PythonでAutoKerasを利用して翌日の株価の上下予測を超簡単に自動機械学習（AutoML）

1. ツールインストール

$ pip install autokeras yfinance

2. ファイル作成

pred.py

import yfinance as yf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import autokeras as ak

df = yf.download("AAPL", start="2010-11-01", end="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,
)
clf = ak.StructuredDataClassifier(overwrite=True, max_trials=3)
clf.fit(X_train, y_train, epochs=10)
y_pred = clf.predict(X_test)
print(accuracy_score(y_test, y_pred))

3. 実行

$ python pred.py

0.4861111111111111

以上、超簡単！

4. 結果

同じデータ、特徴量で、計算した結果、PyCaret・PyCaret(bagging)・PyCaret(voting)・PyCaret(stacking)・TPOT・TPOT(NN)・Auto-sklearn・AutoGluon・AutoKeras・FLAMLのうちTPOTが最も良いという事に

PyCaret            0.5178571428571429
PyCaret(bagging)   0.5496031746031746
PyCaret(voting)    0.5535714285714286
PyCaret(stacking)  0.5496031746031746
TPOT               0.5555555555555556
TPOT(NN)           0.503968253968254
Auto-sklearn       0.5198412698412699
AutoGluon          0.5496031746031746
AutoKeras          0.4861111111111111
FLAML              0.5277777777777778

5. 参考​