ビットコインで挑戦！「買い手 vs 売り手」作戦の成績を見てみよう！

"""
Balance of Power Trading Signal Generator
買い手と売り手の力関係を測定
"""
import pandas as pd
import numpy as np


def calculate_bop_signals(df: pd.DataFrame,
                         smooth_period: int = 14) -> pd.DataFrame:
    """
    Balance of Power戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    smooth_period : int
        平滑化期間（デフォルト: 14）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # Balance of Power計算
    # BOP = (Close - Open) / (High - Low)
    df['bop_raw'] = (df['close'] - df['open']) / (df['high'] - df['low'] + 0.0001)
    
    # スムージング（SMA）
    df['bop'] = df['bop_raw'].rolling(window=smooth_period).mean()
    
    # BOPの移動平均（トレンド）
    df['bop_ma'] = df['bop'].rolling(window=smooth_period).mean()
    
    # BOPの標準偏差（ボラティリティ）
    df['bop_std'] = df['bop'].rolling(window=smooth_period * 2).std()
    
    # ダイナミックレベル
    df['bop_upper'] = df['bop_ma'] + df['bop_std']
    df['bop_lower'] = df['bop_ma'] - df['bop_std']
    
    # ゼロラインクロス
    df['bop_prev'] = df['bop'].shift(1)
    df['zero_cross_up'] = (df['bop'] > 0) & (df['bop_prev'] <= 0)
    df['zero_cross_down'] = (df['bop'] < 0) & (df['bop_prev'] >= 0)
    
    # エクストリーム反転
    df['extreme_low'] = df['bop'] < -0.5
    df['extreme_high'] = df['bop'] > 0.5
    df['extreme_low_prev'] = df['extreme_low'].shift(1)
    df['extreme_high_prev'] = df['extreme_high'].shift(1)
    
    # シグナル生成
    df['is_buy'] = (
        df['zero_cross_up'] | 
        (df['extreme_low_prev'] & ~df['extreme_low'])
    ) & df['bop'].notna()
    df['is_sell'] = (
        df['zero_cross_down'] | 
        (df['extreme_high_prev'] & ~df['extreme_high'])
    ) & df['bop'].notna()
    
    # 不要カラム削除
    df.drop(['bop_raw', 'bop_ma', 'bop_std', 'bop_upper', 'bop_lower', 'bop_prev',
             'zero_cross_up', 'zero_cross_down', 'extreme_low', 'extreme_high',
             'extreme_low_prev', 'extreme_high_prev'], axis=1, inplace=True, errors='ignore')
    
    return df