QuantAgents: Towards Multi-agent Financial System via Simulated Trading

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

Abstract:

In this paper, our objective is to develop a
multi-agent financial system that incorporates
simulated trading, a technique extensively
utilized by financial professionals. While
current LLM-based agent models demonstrate
competitive performance, they still exhibit
significant deviations from real-world fund
companies. A critical distinction lies in the
agents’ reliance on “post-reflection”, particularly in response to adverse outcomes, but
lack a distinctly human capability: long-term
prediction of future trends. Therefore, we
introduce QuantAgents, a multi-agent system
integrating simulated trading, to comprehensively evaluate various investment strategies
and market scenarios without assuming actual
risks. Specifically, QuantAgents comprises
four agents: a simulated trading analyst, a risk
control analyst, a market news analyst, and
a manager, who collaborate through several
meetings. Moreover, our system incentivizes
agents to receive feedback on two fronts: performance in real-world markets and predictive
accuracy in simulated trading. Extensive
experiments demonstrate that our framework
excels across all metrics, yielding an overall
return of nearly 300% over the three years
(https://quantagents.github.io/).