News-Aware Direct Reinforcement Trading for Financial Markets
This addresses the problem of automating news integration in financial trading for investors, though it is incremental as it applies existing reinforcement learning methods to a new data combination.
The paper tackled the challenge of incorporating news data into quantitative trading by using news sentiment scores from large language models with raw price and volume data as inputs for reinforcement learning, achieving performance superior to market benchmarks in cryptocurrency trading experiments.
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.