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

2025-09-14 · Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang arxiv

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.

📄 PDF Abstract BibTeX arXiv:2509.11420

Code (0)

등록된 구현이 없습니다.

Tasks

Reinforcement Learning Decision Making

FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading

2025-02-17 · Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao 외

Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios i…

Decision Making parameter-efficient fine-tuning Reinforcement Learning (RL)

A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist

2024-02-28 · Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun 외

Financial trading is a crucial component of the markets, informed by a multimodal information landscape encompassing news, prices, and Kline charts, and encompasses diverse tasks such as quantitative trading and high-fre…

Quantitative Trading using Deep Q Learning

2023-04-03 · Soumyadip Sarkar

Reinforcement learning (RL) is a subfield of machine learning that has been used in many fields, such as robotics, gaming, and autonomous systems. There has been growing interest in using RL for quantitative trading, whe…

Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents

2025-02-11 · Mostapha Benhenda

This paper presents a novel risk-sensitive trading agent combining reinforcement learning and large language models (LLMs). We extend the Conditional Value-at-Risk Proximal Policy Optimization (CPPO) algorithm, by adding…

Financial News-Driven LLM Reinforcement Learning for Portfolio Management

2024-11-17 · Ananya Unnikrishnan

Reinforcement learning (RL) has emerged as a transformative approach for financial trading, enabling dynamic strategy optimization in complex markets. This study explores the integration of sentiment analysis, derived fr…

Decision Making Management reinforcement-learning Reinforcement Learning+2