ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism

ArXiv ID: 2508.00554 “View on arXiv”

Authors: Li Zhao, Rui Sun, Zuoyou Jiang, Bo Yang, Yuxiao Bai, Mengting Chen, Xinyang Wang, Jing Li, Zuo Bai

Abstract

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.

Keywords: Large Language Model (LLM) Agents, Multi-Agent Systems, Algorithmic Trading, Market Noise Filtering, Reinforcement Learning, Equities / Trading

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper focuses on a novel architectural design (multi-agent system with contest mechanism) and empirical validation, with the math involved being at a systems-engineering level rather than advanced mathematical finance. The empirical rigor is high due to the reported backtests, open-source code availability on GitHub, and focus on real market data processing and performance metrics.

  flowchart TD
    A["Research Goal: LLM Agents in Trading"] --> B{"Problem: High Sensitivity to Market Noise"};
    B --> C["Proposed System: ContestTrade Multi-Agent"];
    C --> D["Key Innovation: Internal Contest Mechanism"];
    D --> E["Data Team: Process Market Data -> Text Factors"];
    D --> F["Research Team: Parallel Trading Decisions"];
    E --> G["Real-time Scoring & Ranking via Market Feedback"];
    F --> G;
    G --> H["Outcome: Top Agent Outputs Adopted"];
    H --> I["Final Results: Outperforms SOTA & Traditional Methods"];