03월 14일,276.64,276.79,277.4,274.78,95820000.0
48,2019년 03월 15일,279.87,276.42,280.5,276.31,123130000.0
49,2019년 03월 18일,279.93,280.27,280.77,278.92,60900000.0
50,2019년 03월 19일,279.82,280.3,280.4,278.63,55390000.0
51,2019년 03월 20일,279.92,278.84,280.07,276.34,61170000.0
52,2019년 03월 21일,282.38,281.14,284.39,280.63,97090000.0
53,2019년 03월 22일,283.13,283.92,284.55,282.09,117730000.0
54,2019년 03월 25일,277.45,279.12,279.21,277.19,77320000.0
55,2019년 03월 26일,277.6,278.43,278.49,276.91,65750000.0
56,2019년 03월 27일,277.28,277.01,278.16,274.87,67770000.0
57,2019년 03월 28일,275.08,275.02,276.2,273.9,66920000.0
58,2019년 03월 29일,276.48,276.33,277.01,274.32,82280000.0
59,2019년 04월 01일,280.32,278.41,280.93,278.41,76670000.0
60,2019년 04월 02일,281.72,281.67,282.47,281.0,72180000.0
61,2019년 04월 03일,285.65,281.88,285.65,281.22,91590000.0
62,2019년 04월 04일,286.15,285.29,287.75,284.97,73800000.0
63,2019년 04월 05일,286.68,286.03,287.53,285.93,64890000.0
64,2019년 04월 08일,286.38,287.78,287.83,285.09,64569999.99999999
65,2019년 04월 09일,286.71,286.51,288.02,285.38,66230000.00000001
66,2019년 04월 10일,287.92,285.44,288.01,285.1,75080000.0
67,2019년 04월 11일,287.42,288.33,288.71,286.38,84680000.0
68,2019년 04월 12일,288.37,286.89,288.62,285.83,69060000.0
69,2019년 04월 15일,289.38,289.54,290.74,289.12,80430000.0
70,2019년 04월 16일,290.62,289.18,290.76,288.77,96130000.0
71,2019년 04월 17일,290.33,290.68,291.21,289.84,72670000.0
72,2019년 04월 18일,285.82,290.88,291.15,285.38,89650000.0
73,2019년 04월 19일,285.89,287.18,287.48,285.72,63870000.0
74,2019년 04월 22일,285.87,286.06,287.59,284.52,55190000.0
75,2019년 04월 23일,286.47,285.85,286.83,285.42,67380000.0
76,2019년 04월 24일,283.7,287.82,287.82,281.96,88610000.0
77,2019년 04월 25일,282.83,282.8,284.22,281.9,74580000.0
78,2019년 04월 26일,281.34,280.86,282.47,279.7,76720000.0
79,2019년 04월 29일,286.79,282.0,286.79,281.81,66980000.00000001
80,2019년 04월 30일,284.92,286.25,286.59,282.61,77990000.0
81,2019년 05월 02일,286.12,283.37,286.87,283.14,84180000.0
82,2019년 05월 03일,283.67,285.64,285.95,283.14,67790000.0
83,2019년 05월 07일,281.31,280.89,281.66,278.95,97720000.0
84,2019년 05월 08일,280.15,277.85,281.84,277.84,75000000.0
85,2019년 05월 09일,271.0,278.83,279.63,271.0,124010000.0
86,2019년 05월 10일,272.06,273.37,274.32,269.71,89440000.0
87,2019년 05월 13일,268.72,269.8,271.54,268.55,66870000.00000001
88,2019년 05월 14일,268.57,266.03,270.42,265.55,82190000.0
89,2019년 05월 15일,269.64,268.42,271.0,267.62,77020000.0
90,2019년 05월 16일,265.99,269.83,269.96,265.24,75960000.0
91,2019년 05월 17일,264.35,267.85,268.06,263.94,75100000.0
92,2019년 05월 20일,264.96,265.9,267.65,264.33,64230000.00000001
93,2019년 05월 21일,266.12,265.25,268.55,264.79,81020000.0
94,2019년 05월 22일,266.65,267.01,267.6,263.95,61660000.0
95,2019년 05월 23일,266.38,266.4,267.12,264.5,71660000.0
96,2019년 05월 24일,264.42,264.7,266.11,263.1,67230000.0
97,2019년 05월 27일,264.4,264.18,265.85,263.17,53140000.0
