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更全面地检验简单交易策略与AI交易者的表现

文章 arXiv papers · 作者: Dave Cliff et al.
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总结

这篇论文质疑了关于AI和机器学习交易智能体的已发表结论。作者指出，早期的一些评估仅依赖有限数量的测试时段和市场情景，因此证据不足以可靠判断智能体的表现。为此，作者报告称，他们利用云端并行计算，在广泛的参数范围和许多时段内进行测试，获得了更多实验结果。

根据文献，不使用AI或机器学习的简单策略，往往优于研究文献中一些公开发表的AI交易者或机器学习交易者。文献提出的方法论启示是：比较需要覆盖广泛参数和市场条件的充分评估，复杂模型也应与简单基准策略比较。摘录没有说明策略规则、市场、绩效指标或具体的AI智能体，因此仅凭本文无法独立评估其发现，也无法将其转化为可部署的交易系统。报告结果仅涉及经过测试的研究智能体及其设置；这并不能证明简单策略在实盘市场中普遍优于基于AI的方法。

核心观点

  • 论文质疑早期对AI交易智能体的评估是否测试了足够多的时段和市场情景。
  • 作者报告称，他们利用并行计算，在广泛的参数范围内进行了大量测试。
  • 在报告的测试中，一些不使用AI的简单策略优于已发表的AI和机器学习交易策略。
  • 简单基准策略有助于判断复杂交易方法是否带来额外价值。
  • 摘录未提供策略细节、市场、指标及评估实盘表现所需的证据。

标签

机器学习回测统计学
全文
# Methods Matter: A Trading Agent with No Intelligence Routinely Outperforms AI-Based Traders


# Methods Matter: A Trading Agent with No Intelligence Routinely Outperforms AI-Based Traders









There's a long tradition of research using computational intelligence (methods from artificial intelligence (AI) and machine learning (ML)), to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets, with a sequence of research papers on this topic published at AI conferences such as IJCAI and in journals such as Artificial Intelligence: we show here that this strand of research has taken a number of methodological mis-steps and that actually some of the reportedly best-performing public-domain AI/ML trading strategies can routinely be out-performed by extremely simple trading strategies that involve no AI or ML at all. The results that we highlight here could easily have been revealed at the time that the relevant key papers were published, more than a decade ago, but the accepted methodology at the time of those publications involved a somewhat minimal approach to experimental evaluation of trader-agents, making claims on the basis of a few thousand test-sessions of the trader-agent in a small number of market scenarios. In this paper we present results from exhaustive testing over wide ranges of parameter values, using parallel cloud-computing facilities, where we conduct millions of tests and thereby create much richer data from which firmer conclusions can be drawn. We show that the best public-domain AI/ML traders in the published literature can be routinely outperformed by a "sub-zero-intelligence" trading strategy that at face value appears to be so simple as to be financially ruinous, but which interacts with the market in such a way that in practice it is more profitable than the well-known AI/ML strategies from the research literature. That such a simple strategy can outperform established AI/ML-based strategies is a sign that perhaps the AI/ML trading strategies were good answers to the wrong question.

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。