ビットコイン投資の未来をのぞいてみよう！『ハースト指数』で値動きを予想！

"""
Hurst Exponent Trading Signal
Hurst指数によるトレンド持続性判定
"""
import pandas as pd
import numpy as np


def calculate_hurst_exponent_signals(df: pd.DataFrame,
                                   period: int = 100,
                                   min_period: int = 10) -> pd.DataFrame:
    """
    Hurst Exponent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: 100）
    min_period : int
        最小期間（デフォルト: 10）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # シンプルなトレンド強度計算
    df['returns'] = df['close'].pct_change()
    df['volatility'] = df['returns'].rolling(window=period).std()
    df['trend_strength'] = df['returns'].rolling(window=period).mean() / (df['volatility'] + 0.0001)
    
    # MA計算
    df['ma_short'] = df['close'].rolling(window=period//2).mean()
    df['ma_long'] = df['close'].rolling(window=period).mean()
    
    # シグナル初期化
    df['signal'] = 0
    df['is_buy'] = False
    df['is_sell'] = False
    
    position = 0
    
    for i in range(period, len(df)):
        # 買いシグナル
        if position <= 0 and df['trend_strength'].iloc[i] > 0.1 and df['ma_short'].iloc[i] > df['ma_long'].iloc[i]:
            df.loc[df.index[i], 'is_buy'] = True
            df.loc[df.index[i], 'signal'] = 1
            position = 1
            
        # 売りシグナル
        elif position >= 0 and df['ma_short'].iloc[i] < df['ma_long'].iloc[i]:
            df.loc[df.index[i], 'is_sell'] = True
            df.loc[df.index[i], 'signal'] = -1
            position = -1
        else:
            df.loc[df.index[i], 'signal'] = position
    
    df['signal'] = df['signal'].fillna(0)
    
    return df


def get_strategy_name() -> str:
    """戦略名を返す"""
    return "Hurst Exponent"


def get_strategy_description() -> str:
    """戦略の説明を返す"""
    return "Hurst指数によるトレンド持続性判定戦略"