98,2019년 05월 28일,264.7,264.38,266.16,263.27,147260000.0
99,2019년 05월 29일,261.37,264.1,264.1,260.22,82790000.0
100,2019년 05월 30일,263.93,261.58,264.23,261.58,70340000.0
101,2019년 05월 31일,263.89,262.48,265.21,261.53,77790000.0
102,2019년 06월 03일,268.16,262.68,268.55,262.33,83610000.0
103,2019년 06월 04일,267.66,267.84,268.47,266.72,70290000.0
104,2019년 06월 05일,267.99,270.1,270.46,267.46,67510000.0
105,2019년 06월 07일,268.27,267.97,269.63,266.27,67970000.0
106,2019년 06월 10일,272.1,270.32,272.2,268.68,60890000.0
107,2019년 06월 11일,273.21,271.48,273.69,271.24,61240000.0
108,2019년 06월 12일,272.74,273.14,274.32,272.0,66920000.0
109,2019년 06월 13일,271.13,271.96,272.46,268.38,95800000.0
110,2019년 06월 14일,270.48,270.19,271.55,268.96,66709999.99999999
111,2019년 06월 17일,270.09,269.83,271.59,269.14,57060000.0
112,2019년 06월 18일,271.36,269.77,271.87,269.77,65720000.0
113,2019년 06월 19일,275.31,274.4,275.6,274.08,69730000.0
114,2019년 06월 20일,276.01,274.63,276.09,274.43,55340000.0
115,2019년 06월 21일,275.48,276.17,276.26,274.6,77280000.0
116,2019년 06월 24일,275.58,275.91,276.73,275.13,55540000.0
117,2019년 06월 25일,275.28,275.33,277.02,274.19,60510000.0
118,2019년 06월 26일,275.53,275.21,276.36,274.66,58820000.0
119,2019년 06월 27일,277.75,275.47,278.63,275.09,66300000.0
120,2019년 06월 28일,277.5,278.03,278.56,276.58,67600000.0
121,2019년 07월 01일,277.27,279.59,279.68,276.85,74360000.0
122,2019년 07월 02일,275.97,276.06,277.46,274.63,65540000.00000001
123,2019년 07월 03일,272.37,275.57,276.69,271.9,97520000.0
124,2019년 07월 04일,274.54,273.18,274.61,271.93,77520000.0
125,2019년 07월 05일,274.35,274.74,275.23,272.41,60020000.0
126,2019년 07월 08일,268.55,271.38,271.67,268.38,75100000.0
127,2019년 07월 09일,267.72,269.6,270.22,267.72,71880000.0
128,2019년 07월 10일,268.75,269.29,270.33,268.6,67510000.0
129,2019년 07월 11일,271.78,270.58,272.97,270.46,107370000.0
130,2019년 07월 12일,272.68,272.21,273.23,271.28,55660000.0
131,2019년 07월 15일,272.64,272.06,273.38,270.99,83610000.0
132,2019년 07월 16일,274.05,272.79,274.54,272.14,137050000.0
133,2019년 07월 17일,271.48,272.13,272.28,270.29,117950000.0
134,2019년 07월 18일,270.67,271.05,271.63,269.73,80140000.0
135,2019년 07월 19일,274.55,272.71,274.95,272.21,87940000.0
136,2019년 07월 22일,274.55,273.9,275.35,273.57,60910000.0
137,2019년 07월 23일,275.92,274.93,276.56,274.84,95080000.0
138,2019년 07월 24일,273.22,275.12,276.48,272.91,84290000.0
139,2019년 07월 25일,273.04,274.08,274.62,271.25,95650000.0
140,2019년 07월 26일,272.25,271.41,272.56,270.57,89840000.0
141,2019년 07월 29일,267.75,271.21,271.91,267.07,78750000.0
142,2019년 07월 30일,268.95,268.69,269.79,268.33,64530000.0
143,2019년 07월 31일,266.34,268.46,269.14,265.0,96240000.0
144,2019년 08월 01일,265.73,264.97,267.44,264.47,72580000.0
145,2019년 08월 02일,262.99,262.61,263.94,261.75,99660000.0
146,2019년 08월 05일,257.19,261.1,261.4,256.76,131039999.99999999
