AI-TRADER: BENCHMARKING AUTONOMOUS AGENTS IN REAL-TIME FINANCIAL MARKETS

Battle Count: 76

Highly relevant to quantitative trading as it directly benchmarks LLM-based autonomous agents in real-time financial markets. The paper provides systematic evaluation of trading strategies across three major markets with multiple frequencies, revealing critical insights about risk management, cross-market generalization, and the gap between general AI capability and trading performance. The findings directly inform the development of AI-driven trading systems and highlight key areas for improvement in autonomous trading agents.

The framework is designed to be modular and extensible with standardized MCP interfaces, making it relatively accessible for integration. However, implementing a fully autonomous trading agent with proper risk management, information verification, and cross-market adaptability is complex. The benchmark itself provides a plug-and-play testing platform, but achieving competitive trading performance requires sophisticated strategy design. The live market integration and temporal filtering add operational complexity.

Code and evaluation data are open-sourced at https://github.com/HKUDS/AI-Trader. The framework uses standardized MCP tools and provides detailed prompts in the appendix. However, live market data is time-dependent and results may not be exactly reproducible due to the dynamic nature of financial markets. The evaluation period (Oct-Nov 2025) is fixed but market conditions are inherently non-reproducible.