市場の元気さに合わせて賢く動く！新しいRSI作戦

"""
Adaptive RSI Trading Signal Generator
RSI期間を市場ボラティリティに応じて動的に調整する戦略
"""
import pandas as pd
import numpy as np


def calculate_adaptive_rsi_signals(df: pd.DataFrame,
                                  base_period: int = 14,
                                  atr_period: int = 14,
                                  oversold: float = 30,
                                  overbought: float = 70) -> pd.DataFrame:
    """
    アダプティブRSI戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    base_period : int
        基本RSI期間（デフォルト: 14）
    atr_period : int
        ATR計算期間（デフォルト: 14）
    oversold : float
        売られ過ぎライン（デフォルト: 30）
    overbought : float
        買われ過ぎライン（デフォルト: 70）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # ATR計算
    df['h_l'] = df['high'] - df['low']
    df['h_c'] = np.abs(df['high'] - df['close'].shift(1))
    df['l_c'] = np.abs(df['low'] - df['close'].shift(1))
    df['true_range'] = df[['h_l', 'h_c', 'l_c']].max(axis=1)
    df['atr'] = df['true_range'].rolling(window=atr_period).mean()
    
    # ATRに基づいて期間を調整（ボラティリティが高いほど短期間）
    df['atr_ratio'] = df['atr'] / df['atr'].rolling(window=50).mean()
    df['adaptive_period'] = (base_period / df['atr_ratio']).clip(5, 30).fillna(base_period).astype(int)
    
    # アダプティブRSI計算
    df['price_change'] = df['close'].diff()
    df['gain'] = df['price_change'].where(df['price_change'] > 0, 0)
    df['loss'] = -df['price_change'].where(df['price_change'] < 0, 0)
    
    # 各行で異なる期間でRSIを計算
    df['rsi'] = 0.0
    for i in range(len(df)):
        if i >= base_period:
            period = int(df.iloc[i]['adaptive_period'])
            if period > 0 and i >= period:
                avg_gain = df['gain'].iloc[i-period+1:i+1].mean()
                avg_loss = df['loss'].iloc[i-period+1:i+1].mean()
                if avg_loss != 0:
                    rs = avg_gain / avg_loss
                    df.loc[df.index[i], 'rsi'] = 100 - (100 / (1 + rs))
    
    # シグナル生成
    df['rsi_prev'] = df['rsi'].shift(1)
    df['is_buy'] = (df['rsi'] > oversold) & (df['rsi_prev'] <= oversold) & (df['rsi'] > 0)
    df['is_sell'] = (df['rsi'] < overbought) & (df['rsi_prev'] >= overbought) & (df['rsi'] > 0)
    
    # 不要カラム削除
    df.drop(['h_l', 'h_c', 'l_c', 'true_range', 'price_change', 'gain', 'loss', 
             'atr_ratio', 'adaptive_period', 'rsi_prev'], axis=1, inplace=True, errors='ignore')
    
    return df