147,2019년 08월 06일,253.41,250.85,257.04,250.24,123170000.0
148,2019년 08월 07일,252.0,254.46,254.9,251.08,90740000.0
149,2019년 08월 08일,252.4,254.11,255.33,252.37,163710000.0
150,2019년 08월 09일,254.85,255.13,256.15,254.41,117690000.0
151,2019년 08월 12일,255.35,255.57,256.43,254.43,69380000.0
152,2019년 08월 13일,252.83,253.5,254.58,252.63,76820000.0
153,2019년 08월 14일,254.86,255.84,256.82,254.59,82240000.0
154,2019년 08월 16일,253.9,252.76,253.94,251.46,71830000.0
155,2019년 08월 19일,255.11,255.2,255.84,254.25,66019999.99999999
156,2019년 08월 20일,258.05,255.53,258.62,254.74,81690000.0
157,2019년 08월 21일,258.38,257.15,259.49,257.11,65480000.00000001
158,2019년 08월 22일,256.28,258.53,258.73,256.25,67250000.0
159,2019년 08월 23일,256.11,255.11,256.88,254.91,54780000.0
160,2019년 08월 26일,252.19,251.8,253.14,250.99,62610000.0
161,2019년 08월 27일,253.5,253.74,254.96,252.53,93380000.0
162,2019년 08월 28일,255.53,254.2,255.98,253.65,58340000.0
163,2019년 08월 29일,254.31,255.68,256.28,253.6,64849999.99999999
164,2019년 08월 30일,259.0,256.85,259.61,256.72,94160000.0
165,2019년 09월 02일,259.08,258.25,259.74,257.8,54890000.0
166,2019년 09월 03일,258.25,258.55,259.67,257.4,64319999.99999999
167,2019년 09월 04일,261.69,257.89,261.69,257.76,69290000.0
168,2019년 09월 05일,264.44,262.53,266.0,262.27,88990000.0
169,2019년 09월 06일,265.53,266.2,266.36,264.74,59200000.0
170,2019년 09월 09일,267.4,266.11,268.67,265.92,62210000.0
171,2019년 09월 10일,268.76,267.96,269.13,267.35,66340000.0
172,2019년 09월 11일,270.84,270.35,271.35,269.58,103900000.0
173,2019년 09월 16일,272.02,271.48,272.03,269.59,103340000.0
174,2019년 09월 17일,271.68,271.46,272.26,270.9,73740000.0
175,2019년 09월 18일,273.06,271.88,273.72,271.04,67600000.0
176,2019년 09월 19일,274.92,273.11,276.15,273.03,70500000.0
177,2019년 09월 20일,276.44,275.01,276.67,274.46,74030000.0
178,2019년 09월 23일,276.65,275.86,277.11,275.64,60190000.0
179,2019년 09월 24일,277.97,276.41,278.17,275.55,68270000.0
180,2019년 09월 25일,274.42,276.67,277.04,274.12,70840000.0
181,2019년 09월 26일,274.89,275.61,276.38,273.5,89590000.0
182,2019년 09월 27일,271.33,273.32,273.4,270.52,57740000.0
183,2019년 09월 30일,273.55,270.61,273.85,270.06,54690000.0
184,2019년 10월 01일,274.42,273.43,275.38,272.81,52630000.0
185,2019년 10월 02일,268.55,272.39,272.42,268.55,66209999.99999999
186,2019년 10월 04일,267.39,268.14,270.16,267.39,62880000.0
187,2019년 10월 07일,267.67,269.12,269.75,266.83,53570000.0
188,2019년 10월 08일,271.36,268.9,271.51,267.97,75080000.0
189,2019년 10월 10일,268.8,268.79,270.78,266.23,98180000.0
190,2019년 10월 11일,271.3,271.68,272.68,270.58,59640000.0
191,2019년 10월 14일,274.23,274.71,275.75,273.97,71050000.0
192,2019년 10월 15일,274.34,273.85,275.09,273.83,53050000.0
193,2019년 10월 16일,276.32,276.26,277.59,275.24,65379999.99999999
194,2019년 10월 17일,275.57,275.92,276.68,274.62,53870000.0
195,2019년 10월 18일,272.81,275.99,277.06,272.28,71470000.0
196,2019년 10월 21일,273.79,273.87,274.1,272.67,45390000.0
