TradingAgents: Multi-Agents LLM Financial Trading Framework
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
Significant progress has been made in automated problem-solving using societies of agents powered by large language models (LLMs). In finance, efforts have largely focused on single-agent systems handling specific tasks or multi-agent frameworks independently gathering data. However, the multi-agent systems' potential to replicate real-world trading firms' collaborative dynamics remains underexplored. TradingAgents proposes a novel stock trading framework inspired by trading firms, featuring LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles. The framework includes Bull and Bear researcher agents assessing market conditions, a risk management team monitoring exposure, and traders synthesizing insights from debates and historical data to make informed decisions. By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance. Detailed architecture and extensive experiments reveal its superiority over baseline models, with notable improvements in cumulative returns, Sharpe ratio, and maximum drawdown, highlighting the potential of multi-agent LLM frameworks in financial trading. TradingAgents is available at https://github.com/TauricResearch/TradingAgents.
Key facts. Cited by 188 papers in the map (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 26 of its 72 references that are in the map. First posted on arXiv 28 Dec 2024.
Builds on
26 of 72 references.
Closest ancestors · 4
Same direction, newest first
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- 2023FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design23 Nov
Other references · 22
- 2017Proximal Policy Optimization Algorithmscited by 15,215
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- 2023Auto-GPT for Online Decision Making: Benchmarks and Additional Opinionscited by 255
- 2024Teaching Large Language Models to Reason with Reinforcement Learningcited by 160
- 2024A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalistcited by 137
- 2023Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Modelscited by 129
- 2023XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameterscited by 92
- 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
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- 2023Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investmentcited by 56
- 2024Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Modelscited by 36
- 2024LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Predictioncited by 33
- 2024QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Modelcited by 30
- 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectioncited by 29
- 2024On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizabilitycited by 18
- 2023Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Predictioncited by 17
- 2023Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?cited by 15
- 2024Sentiment trading with large language modelscited by 7
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