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

Paper: arXiv 2508.00554

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.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs only partially modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"];

Cited References (37)

1. FinRL: deep reinforcement learning framework to automate trading in quantitative finance (2022) — link
2. FinGPT: Open-Source Financial Large Language Models — → 2306.06031
3. TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance (2023) — link
4. Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models — → 2402.03659
5. QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model — → 2402.03755
6. Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment — → 2308.00016
7. Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment — → 2402.09746
8. FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design (2024) — link

11 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.