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

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/).