Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning

Yijia Xiao; Edward Sun; Tong Chen; Fang Wu; Di Luo; Wei Wang

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arXiv · 2509.11420

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning

Yijia Xiao·Edward Sun·Tong Chen·Fang Wu·Di Luo·Wei Wang

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Abstract

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.

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q-fin.TR cs.AI cs.CE cs.CL cs.LG

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BibTeX RIS

Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang. 2025-09-14. Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning. https://arxiv.org/abs/2509.11420

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