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 them across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash. Live experiments on both cryptocurrency and stock markets demonstrate that agent frameworks display markedly distinct behavioral patterns, spanning from aggressive risk-taking to conservative decision-making, whereas model backbones contribute less to outcome variation. AMA thus establishes a foundation for rigorous, reproducible, and continuously evolving evaluation of financial reasoning and trading intelligence in LLM-based agents.
@article{arxiv.2510.11695,
title = {When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents},
author = {Lingfei Qian and Xueqing Peng and Yan Wang and Vincent Jim Zhang and Huan He and Hanley Smith and Yi Han and Yueru He and Haohang Li and Yupeng Cao and Yangyang Yu and Alejandro Lopez-Lira and Peng Lu and Jian-Yun Nie and Guojun Xiong and Jimin Huang and Sophia Ananiadou},
journal= {arXiv preprint arXiv:2510.11695},
year = {2025}
}