哈尔特贸易:为二级高频交易提供一个层次化的自适应强化学习框架
Hao Shi1, Xinting Zhang2, Desheng Wu2
1School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, China.
Chaos (Woodbury, N.Y.)
|February 18, 2026
概括
本研究介绍了使用基于注意力的元代理来进行高频交易 (HFT) 的适应性层次框架. 它提高了在波动性市场的适应能力,以显著的回报优于现有方法.
科学领域:
- 量化金融 量化金融
- 人工智能的人工智能
- 算法交易是一种算法交易.
背景情况:
- 高频交易 (HFT) 需要适应波动的市场条件的适应性策略.
- 现有的离散子代理框架由于严格的市场条件分配而表现出有限的适应性.
研究的目的:
- 提出一个新的层次框架,以基于注意力的元代理为HFT中的动态子代理协调.
- 为了提高适应能力和性能在导航不同的市场制度.
主要方法:
- 开发了一个分层框架,包含一个基于注意力的元代理.
- 利用市场嵌入和强化学习来实现最佳的亚剂重量调整.
- 实施了动态子代理分配和多头注意力机制.
主要成果:
- 拟议的框架实现了42.15%的总回报率和4.19的夏普比率在历史的HFT数据.
- 与最先进的基线相比,表现出卓越的性能.
- 废弃性研究证实了动态分配和注意力机制的有效性.
结论:
- 基于注意力的层次框架在高频交易中提供了卓越的适应性和性能.
- 通过元代理商进行次代理商的动态协调,有效地应对市场波动和过渡.
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