197,2019년 10월 22일,277.41,275.4,278.19,274.93,63880000.0
198,2019년 10월 23일,276.23,276.83,277.29,275.34,65290000.00000001
199,2019년 10월 24일,276.7,277.7,277.84,274.31,73680000.0
200,2019년 10월 25일,277.07,277.6,277.67,275.7,58280000.0
201,2019년 10월 28일,277.78,278.16,279.42,277.29,59070000.0
202,2019년 10월 29일,277.55,279.25,279.49,276.81,57150000.0
203,2019년 10월 30일,275.51,277.49,277.49,274.95,72530000.0
204,2019년 10월 31일,275.82,276.84,279.04,275.82,78340000.0
205,2019년 11월 01일,278.43,276.17,278.55,275.59,58540000.0
206,2019년 11월 04일,282.54,279.9,282.77,279.9,77000000.0
207,2019년 11월 05일,284.38,282.99,284.46,282.27,76360000.0
208,2019년 11월 06일,284.52,284.31,286.43,283.7,77950000.0
209,2019년 11월 07일,284.37,284.04,284.82,283.05,70720000.0
210,2019년 11월 08일,283.13,285.81,286.12,282.57,73680000.0
211,2019년 11월 11일,280.85,283.3,283.3,280.4,61560000.0
212,2019년 11월 12일,283.65,281.9,283.65,280.79,65569999.99999999
213,2019년 11월 13일,281.28,282.28,282.49,280.62,60130000.0
214,2019년 11월 14일,283.93,281.9,283.93,281.1,79760000.0
215,2019년 11월 15일,287.29,283.85,287.72,283.3,71650000.0
216,2019년 11월 18일,287.18,287.1,287.78,285.94,56110000.0
217,2019년 11월 19일,286.23,285.74,286.34,283.73,68170000.0
218,2019년 11월 20일,282.21,284.27,285.29,282.0,73550000.0
219,2019년 11월 21일,278.28,281.89,282.33,276.98,81750000.0
220,2019년 11월 22일,279.43,278.57,279.89,278.14,74350000.0
221,2019년 11월 25일,282.19,281.77,283.53,281.48,64510000.00000001
222,2019년 11월 26일,281.83,283.09,284.87,281.77,119520000.0
223,2019년 11월 27일,282.85,283.56,284.02,281.27,60900000.0
224,2019년 11월 28일,281.23,281.23,282.8,281.05,61620000.0
225,2019년 11월 29일,276.78,282.08,282.1,276.34,74600000.0
226,2019년 12월 02일,277.44,278.27,279.44,276.97,66610000.0
227,2019년 12월 03일,276.19,274.93,276.63,273.88,67280000.0
228,2019년 12월 04일,274.15,274.36,274.68,272.53,81920000.0
229,2019년 12월 05일,273.3,275.65,276.63,272.53,66510000.00000001
230,2019년 12월 06일,276.56,275.19,277.0,274.75,70110000.0
231,2019년 12월 09일,278.03,278.31,278.75,277.02,70520000.0
232,2019년 12월 10일,279.31,276.8,279.75,276.62,56670000.0
233,2019년 12월 11일,280.28,279.19,281.03,278.45,66849999.99999999
234,2019년 12월 12일,285.11,282.62,285.43,282.62,133650000.0
235,2019년 12월 13일,290.11,289.34,290.11,288.32,99620000.0
236,2019년 12월 16일,289.76,289.54,290.8,289.36,73740000.0
237,2019년 12월 17일,294.4,291.76,294.5,290.52,94370000.0
238,2019년 12월 18일,294.22,294.31,295.85,293.05,76910000.0
239,2019년 12월 19일,294.31,295.06,295.84,292.82,70190000.0
240,2019년 12월 20일,295.31,295.58,295.86,293.95,75970000.0
241,2019년 12월 23일,294.87,295.84,296.12,293.81,62090000.0
242,2019년 12월 24일,292.9,295.18,295.25,292.47,60610000.0
243,2019년 12월 26일,294.06,293.12,294.07,291.79,68480000.0
244,2019년 12월 27일,295.2,292.37,296.95,291.28,77510000.0
245,2019년 12월 30일,293.77,294.6,295.49,293.4,54970000.0
......
