딥러닝으로 주식 예측하기!
!pip install finance-datareader
Collecting finance-datareader Downloading finance_datareader-0.9.31-py3-none-any.whl (17 kB) Collecting requests-file Downloading requests_file-1.5.1-py2.py3-none-any.whl (3.7 kB) Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (4.62.3) Requirement already satisfied: pandas>=0.19.2 in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (1.3.5) Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (4.2.6) Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (2.23.0) Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader) (2018.9) Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader) (2.8.2) Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader) (1.21.5) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19.2->finance-datareader) (1.15.0) Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (3.0.4) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (1.24.3) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (2021.10.8) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (2.10) Installing collected packages: requests-file, finance-datareader Successfully installed finance-datareader-0.9.31 requests-file-1.5.1
import pandas as pd
import FinanceDataReader as fdr
import matplotlib.pyplot as plt
df = fdr.DataReader('005930','2018-01-01','2022-02-24') # 삼성전자: 005930
df
| Open | High | Low | Close | Volume | Change | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2018-01-02 | 51380 | 51400 | 50780 | 51020 | 169485 | 0.001177 |
| 2018-01-03 | 52540 | 52560 | 51420 | 51620 | 200270 | 0.011760 |
| 2018-01-04 | 52120 | 52180 | 50640 | 51080 | 233909 | -0.010461 |
| 2018-01-05 | 51300 | 52120 | 51200 | 52120 | 189623 | 0.020360 |
| 2018-01-08 | 52400 | 52520 | 51500 | 52020 | 167673 | -0.001919 |
| ... | ... | ... | ... | ... | ... | ... |
| 2022-02-18 | 74600 | 74800 | 73700 | 74300 | 10122226 | -0.009333 |
| 2022-02-21 | 73200 | 74300 | 72600 | 74200 | 10489717 | -0.001346 |
| 2022-02-22 | 73000 | 73400 | 72800 | 73400 | 11692469 | -0.010782 |
| 2022-02-23 | 73800 | 73800 | 72800 | 73000 | 10397964 | -0.005450 |
| 2022-02-24 | 72300 | 72300 | 71300 | 71500 | 15502410 | -0.020548 |
1022 rows × 6 columns
data = df['Close'].values
plt.figure(figsize=(12, 8))
plt.plot(data)
plt.show()
from sklearn.preprocessing import MinMaxScaler
import numpy as np
data = np.array(data).reshape(-1,1) # 표준화를 하기 위해 사이즈를 (-1,1)로 조정
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(data)
scaled
array([[0.25340803],
[0.26461251],
[0.25452848],
...,
[0.6713352 ],
[0.66386555],
[0.63585434]])
test_idx = int(len(scaled) * 0.8)
train = scaled[:test_idx]
test = scaled[test_idx:]
x_train = []
y_train = []
x_test = []
y_test = []
pasts = 15
for i in range(pasts, len(train)):
x_train.append(train[i-pasts:i, 0])
y_train.append(train[i, 0])
for i in range(pasts, len(test)):
x_test.append(test[i-pasts:i, 0])
y_test.append(test[i, 0])
x_train = np.array(x_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
y_train = np.array(y_train)
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
y_test = np.array(y_test)
x_train.shape, y_train.shape, x_test.shape, y_test.shape
((802, 15, 1), (802,), (190, 15, 1), (190,))
keras를 활용한 예측 모델 생성
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import LSTM
model = Sequential()
model.add(LSTM(50, input_shape=(x_train.shape[1], x_train.shape[2]), activation='relu', return_sequences=True))
model.add(LSTM(10, input_shape=(x_train.shape[1], x_train.shape[2]), activation='relu', return_sequences=False))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
history = model.fit(x_train, y_train, epochs=15, batch_size=16,)
Epoch 1/20 51/51 [==============================] - 5s 13ms/step - loss: 0.0521 Epoch 2/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0042 Epoch 3/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0025 Epoch 4/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0023 Epoch 5/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0019 Epoch 6/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0019 Epoch 7/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0016 Epoch 8/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0014 Epoch 9/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0013 Epoch 10/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0013 Epoch 11/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0013 Epoch 12/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0012 Epoch 13/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0012 Epoch 14/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0012 Epoch 15/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0011 Epoch 16/20 51/51 [==============================] - 1s 12ms/step - loss: 0.0011 Epoch 17/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0010 Epoch 18/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0011 Epoch 19/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0010 Epoch 20/20 51/51 [==============================] - 1s 13ms/step - loss: 0.0011
plt.plot(history.history['loss'])
plt.show()
preds = model.predict(x_test)
plt.plot(preds, label='pred')
plt.plot(y_test, label='label')
plt.legend()
plt.show()
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