ビットコインの値段の動きをチェック！「ボラティリティ・レシオ」で売り買いのチャンスを見つけよう

"""
Volatility Ratio Trading Signal Generator
現在のボラティリティと過去のボラティリティを比較
"""
import pandas as pd
import numpy as np


def calculate_volatility_ratio_signals(df: pd.DataFrame,
                                      period: int = 14,
                                      threshold: float = 0.5) -> pd.DataFrame:
    """
    Volatility Ratio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: 0.5）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # True Range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)
    
    # 現在のTrue Range
    df['current_tr'] = df['true_range']
    
    # 過去のTrue Rangeの最大値
    df['max_tr'] = df['true_range'].rolling(window=period).max()
    
    # Volatility Ratio
    df['vr'] = df['current_tr'] / (df['max_tr'] + 0.0001)
    
    # VRの移動平均
    df['vr_ma'] = df['vr'].rolling(window=period).mean()
    
    # ボラティリティブレイクアウト検出
    df['vr_high'] = df['vr'] > (1 + threshold)
    df['vr_low'] = df['vr'] < threshold
    
    # 価格のトレンド
    df['price_ma'] = df['close'].rolling(window=period).mean()
    df['price_trend'] = np.where(df['close'] > df['price_ma'], 1, -1)
    
    # エクスパンション・コントラクション
    df['vr_expanding'] = df['vr'] > df['vr_ma']
    df['vr_contracting'] = df['vr'] < df['vr_ma']
    
    # シグナル生成
    df['vr_prev'] = df['vr'].shift(1)
    df['vr_expanding_prev'] = df['vr_expanding'].shift(1)
    df['is_buy'] = (
        (df['vr_expanding'] & (df['vr_expanding_prev'] == False) & (df['price_trend'] > 0)) |
        (df['vr_low'] & (df['vr'] > df['vr_prev']))
    ) & df['vr'].notna()
    df['is_sell'] = (
        (df['vr_expanding'] & (df['vr_expanding_prev'] == False) & (df['price_trend'] < 0)) |
        (df['vr_high'] & (df['vr'] < df['vr_prev']))
    ) & df['vr'].notna()
    
    # 不要カラム削除
    df.drop(['h_l', 'h_c', 'l_c', 'current_tr', 'max_tr', 'vr_ma', 'vr_high', 'vr_low',
             'price_ma', 'price_trend', 'vr_expanding', 'vr_contracting', 
             'vr_prev', 'vr_expanding_prev'], axis=1, inplace=True, errors='ignore')
    
    return df