Paper page - AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets

Source: https://huggingface.co/papers/2512.10971

Abstract

AI-Trader presents the first fully automated live benchmark for evaluating large language models in financial decision-making across multiple markets with autonomous information processing.

Large Language Models(LLMs) have demonstrated remarkable potential asautonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. However, decision-making in fully dynamic and live environments remains highly challenging, requiring real-timeinformation integrationand adaptive responses. While existing efforts have exploredlive evaluationmechanisms in structured tasks, a critical gap remains in systematicbenchmarkingfor real-world applications, particularly in finance where stringent requirements exist for live strategic responsiveness. To address this gap, we introduce AI-Trader, the first fully-automated, live, and data-uncontaminated evaluation benchmark for LLM agents infinancial decision-making. AI-Trader spans three major financial markets: U.S. stocks, A-shares, and cryptocurrencies, with multiple trading granularities to simulate live financial environments. Our benchmark implements a revolutionary fully autonomous minimal information paradigm where agents receive only essential context and must independently search, verify, and synthesize live market information without human intervention. We evaluate six mainstream LLMs across three markets and multiple trading frequencies. Our analysis reveals striking findings: general intelligence does not automatically translate to effective trading capability, with most agents exhibiting poor returns and weakrisk management. We demonstrate that risk control capability determines cross-market robustness, and that AItrading strategiesachieve excess returns more readily in highly liquid markets than policy-driven environments. These findings expose critical limitations in currentautonomous agentsand provide clear directions for future improvements. The code and evaluation data are open-sourced to foster community research: https://github.com/HKUDS/AI-Trader.

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