ビットコインでいつ買う？いつ売る？『VHS』っていう方法を調べてみたよ！

"""
Vertical Horizontal Filter Trading Signal Generator
トレンドとレンジ相場を識別する指標
"""
import pandas as pd
import numpy as np


def calculate_vhf_signals(df: pd.DataFrame,
                         period: int = 28) -> pd.DataFrame:
    """
    Vertical Horizontal Filter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: 28）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # 最高値と最安値
    df['highest'] = df['close'].rolling(window=period).max()
    df['lowest'] = df['close'].rolling(window=period).min()
    
    # 垂直距離（トレンドの強さ）
    df['vertical_distance'] = np.abs(df['highest'] - df['lowest'])
    
    # 水平距離（日々の変化の合計）
    df['daily_change'] = np.abs(df['close'] - df['close'].shift(1))
    df['horizontal_distance'] = df['daily_change'].rolling(window=period).sum()
    
    # VHF計算
    df['vhf'] = df['vertical_distance'] / (df['horizontal_distance'] + 0.0001)
    
    # VHFの移動平均
    df['vhf_ma'] = df['vhf'].rolling(window=period // 2).mean()
    
    # VHFの変化率
    df['vhf_change'] = df['vhf'] - df['vhf'].shift(1)
    
    # トレンド/レンジ判定
    df['trending'] = df['vhf'] > df['vhf_ma']
    df['ranging'] = ~df['trending']
    
    # 価格の位置
    df['price_position'] = (df['close'] - df['lowest']) / (df['vertical_distance'] + 0.0001)
    
    # シグナル生成
    df['vhf_prev'] = df['vhf'].shift(1)
    df['trending_prev'] = df['trending'].shift(1)
    df['is_buy'] = (
        (df['trending'] & (df['trending_prev'] == False)) &  # レンジからトレンドへ
        (df['price_position'] > 0.5)  # 価格が上半分
    ) & df['vhf'].notna()
    df['is_sell'] = (
        (~df['trending'] & df['trending_prev']) |  # トレンドからレンジへ
        (df['trending'] & (df['vhf_change'] < 0) & (df['price_position'] > 0.8))  # トレンド弱まり
    ) & df['vhf'].notna()
    
    # 不要カラム削除
    df.drop(['highest', 'lowest', 'vertical_distance', 'daily_change', 'horizontal_distance',
             'vhf_ma', 'vhf_change', 'trending', 'ranging', 'price_position',
             'vhf_prev', 'trending_prev'], axis=1, inplace=True, errors='ignore')
    
    return df