点々が教えてくれる！コインの売り買いタイミング作戦

#!/usr/bin/env python3
"""
パラボリックSAR戦略
SARが価格の上下に転換したらエントリー
"""
import pandas as pd
import numpy as np


def calculate_sar_signals(df: pd.DataFrame, af_start: float = 0.02, af_max: float = 0.2) -> pd.DataFrame:
    """
    パラボリックSARシグナルを生成
    
    Parameters:
    -----------
    df : pd.DataFrame
        OHLCVデータ
    af_start : float
        加速係数の初期値（デフォルト: 0.02）
    af_max : float
        加速係数の最大値（デフォルト: 0.2）
    
    Returns:
    --------
    pd.DataFrame
        シグナル列が追加されたDataFrame
    """
    df = df.copy()
    
    # パラボリックSAR計算
    df['sar'] = df['close'].copy()
    df['ep'] = df['high'].copy()  # Extreme Point
    df['af'] = af_start
    df['trend'] = 1  # 1: 上昇トレンド, -1: 下降トレンド
    
    for i in range(1, len(df)):
        prev_sar = df.loc[df.index[i-1], 'sar']
        prev_ep = df.loc[df.index[i-1], 'ep']
        prev_af = df.loc[df.index[i-1], 'af']
        prev_trend = df.loc[df.index[i-1], 'trend']
        
        # SARの更新
        if prev_trend == 1:  # 上昇トレンド
            sar = prev_sar + prev_af * (prev_ep - prev_sar)
            sar = min(sar, df.loc[df.index[i-1], 'low'], df.loc[df.index[i-2], 'low'] if i > 1 else sar)
            
            if df.loc[df.index[i], 'low'] <= sar:  # トレンド転換
                df.loc[df.index[i], 'trend'] = -1
                df.loc[df.index[i], 'sar'] = prev_ep
                df.loc[df.index[i], 'ep'] = df.loc[df.index[i], 'low']
                df.loc[df.index[i], 'af'] = af_start
            else:
                df.loc[df.index[i], 'trend'] = 1
                df.loc[df.index[i], 'sar'] = sar
                if df.loc[df.index[i], 'high'] > prev_ep:
                    df.loc[df.index[i], 'ep'] = df.loc[df.index[i], 'high']
                    df.loc[df.index[i], 'af'] = min(prev_af + af_start, af_max)
                else:
                    df.loc[df.index[i], 'ep'] = prev_ep
                    df.loc[df.index[i], 'af'] = prev_af
        else:  # 下降トレンド
            sar = prev_sar - prev_af * (prev_sar - prev_ep)
            sar = max(sar, df.loc[df.index[i-1], 'high'], df.loc[df.index[i-2], 'high'] if i > 1 else sar)
            
            if df.loc[df.index[i], 'high'] >= sar:  # トレンド転換
                df.loc[df.index[i], 'trend'] = 1
                df.loc[df.index[i], 'sar'] = prev_ep
                df.loc[df.index[i], 'ep'] = df.loc[df.index[i], 'high']
                df.loc[df.index[i], 'af'] = af_start
            else:
                df.loc[df.index[i], 'trend'] = -1
                df.loc[df.index[i], 'sar'] = sar
                if df.loc[df.index[i], 'low'] < prev_ep:
                    df.loc[df.index[i], 'ep'] = df.loc[df.index[i], 'low']
                    df.loc[df.index[i], 'af'] = min(prev_af + af_start, af_max)
                else:
                    df.loc[df.index[i], 'ep'] = prev_ep
                    df.loc[df.index[i], 'af'] = prev_af
    
    # シグナル生成
    df['is_buy'] = (df['trend'] == 1) & (df['trend'].shift(1) == -1)
    df['is_sell'] = (df['trend'] == -1) & (df['trend'].shift(1) == 1)
    
    # NaN値をFalseに置換
    df['is_buy'] = df['is_buy'].fillna(False)
    df['is_sell'] = df['is_sell'].fillna(False)
    
    print(f"パラボリックSAR: AF開始={af_start}, AF最大={af_max}")
    print(f"買いシグナル数: {df['is_buy'].sum()}")
    print(f"売りシグナル数: {df['is_sell'].sum()}")
    
    return df