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

Author

Listed:
  • Yijia Xiao
  • Edward Sun
  • Tong Chen
  • Fang Wu
  • Di Luo
  • Wei Wang

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.

Suggested Citation

  • Yijia Xiao & Edward Sun & Tong Chen & Fang Wu & Di Luo & Wei Wang, 2025. "Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning," Papers 2509.11420, arXiv.org.
    • Handle: RePEc:arx:papers:2509.11420
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    References listed on IDEAS

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    4. repec:osf:osfxxx:ahkd3_v1 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Rui Sun & Yifan Sun & Sheng Xu & Li Zhao & Jing Li & Daxin Jiang & Cheng Hua & Zuo Bai, 2026. "Trade-R1: Bridging Verifiable Rewards to Stochastic Environments via Process-Level Reasoning Verification," Papers 2601.03948, arXiv.org, revised Jan 2026.
    2. Haofei Yu & Fenghai Li & Jiaxuan You, 2025. "LiveTradeBench: Seeking Real-World Alpha with Large Language Models," Papers 2511.03628, arXiv.org.
    3. Zheng Li, 2026. "Design and Empirical Study of a Large Language Model-Based Multi-Agent Investment System for Chinese Public REITs," Papers 2602.00082, arXiv.org.
    4. Zuoyou Jiang & Li Zhao & Rui Sun & Ruohan Sun & Zhongjian Li & Jing Li & Daxin Jiang & Zuo Bai & Cheng Hua, 2025. "Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning," Papers 2512.23515, arXiv.org.
    5. Zhi Yang & Lingfeng Zeng & Fangqi Lou & Qi Qi & Wei Zhang & Zhenyu Wu & Zhenxiong Yu & Jun Han & Zhiheng Jin & Lejie Zhang & Xiaoming Huang & Xiaolong Liang & Zheng Wei & Junbo Zou & Dongpo Cheng & Zh, 2026. "UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos," Papers 2601.22162, arXiv.org.

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