ビットコインの値段の動きを予測！みんなと逆をいく「はなれ具合作戦」

"""
Disparity Index Trading Signal
価格と移動平均の乖離率を使用
"""
import pandas as pd
import numpy as np


def calculate_disparity_index_signals(df: pd.DataFrame,
                                    period: int = 14,
                                    threshold: float = 3.0) -> pd.DataFrame:
    """
    Disparity Index戦略のシグナル生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    period : int
        移動平均期間（デフォルト: 14）
    threshold : float
        乖離率闾値（デフォルト: 3.0）
    
    Returns:
    --------
    pd.DataFrame
        シグナルが追加されたDataFrame
    """
    df = df.copy()
    
    # 移動平均
    df['ma'] = df['close'].rolling(window=period).mean()
    
    # Disparity Index
    df['disparity'] = ((df['close'] - df['ma']) / df['ma']) * 100
    
    # 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（下方乖離）
        if position <= 0 and df['disparity'].iloc[i] < -threshold / 3:
            df.loc[df.index[i], 'is_buy'] = True
            df.loc[df.index[i], 'signal'] = 1
            position = 1
            
        # 売りシグナル（上方乖離）
        elif position >= 0 and df['disparity'].iloc[i] > threshold / 3:
            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 "Disparity Index"


def get_strategy_description() -> str:
    """戦略の説明を返す"""
    return "価格と移動平均の乖離率を使用した逆張り戦略"