IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.00554.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.

Suggested Citation

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

    Download full text from publisher

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

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    4. Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
    5. 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.
    6. 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.
    7. 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.
    8. 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 Aug 2025.
    3. Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
    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. 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.
    6. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    7. Arvid Oskar Ivar Hoffmann & Wander Jager & J. H. Von Eije, 2007. "Social Simulation of Stock Markets: Taking It to the Next Level," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-7.
    8. Kassiani Papasotiriou & Srijan Sood & Shayleen Reynolds & Tucker Balch, 2024. "AI in Investment Analysis: LLMs for Equity Stock Ratings," Papers 2411.00856, arXiv.org.
    9. Navarro-Barrientos, Jesús Emeterio & Cantero-Álvarez, Rubén & Matias Rodrigues, João F. & Schweitzer, Frank, 2008. "Investments in random environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2035-2046.
    10. J. Doyne Farmer & John Geanakoplos, 2008. "The virtues and vices of equilibrium and the future of financial economics," Papers 0803.2996, arXiv.org.
    11. Gupta, Abhijit, 2025. "Decoding Futures Price Dynamics: A Regularized Sparse Autoencoder for Interpretable Multi-Horizon Forecasting and Factor Discovery," OSF Preprints 4rzky_v1, Center for Open Science.
    12. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    13. Claudia Biancotti & Carolina Camassa, 2023. "Loquacity and visible emotion: ChatGPT as a policy advisor," Questioni di Economia e Finanza (Occasional Papers) 814, Bank of Italy, Economic Research and International Relations Area.
    14. Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez, 2024. "Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning," Papers 2409.11408, arXiv.org.
    15. Rick Steinert & Saskia Altmann, 2023. "Linking microblogging sentiments to stock price movement: An application of GPT-4," Papers 2308.16771, arXiv.org.
    16. Raeid Saqur & Ken Kato & Nicholas Vinden & Frank Rudzicz, 2024. "NIFTY Financial News Headlines Dataset," Papers 2405.09747, arXiv.org.
    17. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    18. Dat Mai, 2024. "StockGPT: A GenAI Model for Stock Prediction and Trading," Papers 2404.05101, arXiv.org, revised Oct 2024.
    19. Emeka Nkoro & Aham Kelvin Uko, 2016. "Exchange Rate and Inflation Volatility and Stock Prices Volatility: Evidence from Nigeria, 1986-2012," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 6(6), pages 1-4.
    20. Berg, Joyce E. & Rietz, Thomas A., 2019. "Longshots, overconfidence and efficiency on the Iowa Electronic Market," International Journal of Forecasting, Elsevier, vol. 35(1), pages 271-287.

    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: http://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.