Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Xiao, Yijia Sun, Edward Chen, Tong Wu, Fang Luo, Di Wang, Wei |
| author_facet | Xiao, Yijia Sun, Edward Chen, Tong Wu, Fang Luo, Di Wang, Wei |
| contents | Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. We present Trading-R1, a financially-aware model that incorporates strategic thinking and planning for comprehensive thesis composition, facts-grounded analysis, and volatility-adjusted decision making. Trading-R1 aligns reasoning with trading principles through supervised fine-tuning and reinforcement learning with a three-stage easy-to-hard curriculum. Training uses Tauric-TR1-DB, a 100k-sample corpus spanning 18 months, 14 equities, and five heterogeneous financial data sources. Evaluated on six major equities and ETFs, Trading-R1 demonstrates improved risk-adjusted returns and lower drawdowns compared to both open-source and proprietary instruction-following models as well as reasoning models. The system generates structured, evidence-based investment theses that support disciplined and interpretable trading decisions. Trading-R1 Terminal will be released at https://github.com/TauricResearch/Trading-R1. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11420 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning Xiao, Yijia Sun, Edward Chen, Tong Wu, Fang Luo, Di Wang, Wei Trading and Market Microstructure Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. We present Trading-R1, a financially-aware model that incorporates strategic thinking and planning for comprehensive thesis composition, facts-grounded analysis, and volatility-adjusted decision making. Trading-R1 aligns reasoning with trading principles through supervised fine-tuning and reinforcement learning with a three-stage easy-to-hard curriculum. Training uses Tauric-TR1-DB, a 100k-sample corpus spanning 18 months, 14 equities, and five heterogeneous financial data sources. Evaluated on six major equities and ETFs, Trading-R1 demonstrates improved risk-adjusted returns and lower drawdowns compared to both open-source and proprietary instruction-following models as well as reasoning models. The system generates structured, evidence-based investment theses that support disciplined and interpretable trading decisions. Trading-R1 Terminal will be released at https://github.com/TauricResearch/Trading-R1. |
| title | Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning |
| topic | Trading and Market Microstructure Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.11420 |