具有市场趋势动态的深度势头网络
1Graduate School of Data Science, Chonnam National University, Gwangju, Republic of Korea.
PloS one
|September 2, 2025
概括
这项研究通过整合市场趋势数据来增强时间序列势头 (TSMOM) 交易策略. 使用长期短期记忆 (LSTM) 和MTDP评分的改进模型在市场转变期间表现更好.
科学领域:
- 数量金融
- 金融中的机器学习
- 算法交易
背景情况:
- 时间序列势头 (TSMOM) 策略依赖于回报趋势的持续性.
- 长期短期记忆 (LSTM) 网络可以改善TSMOM,但难以应对市场趋势的变化.
研究的目的:
- 在市场趋势发生重大变化时提高TSMOM的表现.
- 开发一个包含市场动态的模型来改进交易决策.
主要方法:
- 通过监督学习将短期和长期信号结合为市场状态表示.
- 通过极端梯度提升 (XGBoost) 来生成市场趋势动态预测 (MTDP) 评分.
- 在基于LSTM的交易策略中应用的MTDP得分.
主要成果:
- 在99个期货 (1995-2021) 上进行后期测试显示,MTDP得分整合改善了夏普比率.
- 在稳定的时期 (1995-2019年) 超过了8周的势头窗口.
- 在20周的窗口中,在极端衰退 (例如COVID-19) 期间,表现优越,恢复速度更快.
结论:
- 整合市场状况信息 (MTDP分数) 有效地增强了TSMOM策略.
- 动态调整势头的回顾窗口对于波动性市场的稳定利至关重要.
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