ビットコインの動きは予測できる？「ふしぎな動き発見器」で挑戦！

"""
Random Walk Index Trading Signal Generator
価格変動がランダムウォークからどれだけ乖離しているかを測定
"""
import pandas as pd
import numpy as np


def calculate_rwi_signals(df: pd.DataFrame,
                         period: int = 14,
                         threshold: float = 1.0) -> pd.DataFrame:
    """
    Random Walk Index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: 14）
    threshold : float
        シグナル閾値（デフォルト: 1.0）
    
    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=period).mean()
    
    # RWI High と RWI Low の計算
    df['rwi_high'] = 0.0
    df['rwi_low'] = 0.0
    
    for i in range(2, period + 1):
        # RWI High
        high_move = df['high'] - df['low'].shift(i)
        expected_move = df['atr'] * np.sqrt(i)
        rwi_h = high_move / (expected_move + 0.0001)
        df[f'rwi_h_{i}'] = rwi_h
        
        # RWI Low
        low_move = df['high'].shift(i) - df['low']
        rwi_l = low_move / (expected_move + 0.0001)
        df[f'rwi_l_{i}'] = rwi_l
    
    # 最大値を取得
    rwi_h_cols = [f'rwi_h_{i}' for i in range(2, period + 1)]
    rwi_l_cols = [f'rwi_l_{i}' for i in range(2, period + 1)]
    
    df['rwi_high'] = df[rwi_h_cols].max(axis=1)
    df['rwi_low'] = df[rwi_l_cols].max(axis=1)
    
    # RWI差分
    df['rwi_diff'] = df['rwi_high'] - df['rwi_low']
    
    # 移動平均
    df['rwi_high_ma'] = df['rwi_high'].rolling(window=3).mean()
    df['rwi_low_ma'] = df['rwi_low'].rolling(window=3).mean()
    
    # シグナル生成
    df['rwi_high_prev'] = df['rwi_high_ma'].shift(1)
    df['rwi_low_prev'] = df['rwi_low_ma'].shift(1)
    
    df['is_buy'] = (
        (df['rwi_high_ma'] > threshold) & 
        (df['rwi_high_ma'] > df['rwi_low_ma']) &
        (df['rwi_high_prev'] <= df['rwi_low_prev'])
    ) & df['rwi_high_ma'].notna()
    
    df['is_sell'] = (
        (df['rwi_low_ma'] > threshold) & 
        (df['rwi_low_ma'] > df['rwi_high_ma']) &
        (df['rwi_low_prev'] <= df['rwi_high_prev'])
    ) & df['rwi_low_ma'].notna()
    
    # 不要カラム削除
    drop_cols = ['h_l', 'h_c', 'l_c', 'rwi_diff', 'rwi_high_ma', 'rwi_low_ma',
                 'rwi_high_prev', 'rwi_low_prev'] + rwi_h_cols + rwi_l_cols
    df.drop(drop_cols, axis=1, inplace=True, errors='ignore')
    
    return df