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HedgeAgents: A Balanced-aware Multi-agent Financial Trading System

Author

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  • Xiangyu Li
  • Yawen Zeng
  • Xiaofen Xing
  • Jin Xu
  • Xiangmin Xu

Abstract

As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations, impeding their practical application. Hence, there is an imperative to explore a more robust and resilient framework. This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ``hedging'' strategies. In this well-balanced system, an array of hedging agents has been tailored, where HedgeAgents consist of a central fund manager and multiple hedging experts specializing in various financial asset classes. These agents leverage LLMs' cognitive capabilities to make decisions and coordinate through three types of conferences. Benefiting from the powerful understanding of LLMs, our HedgeAgents attained a 70% annualized return and a 400% total return over a period of 3 years. Moreover, we have observed with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/).

Suggested Citation

  • Xiangyu Li & Yawen Zeng & Xiaofen Xing & Jin Xu & Xiangmin Xu, 2025. "HedgeAgents: A Balanced-aware Multi-agent Financial Trading System," Papers 2502.13165, arXiv.org.
    • Handle: RePEc:arx:papers:2502.13165
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    References listed on IDEAS

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    1. Kanamura, Takashi & Homann, Lasse & Prokopczuk, Marcel, 2021. "Pricing analysis of wind power derivatives for renewable energy risk management," Applied Energy, Elsevier, vol. 304(C).
    2. Louis R. Eeckhoudt & Roger J. A. Laeven, 2022. "Dual Moments and Risk Attitudes," Operations Research, INFORMS, vol. 70(3), pages 1330-1341, May.
    3. 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.
    4. Molei Qin & Shuo Sun & Wentao Zhang & Haochong Xia & Xinrun Wang & Bo An, 2023. "EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading," Papers 2309.12891, arXiv.org.
    5. Yu, Jianfeng & Yuan, Yu, 2011. "Investor sentiment and the mean-variance relation," Journal of Financial Economics, Elsevier, vol. 100(2), pages 367-381, May.
    6. Hongyang Yang & Xiao-Yang Liu & Christina Dan Wang, 2023. "FinGPT: Open-Source Financial Large Language Models," Papers 2306.06031, arXiv.org.
    7. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    8. Yangyang Yu & Haohang Li & Zhi Chen & Yuechen Jiang & Yang Li & Denghui Zhang & Rong Liu & Jordan W. Suchow & Khaldoun Khashanah, 2023. "FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design," Papers 2311.13743, arXiv.org, revised Dec 2023.
    9. Blanka Horvath & Aitor Muguruza & Mehdi Tomas, 2021. "Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 21(1), pages 11-27, January.
    10. Shuo Sun & Molei Qin & Xinrun Wang & Bo An, 2023. "PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets," Papers 2302.00586, arXiv.org, revised Mar 2023.
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