def prepare_ml_data(df):
    df['Future_Close'] = df['close'].shift(-1)
    df['Target'] = (df['Future_Close'] > df['close']).astype(int)

    features = ['MA20', 'MA50', 'RSI', 'MACD', 'Signal']
    df = df.dropna()  # 移除 NaN

    X = df[features]
    y = df['Target']

    return X, y

# -------------------------
# Step 4: 訓練 ML 模型
# -------------------------
def train_ml_model(X, y):
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, shuffle=False
    )

    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)

    print("混淆矩陣 (Confusion Matrix):")
    print(confusion_matrix(y_test, y_pred))
    print("\n分類報告 (Classification Report):")
    print(classification_report(y_test, y_pred))

    return model