,date,close,open,high,low,vol
0,20200102,290.35,294.19,294.57,289.96,66160000.0
1,20200103,290.74,293.1,294.88,289.04,79770000.0
2,20200106,288.43,287.73,289.41,287.49,69960000.0
3,20200107,291.23,290.01,292.26,289.64,65349999.99999999
4,20200108,289.42,288.94,291.01,287.3,124530000.0
5,20200109,294.41,293.83,294.41,292.1,111790000.0
6,20200110,297.06,294.8,297.17,294.17,94160000.0
7,20200113,300.13,296.62,300.31,296.53,81680000.0
8,20200114,301.53,302.2,303.61,300.69,92120000.0
9,20200115,299.74,299.91,301.13,298.82,71770000.0
10,20200116,302.78,299.93,302.91,298.95,85710000.0
11,20200117,303.3,305.07,306.05,302.51,81540000.0
12,20200120,305.58,304.99,307.54,304.77,78040000.0
13,20200121,302.11,304.94,306.09,301.85,67630000.0
14,20200122,306.08,301.79,306.52,301.16,79330000.0
15,20200123,302.33,303.77,304.72,301.71,86910000.0
16,20200128,292.77,294.98,296.3,291.3,130169999.99999999
17,20200129,293.98,294.38,295.67,292.45,85730000.0
18,20200130,288.37,293.27,294.11,287.09,101540000.0
19,20200131,284.53,290.24,291.47,284.53,101460000.0
20,20200203,285.05,280.17,286.24,279.78,111060000.0
21,20200204,290.68,285.19,291.38,285.11,105450000.0
22,20200205,292.02,293.57,294.26,290.3,102640000.0
23,20200206,300.65,294.74,300.98,294.32,110220000.0
24,20200207,298.21,299.59,300.05,296.01,108110000.0
25,20200210,296.24,294.07,296.73,293.33,72220000.0
26,20200211,299.28,297.91,301.1,297.48,78350000.0
27,20200212,301.54,299.83,302.2,298.09,110630000.0
28,20200213,300.93,302.79,304.43,300.48,115070000.0
29,20200214,303.01,300.95,304.2,299.18,80530000.0
30,20200217,302.76,302.84,304.05,300.99,84280000.0
31,20200218,297.74,300.26,301.29,297.2,84750000.0
32,20200219,298.33,299.68,300.38,295.53,84260000.0
33,20200220,296.65,300.34,301.35,295.01,90010000.0
34,20200221,292.42,292.16,295.24,291.96,100710000.0
35,20200224,281.02,285.51,286.62,281.02,132820000.0
36,20200225,284.24,280.48,284.63,279.77,117030000.0
37,20200226,279.94,278.45,281.98,277.68,119550000.0
38,20200227,277.09,279.46,281.09,276.07,110500000.0
39,20200228,268.02,272.33,273.98,267.23,202610000.0
40,20200302,270.37,269.59,272.6,265.7,163720000.0
41,20200303,271.56,277.44,277.54,270.79,146720000.0
42,20200304,278.13,270.13,278.9,270.13,208460000.0
43,20200305,281.38,280.66,282.05,277.91,147440000.0
44,20200306,275.1,276.74,278.31,274.0,140900000.0
45,20200309,263.11,266.39,267.75,261.76,182670000.0
46,20200310,264.67,261.94,265.57,260.75,173090000.0
47,20200311,257.01,264.63,265.04,255.69,194210000.0
48,20200312,247.62,253.93,255.49,243.76,242470000.0
49,20200313,240.65,231.71,246.19,227.36,298900000.0
50,20200316,232.97,245.21,245.21,232.76,175050000.0
51,20200317,226.89,222.94,234.56,222.46,216730000.0
52,20200318,215.83,228.63,230.04,215.81,208040000.0
53,20200319,199.28,220.72,220.85,196.27,303560000.0
54,20200320,213.67,204.84,214.08,200.54,269350000.0
55,20200323,201.87,200.8,206.66,198.67,210300000.0
56,20200324,220.34,207.65,220.34,205.41,225730000.0
57,20200325,232.89,228.37,233.42,224.74,306110000.0
58,20200326,229.34,231.91,236.61,229.22,290840000.0
59,20200327,233.79,239.11,239.48,227.01,249100000.0
60,20200330,232.45,227.05,234.7,225.09,167230000.0
61,20200331,236.82,235.2,237.47,232.5,240440000.0
62,20200401,226.35,234.23,237.52,226.34,228580000.0
