{"ID":2871452,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11420","arxiv_id":"2509.11420","title":"Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning","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.","short_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 discipli...","url_abs":"https://arxiv.org/abs/2509.11420","url_pdf":"https://arxiv.org/pdf/2509.11420v1","authors":"[\"Yijia Xiao\",\"Edward Sun\",\"Tong Chen\",\"Fang Wu\",\"Di Luo\",\"Wei Wang\"]","published":"2025-09-14T20:13:41Z","proceeding":"q-fin.TR","tasks":"[\"q-fin.TR\",\"cs.AI\",\"cs.CE\",\"cs.CL\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\"]","has_code":false,"code_links":[{"ID":609859,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2871452,"paper_url":"https://arxiv.org/abs/2509.11420","paper_title":"Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning","repo_url":"https://github.com/TauricResearch/Trading-R1","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}