IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.00554.html

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

Author

Listed:
  • Rui Sun
  • Li Zhao
  • Zuoyou Jiang
  • Bo Yang
  • Yuxiao Bai
  • Mengting Chen
  • Jing Li
  • Zuo Bai

Abstract

In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy and non-stationary market information. We propose ContestTrade, a multi-agent trading system with an internal competitive mechanism inspired by institutional investment workflows. The system consists of two specialized teams: (1) a Data Team that processes and condenses massive market data into diversified textual factors optimized for constrained LLM context windows, and (2) a Research Team that produces parallelized multipath trading decisions via tool-augmented deep research. The core design is a "Quantify-Predict-Allocate" contest mechanism within each team: agent outputs are scored only after market outcomes become observable, future utility is predicted from historical scores, and resources are allocated to agents with positive predicted utility. In a post-2024 A-share backtest, ContestTrade achieves higher backtested return and risk-adjusted performance than the evaluated baselines. We further describe the temporal protocol, implementation choices, and limitations to clarify the scope of these results.

Suggested Citation

  • Rui Sun & Li Zhao & Zuoyou Jiang & Bo Yang & Yuxiao Bai & Mengting Chen & Jing Li & Zuo Bai, 2025. "ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism," Papers 2508.00554, arXiv.org, revised Jul 2026.
    • Handle: RePEc:arx:papers:2508.00554
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2508.00554
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    2. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    3. Yang Li & Yangyang Yu & Haohang Li & Zhi Chen & Khaldoun Khashanah, 2023. "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance," Papers 2309.03736, arXiv.org.
    4. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Oct 2025.
    5. Han Ding & Yinheng Li & Junhao Wang & Hang Chen & Doudou Guo & Yunbai Zhang, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org, revised Mar 2026.
    6. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    7. Georgios Fatouros & Konstantinos Metaxas & John Soldatos & Dimosthenis Kyriazis, 2024. "Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection," Papers 2401.03737, arXiv.org, revised Apr 2024.
    8. Haohan Zhang & Fengrui Hua & Chengjin Xu & Hao Kong & Ruiting Zuo & Jian Guo, 2023. "Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?," Papers 2306.14222, arXiv.org, revised May 2024.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yijia Xiao & Edward Sun & Tong Chen & Fang Wu & Di Luo & Wei Wang, 2025. "Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning," Papers 2509.11420, arXiv.org.
    2. Weixian Waylon Li & Hyeonjun Kim & Mihai Cucuringu & Tiejun Ma, 2025. "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?," Papers 2505.07078, arXiv.org, revised Jun 2026.
    3. Han Ding & Yinheng Li & Junhao Wang & Hang Chen & Doudou Guo & Yunbai Zhang, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org, revised Mar 2026.
    4. Mohammed-Khalil Ghali & Cecil Pang & Oscar Molina & Carlos Gershenson-Garcia & Daehan Won, 2025. "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News," Papers 2508.06497, arXiv.org.
    5. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
    6. Çetin, Uzay & Demirtaş, Şükrü C. & Şahin, Senem Çakmak, 2025. "Price dynamics in artificial stock market with realistic order book mechanism," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
    7. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    8. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    9. Vivien Lespagnol & Juliette Rouchier, 2015. "What Is the Impact of Heterogeneous Knowledge About Fundamentals on Market Liquidity and Efficiency: An ABM Approach," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 105-117, Springer.
    10. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    11. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    12. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    13. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    14. Xintong Wang & Christopher Hoang & Yevgeniy Vorobeychik & Michael P. Wellman, 2021. "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis," Games, MDPI, vol. 12(2), pages 1-43, May.
    15. Tucker Hybinette Balch & Mahmoud Mahfouz & Joshua Lockhart & Maria Hybinette & David Byrd, 2019. "How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?," Papers 1906.12010, arXiv.org.
    16. Hui Gong, 2026. "AI Agents in Financial Markets: Architecture, Applications, and Systemic Implications," Papers 2603.13942, arXiv.org, revised Apr 2026.
    17. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    18. Alan G. Isaac & Vasudeva Ramaswamy, 2023. "Rule-based trading on an order-driven exchange: a reassessment," Quantitative Finance, Taylor & Francis Journals, vol. 23(12), pages 1871-1886, November.
    19. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    20. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2508.00554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.