ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination
arXiv:2510.15949v5 Announce Type: replace-cross Abstract: Large language models show promise for financial decision-making, yet deploying them as autonomous trading agents raises fundamental challenges: how to adapt instructions when rewards arrive late and obscured by market noise, how to synthesize heterogeneous information streams into coherent decisions, and how to bridge the gap between model outputs and executable market actions. We present ATLAS (Adaptive Trading with LLM AgentS), a unified multi-agent framework that integrates structured information from markets, news, and corporate fu
The paper demonstrates significant progress in applying large language models to complex, real-time financial decision-making, addressing key challenges in autonomous trading agent development.
This development indicates a tangible path toward deploying AI agents in high-stakes financial environments, potentially automating and transforming a significant segment of white-collar work and capital allocation.
The ability of LLM agents to synthesize diverse information and adapt strategies dynamically in trading scenarios signifies a move from assistive AI tools to autonomous decision-making systems in finance.
- · AI/ML research institutions
- · Quantitative trading firms embracing AI
- · Financial technology providers
- · Developers of multi-agent LLM systems
- · Traditional human-led discretionary trading desks
- · Legacy financial institutions slow to adopt AI
- · Vendors of less sophisticated trading automation tools
Increased efficiency and potentially new forms of market volatility due to AI-driven trading strategies.
Heightened competition in financial markets as AI agents optimize capital deployment and exploit micro-inefficiencies at scale.
Regulatory bodies will need to rapidly adapt to govern autonomous AI agents in financial markets, potentially leading to new compliance frameworks and oversight mechanisms.
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Read at arXiv cs.AI