ビットコインの動きを予測！「いつもの範囲」で考える投資の作戦

"""
Standard Deviation Channel Trading Signal
標準偏差チャネルを使用したトレード
"""
import pandas as pd
import numpy as np


def calculate_standard_deviation_channel_signals(df: pd.DataFrame,
                                              period: int = 20,
                                              std_multiplier: float = 2.0) -> pd.DataFrame:
    """
    Standard Deviation Channel戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    period : int
        計算期間（デフォルト: 20）
    std_multiplier : float
        標準偏差の乗数（デフォルト: 2.0）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # 線形回帰値と標準偏差を計算
    df['lr_value'] = np.nan
    df['std_value'] = np.nan
    
    for i in range(period - 1, len(df)):
        data = df['close'].iloc[i-period+1:i+1].values
        if len(data) >= 2:
            x = np.arange(len(data))
            coeffs = np.polyfit(x, data, 1)
            regression_line = coeffs[0] * x + coeffs[1]
            df.loc[df.index[i], 'lr_value'] = regression_line[-1]
            df.loc[df.index[i], 'std_value'] = np.std(data - regression_line)
    
    # チャネル
    df['upper_channel'] = df['lr_value'] + std_multiplier * df['std_value']
    df['lower_channel'] = df['lr_value'] - std_multiplier * df['std_value']
    
    # シグナル初期化
    df['signal'] = 0
    df['is_buy'] = False
    df['is_sell'] = False
    
    position = 0
    
    for i in range(period, len(df)):
        # 買いシグナル
        if position <= 0 and df['close'].iloc[i] > df['lower_channel'].iloc[i] and \
           df['close'].iloc[i-1] <= df['lower_channel'].iloc[i-1]:
            df.loc[df.index[i], 'is_buy'] = True
            df.loc[df.index[i], 'signal'] = 1
            position = 1
            
        # 売りシグナル
        elif position >= 0 and df['close'].iloc[i] < df['upper_channel'].iloc[i] and \
             df['close'].iloc[i-1] >= df['upper_channel'].iloc[i-1]:
            df.loc[df.index[i], 'is_sell'] = True
            df.loc[df.index[i], 'signal'] = -1
            position = -1
        else:
            df.loc[df.index[i], 'signal'] = position
    
    df['signal'] = df['signal'].fillna(0)
    
    return df