@inproceedings{ma-etal-2025-agent,
title = "Agent Trading Arena: A Study on Numerical Understanding in {LLM}-Based Agents",
author = "Ma, Tianmi and
Du, Jiawei and
Huang, Wenxin and
Wang, Wenjie and
Xie, Liang and
Zhong, Xian and
Zhou, Joey Tianyi",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.294/",
doi = "10.18653/v1/2025.findings-emnlp.294",
pages = "5496--5514",
ISBN = "979-8-89176-335-7",
abstract = "Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are confined to historical backtesting, where trading actions cannot influence market prices, and agents train on static data. To overcome this limitation, we present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive, mult-agent trading and directly impact price dynamics. By simulating realistic bid-ask interactions, our platform enables agents to train in scenarios that closely mirror live markets, thereby narrowing the gap between training and evaluation. Experiments show that LLMs struggle with numerical reasoning when given plain-text data, tending to overfit local patterns and recent values. In contrast, chart-based visualizations significantly boost both numerical reasoning and trading performance. Moreover, integrating a reflection module yields further improvements, especially with visual inputs. Finally, evaluations of the NASDAQ and CSI datasets demonstrate the superiority of our method, particularly under high volatility. All code and data are available at \url{https://github.com/wekjsdvnm/Agent-Trading-Arena}."
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<abstract>Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are confined to historical backtesting, where trading actions cannot influence market prices, and agents train on static data. To overcome this limitation, we present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive, mult-agent trading and directly impact price dynamics. By simulating realistic bid-ask interactions, our platform enables agents to train in scenarios that closely mirror live markets, thereby narrowing the gap between training and evaluation. Experiments show that LLMs struggle with numerical reasoning when given plain-text data, tending to overfit local patterns and recent values. In contrast, chart-based visualizations significantly boost both numerical reasoning and trading performance. Moreover, integrating a reflection module yields further improvements, especially with visual inputs. Finally, evaluations of the NASDAQ and CSI datasets demonstrate the superiority of our method, particularly under high volatility. All code and data are available at https://github.com/wekjsdvnm/Agent-Trading-Arena.</abstract>
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%0 Conference Proceedings
%T Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents
%A Ma, Tianmi
%A Du, Jiawei
%A Huang, Wenxin
%A Wang, Wenjie
%A Xie, Liang
%A Zhong, Xian
%A Zhou, Joey Tianyi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F ma-etal-2025-agent
%X Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are confined to historical backtesting, where trading actions cannot influence market prices, and agents train on static data. To overcome this limitation, we present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive, mult-agent trading and directly impact price dynamics. By simulating realistic bid-ask interactions, our platform enables agents to train in scenarios that closely mirror live markets, thereby narrowing the gap between training and evaluation. Experiments show that LLMs struggle with numerical reasoning when given plain-text data, tending to overfit local patterns and recent values. In contrast, chart-based visualizations significantly boost both numerical reasoning and trading performance. Moreover, integrating a reflection module yields further improvements, especially with visual inputs. Finally, evaluations of the NASDAQ and CSI datasets demonstrate the superiority of our method, particularly under high volatility. All code and data are available at https://github.com/wekjsdvnm/Agent-Trading-Arena.
%R 10.18653/v1/2025.findings-emnlp.294
%U https://aclanthology.org/2025.findings-emnlp.294/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.294
%P 5496-5514
Markdown (Informal)
[Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents](https://aclanthology.org/2025.findings-emnlp.294/) (Ma et al., Findings 2025)
ACL