用大模型生成的新闻情感直接训练交易策略，不依赖人工规则。
News-Aware Direct Reinforcement Trading for Financial Markets
- 直接使用LLM生成的新闻情感+原始价格量价数据作为强化学习输入
- 在加密货币市场中，性能超越市场基准，且不依赖手工特征
- 强调时序信息对交易决策的关键作用，适合量化交易研究者
金融市场对新闻高度敏感，如何有效融合新闻数据仍是重要挑战。现有方法多依赖人工设计规则或手工特征。本文直接使用大语言模型生成的新闻情感分数，结合原始价格与成交量数据，作为强化学习的可观测输入。通过循环神经网络或Transformer等序列模型，实现端到端的交易决策。以加密货币市场为例，实验评估了双深度Q网络（DDQN）和组相对策略优化（GRPO）两种典型强化学习算法。结果表明，该无需手工特征或人工规则的新闻感知方法，性能优于市场基准。研究进一步凸显时序信息在此过程中的关键作用。
原文摘要 · Abstract (English)
The financial market is known to be highly sensitive to news. Therefore, effectively incorporating news data into quantitative trading remains an important challenge. Existing approaches typically rely on manually designed rules and/or handcrafted features. In this work, we directly use the news sentiment scores derived from large language models, together with raw price and volume data, as observable inputs for reinforcement learning. These inputs are processed by sequence models such as recurrent neural networks or Transformers to make end-to-end trading decisions. We conduct experiments using the cryptocurrency market as an example and evaluate two representative reinforcement learning algorithms, namely Double Deep Q-Network (DDQN) and Group Relative Policy Optimization (GRPO). The results demonstrate that our news-aware approach, which does not depend on handcrafted features or manually designed rules, can achieve performance superior to market benchmarks. We further highlight the critical role of time-series information in this process.
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