SAR抛物线指标策略–量化实战(附Python代码)

import pandas as pdimport numpy as npimport matplotlib.pyplot as plt
def calculate_sar(df, initial_af=0.02, af_increment=0.02, max_af=0.2):    df['SAR'] = np.nan  # 初始化SAR列    df['SAR'][0] = df['Low'][0]  # 第一个SAR值设为第一个周期的最低价    df['EP'] = np.nan  # 初始化极值点列    df['EP'][0] = df['High'][0]  # 第一个极值点设为第一个周期的最高价    af = initial_af  # 初始化加速因子    trend = None  # 初始趋势
    for i in range(1, len(df)):        if trend is None and df['High'][i] > df['EP'][i-1]:            trend = 'up'            af = initial_af            df['EP'][i] = df['High'][i]        elif trend is None and df['Low'][i] < df['SAR'][i-1]:            trend = 'down'            af = initial_af            df['EP'][i] = df['Low'][i]                if trend == 'up':            df['SAR'][i] = df['SAR'][i-1] + af * (df['EP'][i-1] - df['SAR'][i-1])            if df['Low'][i] < df['SAR'][i]:                trend = 'down'                af = initial_af                df['EP'][i] = df['Low'][i]        elif trend == 'down':            df['SAR'][i] = df['SAR'][i-1] - af * (df['SAR'][i-1] - df['EP'][i-1])            if df['High'][i] > df['SAR'][i]:                trend = 'up'                af = initial_af                df['EP'][i] = df['High'][i]                af = min(max_af, af + af_increment)  # 更新加速因子
    return df['SAR']
# 假设df是包含股票价格的DataFramedf['SAR'] = calculate_sar(df)
# 生成交易信号df['Signal'] = np.where(df['Close'].shift(1) < df['SAR'].shift(1), -1, 0)df['Signal'] = np.where(df['Signal'].shift(-1) == -1 and df['Close'] > df['SAR'], -df['Signal'].shift(-1), df['Signal'])df['Signal'] = np.where(df['Close'].shift(1) > df['SAR'].shift(1), 1, df['Signal'])
# 绘制SAR指标和交易信号plt.figure(figsize=(14, 7))plt.plot(df['Close'], label='Close Price')plt.plot(df['SAR'], label='SAR', linestyle='--')plt.plot(df[df['Signal'] == 1].index, df['Close'][df['Signal'] == 1], '^', markersize=10, color='green', label='Buy Signal')plt.plot(df[df['Signal'] == -1].index, df['Close'][df['Signal'] == -1], 'v', markersize=10, color='red', label='Sell Signal')plt.legend()plt.show()