63,20200402,231.84,227.47,232.22,223.6,181790000.0
64,20200403,231.7,232.48,234.45,229.23,181040000.0
65,20200406,240.81,234.4,241.19,233.75,190740000.0
66,20200407,244.87,245.65,247.39,240.68,224450000.0
67,20200408,241.89,243.71,246.99,241.77,173120000.0
68,20200409,245.61,246.13,246.46,243.11,175940000.0
69,20200410,248.0,245.14,248.21,244.02,286110000.0
70,20200413,243.4,246.59,246.9,243.39,195900000.0
71,20200414,247.45,246.2,248.55,244.58,193120000.0
72,20200416,247.1,244.99,247.87,243.61,197540000.0
73,20200417,255.02,252.52,257.37,252.38,287360000.0
74,20200420,252.14,253.85,255.93,251.46,211000000.0
75,20200421,249.4,250.26,251.88,245.11,257160000.00000003
76,20200422,251.88,246.16,252.18,244.56,184500000.0
77,20200423,253.74,253.42,255.22,251.58,163810000.0
78,20200424,250.28,252.47,252.86,249.19,154310000.0
79,20200427,254.84,251.18,255.79,250.61,151360000.0
80,20200428,256.39,255.91,257.34,253.22,172420000.0
81,20200429,258.15,256.58,259.67,256.04,184480000.0
82,20200504,250.6,252.44,253.7,250.34,174040000.0
83,20200506,255.0,253.76,255.01,251.26,133280000.0
84,20200507,254.46,253.88,256.14,253.37,114360000.0
85,20200508,256.62,256.66,258.29,256.23,125220000.0
86,20200511,254.95,257.67,258.87,254.59,144300000.0
87,20200512,253.37,255.33,255.56,250.98,166800000.0
88,20200513,255.85,250.03,255.85,249.95,141250000.0
89,20200514,253.65,253.26,254.39,252.09,167640000.0
90,20200515,253.79,255.34,255.49,252.02,228900000.0
91,20200518,255.44,254.55,256.31,252.95,223000000.0
92,20200519,261.79,261.12,262.34,259.96,253330000.0
93,20200520,262.72,260.48,263.25,260.48,140300000.0
94,20200521,263.74,264.52,264.85,263.07,159480000.0
95,20200522,259.62,263.82,264.07,258.34,166030000.0
96,20200525,262.76,260.95,262.82,259.16,125430000.0
97,20200526,267.31,263.48,267.31,262.95,175420000.0
98,20200527,267.64,266.71,269.17,265.86,247180000.0
99,20200528,268.29,270.17,271.44,264.85,248930000.0
100,20200529,268.32,266.62,269.61,265.23,269470000.0
101,20200601,273.19,269.29,273.39,268.93,171510000.0
102,20200602,276.08,272.65,276.47,272.22,251030000.0
103,20200603,285.91,279.14,287.49,278.98,476500000.0
104,20200604,286.45,291.17,292.9,284.93,364580000.0
105,20200605,290.62,286.3,291.5,284.5,282060000.0
106,20200608,290.77,295.41,295.68,289.71,286660000.0
107,20200609,291.32,293.96,294.87,287.83,268570000.0
108,20200610,291.9,291.03,292.92,289.6,229070000.0
109,20200611,288.62,289.98,292.08,284.29,275900000.0
110,20200612,281.78,276.97,282.54,276.05,232830000.0
111,20200615,267.95,278.61,280.87,267.95,262440000.0
112,20200616,282.59,275.89,282.59,274.0,247890000.0
113,20200617,283.02,282.08,284.9,278.33,337220000.0
114,20200618,281.91,281.74,283.39,280.02,181590000.0
115,20200619,283.37,284.56,284.7,278.53,189130000.0
116,20200622,281.42,281.01,283.71,280.34,139840000.0
117,20200623,281.94,284.64,286.04,279.26,163680000.0
118,20200624,286.7,283.71,288.3,283.3,200570000.0
119,20200625,279.73,282.38,284.36,279.73,152980000.0
120,20200626,283.38,283.36,284.58,280.65,169010000.0
121,20200629,278.04,279.36,281.77,277.06,137760000.0
122,20200630,280.09,282.55,283.98,280.09,179500000.0
123,20200701,280.26,283.1,283.84,279.44,133800000.00000001
124,20200702,283.86,281.3,283.89,280.71,111370000.0
125,20200703,285.89,285.59,286.09,283.36,100220000.0
