|
阅读：82回复：0
均值回归策略（震荡市）
均值回归策略（震荡市）
核心逻辑：价格偏离均值后会回归，高胜率（60%-70%）但盈亏比低，适合震荡行情。 用布林带判断偏离程度 超买做空，超卖做多，固定止盈止损 import pandas as pd import numpy as np import talib def mean_reversion_strategy(df, bb_period=20, bb_dev=2, sl_pct=0.01, tp_pct=0.005): # 计算布林带 df['bb_mid'] = talib.MA(df['close'], timeperiod=bb_period) df['bb_upper'], df['bb_mid'], df['bb_lower'] = talib.BBANDS( df['close'], timeperiod=bb_period, nbdevup=bb_dev, nbdevdn=bb_dev ) # 初始化信号和仓位 df['signal'] = 0 df['position'] = 0 df['stop_loss'] = 0 df['take_profit'] = 0 df['pnl'] = 0 # 遍历计算信号 for i in range(bb_period, len(df)): current_close = df.iloc['close'] prev_position = df.iloc[i-1]['position'] # 超卖做多：价格低于下轨且无仓位 if current_close < df.iloc['bb_lower'] and prev_position == 0: df.iloc[i, df.columns.get_loc('signal')] = 1 df.iloc[i, df.columns.get_loc('position')] = 1 df.iloc[i, df.columns.get_loc('stop_loss')] = current_close * (1 - sl_pct) df.iloc[i, df.columns.get_loc('take_profit')] = current_close * (1 + tp_pct) # 超买做空：价格高于上轨且无仓位 elif current_close > df.iloc['bb_upper'] and prev_position == 0: df.iloc[i, df.columns.get_loc('signal')] = -1 df.iloc[i, df.columns.get_loc('position')] = -1 df.iloc[i, df.columns.get_loc('stop_loss')] = current_close * (1 + sl_pct) df.iloc[i, df.columns.get_loc('take_profit')] = current_close * (1 - tp_pct) # 止盈止损平仓 elif prev_position == 1: if current_close <= df.iloc[i-1]['stop_loss'] or current_close >= df.iloc[i-1]['take_profit']: df.iloc[i, df.columns.get_loc('signal')] = -1 df.iloc[i, df.columns.get_loc('position')] = 0 else: df.iloc[i, df.columns.get_loc('position')] = 1 elif prev_position == -1: if current_close >= df.iloc[i-1]['stop_loss'] or current_close <= df.iloc[i-1]['take_profit']: df.iloc[i, df.columns.get_loc('signal')] = 1 df.iloc[i, df.columns.get_loc('position')] = 0 else: df.iloc[i, df.columns.get_loc('position')] = -1 # 计算盈亏 if prev_position != 0: price_change = df.iloc['close'] - df.iloc[i-1]['close'] df.iloc[i, df.columns.get_loc('pnl')] = prev_position * price_change * 10 df['cum_pnl'] = df['pnl'].cumsum() return df # 回测示例 if __name__ == "__main__": df = load_futures_data('futures_data.csv') # 复用之前的加载函数 result = mean_reversion_strategy(df) total_trades = len(result[result['signal'] != 0]) // 2 win_trades = len(result[result['pnl'] > 0]) win_rate = win_trades / total_trades if total_trades > 0 else 0 total_pnl = result['cum_pnl'].iloc[-1] print(f"均值回归策略 - 总交易次数：{total_trades}") print(f"均值回归策略 - 胜率：{win_rate:.2%}") print(f"均值回归策略 - 累计盈亏：{total_pnl:.2f}元") |
|
|
Powered by phpwind v9.0.1 ©2001-2111 malhj.com 桂ICP备2023008533号-1
本站导航链接目的在于免费传播更多信息，版权归原站点或原作者所有，所有链接来源于网络，对于内容的真实性、完整性、及时性本站不作出任何保证或承诺，请自行核实相关内容。本站的所有链接，只是为了方便自己使用，本站对所有流媒体均链接至互联网，本站不制作、编辑、集成、上传或存储到服务器.对任何因直接或间接使用本网站及子站搜索而造成的损失，包括但不限于因有关内容不准确、不完整而导致的损失，本站不承担任何法律责任。
除本人原创外：如链接或者内容有侵权问题，请及时联系我们，我们将尽快处理。
