超簡単Pythonで株価予測(keras・DNN 利用)ディープラーニング
Pythonでkerasを利用して翌日の株価の上下予測を超簡単にディープラーニング(DNN利用)
1. ツールインストール
$ pip install scikit-learn keras pandas-datareader
2. ファイル作成
pred.py
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from keras import Sequential
from keras.layers import Dense
from sklearn.metrics import accuracy_score
import pandas_datareader as pdr
df = pdr.get_data_yahoo("AAPL", "2010-11-01", "2020-11-01")
df["Diff"] = df.Close.diff()
df["SMA_2"] = df.Close.rolling(2).mean()
df["Force_Index"] = df.Close * df.Volume
df["y"] = df["Diff"].apply(lambda x: 1 if x > 0 else 0).shift(-1)
df = df.drop(
["Open", "High", "Low", "Close", "Volume", "Diff", "Adj Close"],
axis=1,
).dropna()
# print(df)
X = StandardScaler().fit_transform(df.drop(["y"], axis=1))
y = df["y"].values
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.2,
shuffle=False,
)
model = Sequential()
model.add(Dense(2, activation="relu", input_shape=(X_train.shape[1],)))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["acc"])
model.fit(X_train, y_train, epochs=100)
y_pred = model.predict(X_test)
print(accuracy_score(y_test, y_pred > 0.5))
3. 実行
$ python pred.py
Epoch 1/100
63/63 [==============================] - 0s 1ms/step - loss: 0.6980 - acc: 0.5209
Epoch 2/100
63/63 [==============================] - 0s 1ms/step - loss: 0.6955 - acc: 0.5219
:
Epoch 99/100
63/63 [==============================] - 0s 792us/step - loss: 0.6917 - acc: 0.5184
Epoch 100/100
63/63 [==============================] - 0s 869us/step - loss: 0.6916 - acc: 0.5159
0.5496031746031746
以上、超簡単!
4. 結果
同じデータ、特徴量で、計算した結果、XGBoost・DNN・LSTM・GRU・RNN・LogisticRegression・k-nearest neighbor・RandomForest・BernoulliNB・SVM・RGF・MLP・Bagging・Voting・Stacking・LightGBM・TCN・HGBCのうちMLPが最も良いという事に
XGBoost 0.5119047619047619
DNN 0.5496031746031746
LSTM 0.5178571428571429
GRU 0.5138888888888888
RNN 0.5376984126984127
LogisticRegression 0.5496031746031746
k-nearest neighbor 0.5198412698412699
RandomForest 0.49603174603174605
BernoulliNB 0.5496031746031746
SVM 0.5396825396825397
RGF 0.5158730158730159
MLP 0.5694444444444444
Bagging 0.5297619047619048
Voting 0.5416666666666666
Stacking 0.5218253968253969
LightGBM 0.5456349206349206
TCN 0.5198412698412699
HGBC 0.5
5. 参考
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