# When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents

arXiv: https://arxiv.org/abs/2510.11695 · HTML: https://mlsanity.com/paper/2510.11695/

## Key facts

- Cited by 16 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 9 of its 44 references that are in the map.
- First posted on arXiv 13 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, 9 of 9)

- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading](https://mlsanity.com/paper/2502.11433/) (2025)
- [Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance](https://mlsanity.com/paper/2502.18772/) (2025)
- [Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management](https://mlsanity.com/paper/2402.00515/) (2024)

## Built on by (most cited first, 16 of 16)

- [Quantifying Trust: Financial Risk Management for Trustworthy AI Agents](https://mlsanity.com/paper/2604.03976/) (2026)
- [Agentic Trading: When LLM Agents Meet Financial Markets](https://mlsanity.com/paper/2605.19337/) (2026)
- [Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness](https://mlsanity.com/paper/2603.27539/) (2026)
- [FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosures](https://mlsanity.com/paper/2604.05966/) (2026)
- [The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence](https://mlsanity.com/paper/2605.16895/) (2026)
- [OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents](https://mlsanity.com/paper/2606.26350/) (2026)
- [E-Commerce Bench: Evaluating LLM Agents on Long-Horizon Autonomous Business Operation](https://mlsanity.com/paper/2608.30730/) (2026)
- [TruthTensor: Evaluating LLMs through Human Imitation on Prediction Market under Drift and Holistic Reasoning](https://mlsanity.com/paper/2601.13545/) (2026)
- [Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines](https://mlsanity.com/paper/2603.08704/) (2026)
- [TimeSeek: Temporal Reliability of Agentic Forecasters](https://mlsanity.com/paper/2604.04220/) (2026)
- [Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading](https://mlsanity.com/paper/2605.01954/) (2026)
- [Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents](https://mlsanity.com/paper/2605.29174/) (2026)
- [LATTICE: Evaluating Decision Support Utility of Crypto Agents](https://mlsanity.com/paper/2604.26235/) (2026)
- [ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution](https://mlsanity.com/paper/2608.05790/) (2026)
- [Can Language Models Learn to Forecast Stock Prices](https://mlsanity.com/paper/2609.36914/) (2026)
- [ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality](https://mlsanity.com/paper/2610.00710/) (2026)

## Abstract (arXiv)

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.