用大模型生成交易策略，指导强化学习提升收益与风控。
Language Model Guided Reinforcement Learning in Quantitative Trading
- 大模型生成高层交易策略，引导强化学习决策。
- 相比标准强化学习，夏普比率提升，最大回撤降低。
- 适合量化交易研究者与金融AI开发者参考。
算法交易需要在短期战术决策与长期财务目标之间保持一致。强化学习（RL）已被应用于此类问题，但其应用受限于短视行为和策略不透明。大型语言模型（LLMs）通过结构化提示，可提供互补的战略推理和多模态信号解读能力。本文提出一种混合框架，由LLMs生成高层交易策略以指导RL代理。我们评估了（i）专家对LLM生成策略的经济合理性，以及（ii）LLM引导代理相对于无引导RL基线的性能，使用夏普比率（SR）和最大回撤（MDD）作为指标。实证结果表明，相较于标准强化学习，LLM引导显著改善了收益与风险指标。
原文摘要 · Abstract (English)
Algorithmic trading requires short-term tactical decisions consistent with long-term financial objectives. Reinforcement Learning (RL) has been applied to such problems, but adoption is limited by myopic behaviour and opaque policies. Large Language Models (LLMs) offer complementary strategic reasoning and multi-modal signal interpretation when guided by well-structured prompts. This paper proposes a hybrid framework in which LLMs generate high-level trading strategies to guide RL agents. We evaluate (i) the economic rationale of LLM-generated strategies through expert review, and (ii) the performance of LLM-guided agents against unguided RL baselines using Sharpe Ratio (SR) and Maximum Drawdown (MDD). Empirical results indicate that LLM guidance improves both return and risk metrics relative to standard RL.
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