# QuantAgents: Towards Multi-agent Financial System via Simulated Trading

arXiv: https://arxiv.org/abs/2510.04643 · HTML: https://mlsanity.com/paper/2510.04643/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 14 of 14)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [Qlib: An AI-oriented Quantitative Investment Platform](https://mlsanity.com/paper/2009.11189/) (2020)
- [HedgeAgents: A Balanced-aware Multi-agent Financial Trading System](https://mlsanity.com/paper/2502.13165/) (2025)
- [GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding](https://mlsanity.com/paper/2406.09781/) (2024)
- [FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model](https://mlsanity.com/paper/2403.02647/) (2024)
- [PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets](https://mlsanity.com/paper/2302.00586/) (2023)
- [Democratization of Retail Trading: Can Reddit's WallStreetBets Outperform Investment Bank Analysts?](https://mlsanity.com/paper/2301.00170/) (2022)

## Built on by (most cited first, 8 of 8)

- [QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining](https://mlsanity.com/paper/2602.07085/) (2026)
- [SAGE: Benchmarking and Improving Retrieval for Deep Research Agents](https://mlsanity.com/paper/2602.05975/) (2026)
- [Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness](https://mlsanity.com/paper/2603.27539/) (2026)
- [EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies](https://mlsanity.com/paper/2602.09514/) (2026)
- [A Multi-Agent Orchestration Framework for Venture Capital Due Diligence](https://mlsanity.com/paper/2605.13110/) (2026)
- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)
- [From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models](https://mlsanity.com/paper/2608.06020/) (2026)
- [PAWS: Policy-driven Agentic World Simulation](https://mlsanity.com/paper/2609.28547/) (2026)

## Abstract (arXiv)

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/).