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将深度强化学习用于均值回归交易

文章 arXiv papers · 作者: Sophia Gu
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总结

本文介绍将深度强化学习用于均值回归交易问题。其目标是让实践者更容易使用近期的强化学习方法，否则他们可能需要在众多方法中作出选择，或从经典基础开始构建智能体。文中演示使用一个最初为策略游戏开发的强化学习库，并将其用于金融决策。

该框架还纳入了具有经济学动机的函数性质，目标是得到收敛且表现出色的解。不过，本文没有给出具体模型架构、奖励设计、交易规则、数据、基准或数值结果。因此，文中概述的是方法及其动机，证据不足以评估盈利能力、稳健性或相对于其他方法的表现。本段中关于收敛性和表现的主张没有实验细节支持。

核心观点

  • 这项工作将深度强化学习用于均值回归交易。
  • 该方法演示了如何将最初为策略游戏开发的库用于交易问题。
  • 该框架将具有经济学动机的函数性质纳入学习方法。
  • 本文声称该方法可得到收敛且表现出色的解，但此处未提供实验细节或结果。
  • 根据所提供的信息，无法评估盈利能力和稳健性。

标签

机器学习均值回归加密资产回测
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# Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies


# Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies









Over the past decades, researchers have been pushing the limits of Deep Reinforcement Learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora of available methodologies that are seemingly alike, while others are still building RL agents from scratch based on classical theories. To address the aforementioned gaps in adopting the latest DRL methods, I am particularly interested in testing out if any of the recent technology developed by the leads in the field can be readily applied to a class of optimal trading problems. Unsurprisingly, many prominent breakthroughs in DRL are investigated and tested on strategic games: from AlphaGo to AlphaStar and at about the same time, OpenAI Five. Thus, in this writing, I want to show precisely how to use a DRL library that is initially built for games in a fundamental trading problem; mean reversion. And by introducing a framework that incorporates economically-motivated function properties, I also demonstrate, through the library, a highly-performant and convergent DRL solution to decision-making financial problems in general.

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。