{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### case3. Simple Price Data (시가, 종가, 고가, 저가, 거래량) + Exponential Moving Average(종가) + Exponential Volume Moving Average(거래량)\n",
    "---\n",
    "단방향 LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "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": 36,
   "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')\n",
    "\n",
    "train_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/train_EMA_scaled.csv')\n",
    "test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/test_EMA_scaled.csv')\n",
    "val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/close/exp/val_EMA_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/train_EVMA_scaled.csv')\n",
    "test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/test_EVMA_scaled.csv')\n",
    "val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/vol/exp/val_EVMA_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#불필요한 열 삭제\n",
    "\n",
    "del train['Unnamed: 0']\n",
    "del test['Unnamed: 0']\n",
    "del validation['Unnamed: 0']\n",
    "del train_EMA['Unnamed: 0']\n",
    "del test_EMA['Unnamed: 0']\n",
    "del val_EMA['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train_EVMA['Unnamed: 0']\n",
    "del test_EVMA['Unnamed: 0']\n",
    "del val_EVMA['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_EVMA = train_EVMA.rename(columns={'5':'5vol','10':'10vol','20':'20vol','60':'60vol','120':'120vol'})\n",
    "test_EVMA = test_EVMA.rename(columns={'5':'5vol','10':'10vol','20':'20vol','60':'60vol','120':'120vol'})\n",
    "val_EVMA = val_EVMA.rename(columns={'5':'5vol','10':'10vol','20':'20vol','60':'60vol','120':'120vol'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, train_EMA, train_EVMA], axis = 1)\n",
    "test = pd.concat([test, test_EMA, test_EVMA],axis = 1)\n",
    "validation = pd.concat([validation,val_EMA, val_EVMA], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
      "5         0\n",
      "10        0\n",
      "20        0\n",
      "60        0\n",
      "120       0\n",
      "5vol      0\n",
      "10vol     0\n",
      "20vol     0\n",
      "60vol     0\n",
      "120vol    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",
      "5         0\n",
      "10        0\n",
      "20        0\n",
      "60        0\n",
      "120       0\n",
      "5vol      0\n",
      "10vol     0\n",
      "20vol     0\n",
      "60vol     0\n",
      "120vol    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",
      "5         0\n",
      "10        0\n",
      "20        0\n",
      "60        0\n",
      "120       0\n",
      "5vol      0\n",
      "10vol     0\n",
      "20vol     0\n",
      "60vol     0\n",
      "120vol    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# NULL value 없나 확인해보기 (지수이동평균의 경우는 결측치가 없다)\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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "\n",
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       "<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",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>20</th>\n",
       "      <th>60</th>\n",
       "      <th>120</th>\n",
       "      <th>5vol</th>\n",
       "      <th>10vol</th>\n",
       "      <th>20vol</th>\n",
       "      <th>60vol</th>\n",
       "      <th>120vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.733962</td>\n",
       "      <td>0.746881</td>\n",
       "      <td>0.735279</td>\n",
       "      <td>0.748024</td>\n",
       "      <td>0.00197</td>\n",
       "      <td>0.720779</td>\n",
       "      <td>0.70037</td>\n",
       "      <td>0.679747</td>\n",
       "      <td>0.713556</td>\n",
       "      <td>0.79845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      close      open      high       low      vol         5       10  \\\n",
       "0  0.733962  0.746881  0.735279  0.748024  0.00197  0.720779  0.70037   \n",
       "\n",
       "         20        60      120  5vol  10vol  20vol  60vol  120vol  \n",
       "0  0.679747  0.713556  0.79845   0.0    0.0    0.0    0.0     0.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature , label 분리\n",
    "feature_cols = ['open','high','low','vol','5','10','20','60','120','5vol','10vol','20vol','60vol','120vol']\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": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, X_val, y_val = train_feature, train_label, validation_feature, validation_label\n",
    "X_test, y_test = test_feature, test_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3444, 20, 14)\n",
      "(3444, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_11 (LSTM)               (None, 20, 64)            20224     \n",
      "_________________________________________________________________\n",
      "lstm_12 (LSTM)               (None, 50)                23000     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 43,275\n",
      "Trainable params: 43,275\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# keras 활용한 LSTM 모델 생성\n",
    "model = Sequential()\n",
    "model.add(LSTM(64, return_sequences=True, input_shape=(20,14)))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(1, activation='linear'))\n",
    "model.compile(loss='mse',optimizer='adam')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3444 samples, validate on 167 samples\n",
      "Epoch 1/100\n",
      "3444/3444 [==============================] - 12s 4ms/step - loss: 0.0159 - val_loss: 0.0241\n",
      "Epoch 2/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 0.0010 - val_loss: 0.0190\n",
      "Epoch 3/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 8.2369e-04 - val_loss: 0.0148\n",
      "Epoch 4/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 7.4361e-04 - val_loss: 0.0117\n",
      "Epoch 5/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 7.0004e-04 - val_loss: 0.0107\n",
      "Epoch 6/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 6.6421e-04 - val_loss: 0.0094\n",
      "Epoch 7/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 6.2449e-04 - val_loss: 0.0087\n",
      "Epoch 8/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 6.5511e-04 - val_loss: 0.0086\n",
      "Epoch 9/100\n",
      "3444/3444 [==============================] - 7s 2ms/step - loss: 5.9712e-04 - val_loss: 0.0086\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping() \n",
    "hist = model.fit(X_train, y_train, validation_data=(X_val, y_val),batch_size=50, epochs=100,callbacks=[early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "226/226 [==============================] - 0s 838us/step\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test,y_test,batch_size=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.015598734296792377\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x64585d978>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 모델 학습 과정\n",
    "fig, loss_ax = plt.subplots()\n",
    "\n",
    "acc_ax = loss_ax.twinx()\n",
    "\n",
    "loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n",
    "loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n",
    "\n",
    "loss_ax.set_xlabel('epoch')\n",
    "loss_ax.set_ylabel('loss')\n",
    "\n",
    "\n",
    "loss_ax.legend(loc='upper left')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list['50'] = score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'5': 0.0041133320130061655,\n",
       " '10': 0.007424544826339502,\n",
       " '20': 0.009714267598835966,\n",
       " '30': 0.0111304236023761,\n",
       " '50': 0.015598734296792377}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = dict()"
   ]
  },
  {
   "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
}