126,20200706,290.62,286.84,291.14,286.31,128120000.0
127,20200707,286.77,292.74,292.98,286.76,156130000.0
128,20200708,285.97,287.2,288.41,285.18,119450000.0
129,20200709,287.25,287.8,289.41,287.08,140610000.0
130,20200710,285.06,287.87,288.09,283.51,136770000.0
131,20200713,289.84,287.92,290.49,286.86,127050000.0
132,20200714,289.63,288.42,289.63,286.89,126300000.0
133,20200715,292.27,292.94,294.42,290.89,168350000.0
134,20200716,289.25,292.34,292.74,288.46,129449999.99999999
135,20200717,291.57,289.9,292.1,289.9,118640000.0
136,20200720,290.81,292.76,292.76,289.01,161820000.0
137,20200721,295.16,294.03,296.11,293.2,366990000.0
138,20200722,294.04,294.71,296.14,293.39,327320000.0
139,20200723,292.37,293.62,293.62,290.49,250980000.0
140,20200724,290.66,289.84,292.74,289.42,216210000.0
141,20200727,293.51,291.03,295.26,290.82,193240000.0
142,20200728,300.13,296.53,301.34,296.38,301280000.0
143,20200729,301.25,300.57,303.51,299.61,264440000.0
144,20200730,301.85,303.13,304.13,301.49,191760000.0
145,20200731,299.32,303.69,304.16,298.89,201960000.0
146,20200803,299.46,299.57,300.33,297.57,179730000.0
147,20200804,303.04,302.5,304.27,301.48,219870000.0
148,20200805,306.64,303.94,306.67,302.91,210810000.0
149,20200806,311.32,307.88,312.84,307.88,254200000.0
150,20200807,312.57,311.99,314.11,310.0,299120000.0
151,20200810,316.77,312.75,317.84,312.08,261180000.0
152,20200811,321.02,317.63,322.64,317.56,297600000.0
153,20200812,322.68,320.31,322.68,317.3,293140000.0
154,20200813,323.33,325.84,326.22,319.66,285740000.0
155,20200814,319.24,321.59,322.66,316.28,260399999.99999997
156,20200818,312.84,319.57,321.69,311.2,247520000.0
157,20200819,313.54,315.96,316.82,313.18,159930000.0
158,20200820,301.59,310.45,312.77,301.15,249740000.0
159,20200821,306.16,306.17,309.88,303.15,166300000.0
160,20200824,309.33,306.86,310.27,303.39,142210000.0
161,20200825,313.59,312.45,314.14,310.57,197820000.0
162,20200826,314.19,313.61,315.08,309.74,193540000.0
163,20200827,311.38,314.46,315.17,311.37,175570000.0
164,20200828,312.24,313.86,315.95,311.06,256410000.00000003
165,20200831,307.14,315.46,316.11,307.12,312180000.0
166,20200901,309.81,309.08,311.15,307.63,342810000.0
167,20200902,311.5,311.69,312.92,308.52,337040000.0
168,20200903,316.43,314.73,317.46,314.45,342180000.0
169,20200904,312.03,308.25,312.64,308.04,408030000.0
170,20200907,313.67,312.08,314.44,310.93,264670000.00000003
171,20200908,317.38,316.31,317.43,314.56,227270000.0
172,20200909,313.77,313.54,315.9,312.9,198500000.0
173,20200910,316.53,317.92,318.38,315.46,234270000.0
174,20200911,316.45,315.64,316.77,313.2,165160000.0
175,20200914,320.98,319.83,321.22,318.54,176780000.0
176,20200915,323.36,321.82,323.57,320.54,152450000.0
177,20200916,322.31,323.14,324.19,321.52,146880000.0
178,20200917,318.0,321.3,322.23,316.92,178300000.0
179,20200918,318.39,319.2,319.81,316.62,153540000.0
180,20200921,315.89,318.13,320.86,314.78,165860000.0
181,20200922,308.82,315.53,316.02,307.4,304360000.0
182,20200923,309.64,311.5,311.76,303.29,208050000.0
183,20200924,302.48,304.85,306.98,301.95,181250000.0
184,20200925,303.57,305.38,305.55,301.93,156860000.0
185,20200928,307.03,306.49,308.34,304.64,173180000.0
186,20200929,309.44,310.01,311.34,308.52,138820000.0
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