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基于分层强化学习的配对选择与交易联合优化

文章 arXiv papers · 作者: Weiguang Han et al.
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总结

本文将配对交易视为一个联合决策问题，而非先选择资产对再进行交易的流程。作者认为，将这两个阶段分开可能会丢失有用信息：选择阶段可能忽视某个资产对是否适合有效交易，而交易智能体可能对选定资产过拟合，无法从更广泛的资产集合中学习。

其分层强化学习方法将资产对选择交给高层策略，将交易操作交给低层策略。两个策略联合优化，使选择与执行决策能够相互影响。作者报告称，基于真实股票数据的实验显示，与现有选择和交易方法相比，该方法提高了配对交易效果。本文未提供具体表现数据或详细实验局限，因此无法据此确定该方法能否推广至其他市场、资产类别或交易成本环境。

核心观点

  • 资产对选择和交易执行可以作为一个任务中相互关联的部分来学习。
  • 高层策略选择资产对，低层策略作出交易决策。
  • 联合学习可以将交易表现信息反馈给资产对选择。
  • 该研究报告了基于真实股票数据与现有方法的比较。
  • 所提供的说明未给出详细表现数据，也未提供股票实验以外范围的证据。

标签

配对交易股票机器学习套利统计学
全文
# Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning


# Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning









Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。