Large language models (LLMs) have shown strong reasoning capabilities and are increasingly explored for financial trading. Existing LLM-based trading agents, however, largely focus on single-step prediction and lack integrated mechanisms for risk management, which reduces their effectiveness in volatile markets. We introduce FinRS, a risk-sensitive trading framework that combines hierarchical market analysis, dual-decision agents, and multi-timescale reward reflection to align trading actions with both return objectives and downside risk constraints. Experiments on multiple stocks and market conditions show that FinRS achieves superior profitability and stability compared to state-of-the-art methods.
翻译：大语言模型（LLMs）已展现出强大的推理能力，并越来越多地被探索用于金融交易。然而，现有的基于LLM的交易代理主要侧重于单步预测，缺乏综合的风险管理机制，这降低了它们在波动市场中的有效性。我们提出了FinRS，一种风险敏感的交易框架，它结合了分层市场分析、双决策代理和多时间尺度奖励反馈，以使交易行为与收益目标和下行风险约束相一致。在多种股票和市场条件下的实验表明，与最先进的方法相比，FinRS实现了更优的盈利能力和稳定性。
相关内容
微信扫码咨询专知VIP会员