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.

翻译：配对交易是一种最有效的统计套利策略之一，通过对选定的两种资产进行对冲来获取中性收益。现有方法通常将该任务分解为两个独立步骤：配对选择与交易。然而，这两个密切相关的子任务相互分离会阻碍信息传递，导致整体性能受限。在配对选择中，忽略交易性能会导致选择价格波动不相关的错误资产，而针对交易训练的智能体可能过度拟合已选资产，无法利用其他资产的历史信息。为解决这一问题，本文提出一种自动配对交易范式，将其视为统一任务而非两阶段流程。我们设计了分层强化学习框架，联合学习并优化两个子任务：高层策略从所有可能的组合中选择两种资产，低层策略则执行一系列交易操作。基于真实股票数据的实验结果表明，与现有的配对选择和交易方法相比，我们的方法在配对交易中具有显著有效性。