WWW2026
When Agents Trade: Live Multi-Market Trading Arena for LLM Agents
Lingfei Qian, Xueqing Peng, Hanley Smith, Yi Han, Yueru He, Haohang Li, Yupeng Cao, Yangyang Yu, Guojun Xiong, Peng Lu, Yan Wang, Vincent Jim Zhang, Huan He, Alejandro Lopez-Lira, Jimin Huang, Jian-Yun Nie, Sophia Ananiadou
Abstract
Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies test models instead of agents, cover limited periods and assets, and rely on unverified data. To address these gaps, we introduce Agent Market Arena (AMA), the first lifelong, real-time benchmark for evaluating LLM-based trading agents across multiple markets. AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework, enabling fair and continuous comparison under real conditions. It implements four agents, including InvestorAgent as a single-agent baseline, TradeAgent and HedgeFundAgent with different risk styles, and DeepFundAgent with memory-based reasoning, and evaluates