用GPU并行模拟提升股市与加密货币交易的强化学习模型效率与稳定性。
Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024
- 通过单块GPU并行2048个环境，采样速度提升1746倍。
- 集成模型最大回撤降低4.17%，夏普比率提升0.21，表现优于单一模型。
- 适合关注金融交易强化学习高效训练与鲁棒性优化的研究者。
强化学习在金融任务中展现出巨大潜力，但面临策略不稳和采样瓶颈两大挑战。本文通过在图形处理器（GPU）上进行大规模并行模拟，重访集成方法，显著提升了模型在波动性金融市场中的计算效率与鲁棒性。利用GPU的并行处理能力，大幅提高集成模型训练时的采样速度。集成模型融合多个智能体的优势，增强金融决策策略的稳健性。我们在股票与加密货币交易任务中验证了该方法的有效性：单块GPU上使用2048个并行环境，采样速度相比单环境提升最高达1746倍；集成模型实现更高累计收益，最大回撤降低最多4.17%，夏普比率提升最高0.21，优于部分独立智能体。本文还介绍了ACM ICAIF FinRL竞赛2023-2024年的交易任务。
原文摘要 · Abstract (English)
Reinforcement learning has demonstrated great potential for performing financial tasks. However, it faces two major challenges: policy instability and sampling bottlenecks. In this paper, we revisit ensemble methods with massively parallel simulations on graphics processing units (GPUs), significantly enhancing the computational efficiency and robustness of trained models in volatile financial markets. Our approach leverages the parallel processing capability of GPUs to significantly improve the sampling speed for training ensemble models. The ensemble models combine the strengths of component agents to improve the robustness of financial decision-making strategies. We conduct experiments in both stock and cryptocurrency trading tasks to evaluate the effectiveness of our approach. Massively parallel simulation on a single GPU improves the sampling speed by up to $1,746\times$ using $2,048$ parallel environments compared to a single environment. The ensemble models have high cumulative returns and outperform some individual agents, reducing maximum drawdown by up to $4.17\%$ and improving the Sharpe ratio by up to $0.21$. This paper describes trading tasks at ACM ICAIF FinRL Contests in 2023 and 2024.
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