超簡単Pythonで株価予測（Optuna・LightGBM 利用）ハイパーパラメータ自動最適化
PythonでOptunaを利用して超簡単に翌日の株価の上下予測のハイパーパラメータを自動最適化
自動最適化元は下記の過去投稿をどうぞ
1. ツールインストール
＄ pip install scikit-learn lightgbm pandas-datareader optuna2. 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
import numpy as np
import optuna
def objective(trial):
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.2,
shuffle=False,
)
dtrain = lgb.Dataset(X_train, label=y_train)
param = {
"objective": "binary",
"metric": "binary_logloss",
"verbosity": -1,
"boosting_type": "gbdt",
"lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True),
"lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True),
"num_leaves": trial.suggest_int("num_leaves", 2, 256),
"feature_fraction": trial.suggest_float("feature_fraction", 0.4, 1.0),
"bagging_fraction": trial.suggest_float("bagging_fraction", 0.4, 1.0),
"bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
}
gbm = lgb.train(param, dtrain)
preds = gbm.predict(X_test)
pred_labels = np.rint(preds)
accuracy = accuracy_score(y_test, pred_labels)
return accuracy
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,
)
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=100)
clf = lgb.LGBMRegressor(**dict(trial.params.items()))
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
0.5515873015873016以上、超簡単！
4. 結果
自動最適化前 0.5456349206349206
自動最適化後 0.5515873015873016
約1%改善しました