PRUDEX-Compass：强化学习在金融市场中的系统评估
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets
摘要：
涉及90多万亿美元市场资金的金融市场吸引着全球无数投资者的目光。最近，强化学习在金融市场（FinRL）已成为一个有前途的方向，训练代理作出有利可图的投资决策。然而，大多数FinRL方法的评估仅关注与利润相关的指标，而忽略了许多关键轴，这对于金融从业者将这些方法部署到现实世界的金融市场中来说远远不能令人满意。因此，我们推出PRUDEX-Compass，它有6个轴，即，盈利性、风险控制、普遍性、多样性、可靠性和可解释性，共17项指标进行系统评价。具体来说，i）我们建议AlphaMix+作为一个强大的FinRL基准，它利用专家混合（MoE）和风险敏感方法来做出多样化的风险意识投资决策，ii）我们在4个有影响力的金融市场的长期真实数据集中评估了8种FinRL方法，以展示我们的PRUDEX-Compass的使用，iii）PRUDEX-Compass与4个真实数据集，8种FinRL方法的标准实现和投资组合管理环境作为公共资源发布，以促进新FinRL方法的设计和比较。我们希望PRUDEX-Compass不仅可以为未来的FinRL研究提供指导，以防止不可信的结果阻碍FinRL进入成功的行业部署，还可以为强化学习（RL）社区提供一个新的具有挑战性的算法评估场景。
英文摘要：
The financial markets, which involve more than $90 trillion market capitals, attract the attention of innumerable investors around the world. Recently, reinforcement learning in financial markets (FinRL) has emerged as a promising direction to train agents for making profitable investment decisions. However, the evaluation of most FinRL methods only focuses on profit-related measures and ignores many critical axes, which are far from satisfactory for financial practitioners to deploy these methods into real-world financial markets. Therefore, we introduce PRUDEX-Compass, which has 6 axes, i.e., Profitability, Risk-control, Universality, Diversity, rEliability, and eXplainability, with a total of 17 measures for a systematic evaluation. Specifically, i) we propose AlphaMix+ as a strong FinRL baseline, which leverages mixture-of-experts (MoE) and risk-sensitive approaches to make diversified risk-aware investment decisions, ii) we evaluate 8 FinRL methods in 4 long-term real-world datasets of influential financial markets to demonstrate the usage of our PRUDEX-Compass, iii) PRUDEX-Compass together with 4 real-world datasets, standard implementation of 8 FinRL methods and a portfolio management environment is released as public resources to facilitate the design and comparison of new FinRL methods. We hope that PRUDEX-Compass can not only shed light on future FinRL research to prevent untrustworthy results from stagnating FinRL into successful industry deployment but also provide a new challenging algorithm evaluation scenario for the reinforcement learning (RL) community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
