用多智能体系统实现金融交易中的风险对冲，提升稳定性与收益。
HedgeAgents: A Balanced-aware Multi-agent Financial Trading System
- 设计中心管理+专业对冲专家的多智能体架构，通过会议协调决策。
- 三年内年化收益70%，总回报率达400%，显著优于传统策略。
- 适合追求稳健高回报的量化交易研究者与金融机构参考。
随着自动化交易在金融市场的普及，算法投资策略日益重要。尽管大语言模型（LLMs）和基于智能体的模型在实时市场分析与交易决策中展现出潜力，但在面临快速下跌或频繁波动时仍会遭遇高达20%的损失，限制了其实际应用。因此，亟需构建更稳健、更具韧性的框架。本文提出创新的多智能体系统HedgeAgents，通过‘对冲’策略增强系统鲁棒性。该系统由一名中央基金经理和多位专注于不同金融资产类别的对冲专家组成，各智能体利用LLM的认知能力进行决策，并通过三种类型的会议实现协同。得益于LLM的强大理解能力，HedgeAgents在三年周期内实现了70%的年化收益率和400%的总回报率。此外，我们欣喜地发现，该系统所形成的投资经验已接近人类专家水平（https://hedgeagents.github.io/）。
原文摘要 · Abstract (English)
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/).
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