News-Aware Direct Reinforcement Trading for Financial Markets
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908605867884544 |
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| author | Lan, Qing-Yu Wang, Zhan-He Jiang, Jun-Qian Wang, Yu-Tong Piao, Yun-Song |
| author_facet | Lan, Qing-Yu Wang, Zhan-He Jiang, Jun-Qian Wang, Yu-Tong Piao, Yun-Song |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | News-Aware Direct Reinforcement Trading for Financial Markets Lan, Qing-Yu Wang, Zhan-He Jiang, Jun-Qian Wang, Yu-Tong Piao, Yun-Song Computational Finance Artificial Intelligence Machine Learning 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. |
| title | News-Aware Direct Reinforcement Trading for Financial Markets |
| topic | Computational Finance Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.19173 |