超簡単Pythonで株価予測（LightGBM 利用）機械学習

PythonでLightGBMを利用して翌日の株価の上下予測を超簡単に機械学習

Optunaを利用した関連投稿も宜しければどうぞ

1. ツールインストール

＄ pip install scikit-learn lightgbm pandas-datareader

2. ファイル作成

pred.py

import pandas_datareader as pdr
from sklearn.model_selection import train_test_split
import lightgbm as lgb
from sklearn.metrics import accuracy_score

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 = df.drop(["y"], axis=1).values
y = df["y"].values
X_train, X_test, y_train, y_test = train_test_split(
  X,
  y,
  test_size=0.2,
  shuffle=False,
)
clf = lgb.LGBMRegressor(learning_rate=0.01, n_estimators=20)
clf.fit(
  X_train,
  y_train,
)
y_pred = clf.predict(X_test)
print(accuracy_score(y_test, y_pred > 0.5))

3. 実行

$ python pred.py

0.5456349206349206

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

Welcome to LightGBM’s documentation! — LightGBM 3.2.1.99 documentation