Q uant A gents: Towards Multi-agent Financial System via Simulated Trading (2025)

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).

## introduction in the data-driven era, the ascendancy of artificial intelligence has sparked transformative changes within the financial area @xcite . advancements in large language models (llms), notably exemplified by systems like fingpt @xcite and finreport @xcite , are significantly elevating the automation and intelligence levels in financial analysis and decisionmaking. these frameworks not only enhance the scope and profundity of financial analysis but * equal contribution.