ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915448612716544 |
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| author | Zhao, Li Sun, Rui Jiang, Zuoyou Yang, Bo Bai, Yuxiao Chen, Mengting Wang, Xinyang Li, Jing Bai, Zuo |
| author_facet | Zhao, Li Sun, Rui Jiang, Zuoyou Yang, Bo Bai, Yuxiao Chen, Mengting Wang, Xinyang Li, Jing Bai, Zuo |
| contents | In financial trading, large language model (LLM)-based agents demonstrate significant potential. However, the high sensitivity to market noise undermines the performance of LLM-based trading systems. To address this limitation, we propose a novel multi-agent system featuring an internal competitive mechanism inspired by modern corporate management structures. The system consists of two specialized teams: (1) Data Team - responsible for processing and condensing massive market data into diversified text factors, ensuring they fit the model's constrained context. (2) Research Team - tasked with making parallelized multipath trading decisions based on deep research methods. The core innovation lies in implementing a real-time evaluation and ranking mechanism within each team, driven by authentic market feedback. Each agent's performance undergoes continuous scoring and ranking, with only outputs from top-performing agents being adopted. The design enables the system to adaptively adjust to dynamic environment, enhances robustness against market noise and ultimately delivers superior trading performance. Experimental results demonstrate that our proposed system significantly outperforms prevailing multi-agent systems and traditional quantitative investment methods across diverse evaluation metrics. ContestTrade is open-sourced on GitHub at https://github.com/FinStep-AI/ContestTrade. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00554 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism Zhao, Li Sun, Rui Jiang, Zuoyou Yang, Bo Bai, Yuxiao Chen, Mengting Wang, Xinyang Li, Jing Bai, Zuo Trading and Market Microstructure Computation and Language Computational Finance In financial trading, large language model (LLM)-based agents demonstrate significant potential. However, the high sensitivity to market noise undermines the performance of LLM-based trading systems. To address this limitation, we propose a novel multi-agent system featuring an internal competitive mechanism inspired by modern corporate management structures. The system consists of two specialized teams: (1) Data Team - responsible for processing and condensing massive market data into diversified text factors, ensuring they fit the model's constrained context. (2) Research Team - tasked with making parallelized multipath trading decisions based on deep research methods. The core innovation lies in implementing a real-time evaluation and ranking mechanism within each team, driven by authentic market feedback. Each agent's performance undergoes continuous scoring and ranking, with only outputs from top-performing agents being adopted. The design enables the system to adaptively adjust to dynamic environment, enhances robustness against market noise and ultimately delivers superior trading performance. Experimental results demonstrate that our proposed system significantly outperforms prevailing multi-agent systems and traditional quantitative investment methods across diverse evaluation metrics. ContestTrade is open-sourced on GitHub at https://github.com/FinStep-AI/ContestTrade. |
| title | ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism |
| topic | Trading and Market Microstructure Computation and Language Computational Finance |
| url | https://arxiv.org/abs/2508.00554 |