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ATLAS framework uses adaptive LLM prompts for better trading decisions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ATLAS, a multi-agent framework designed to enhance financial trading decisions using large language models. This system integrates market data, news, and corporate fundamentals, with a central agent capable of generating executable market orders. A key innovation is Adaptive-OPRO, a prompt-optimization technique that dynamically adjusts instructions based on real-time feedback, leading to improved performance over time compared to static prompts. AI

IMPACT Introduces a novel prompt optimization technique for LLM agents in financial trading, potentially improving decision-making and order execution.

RANK_REASON This is a research paper detailing a novel framework and technique for LLM-based trading agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ATLAS framework uses adaptive LLM prompts for better trading decisions

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This is a research paper detailing a novel framework and technique for LLM-based trading agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Charidimos Papadakis, Angeliki Dimitriou, Giorgos Filandrianos, Maria Lymperaiou, Konstantinos Thomas, Giorgos Stamou ·

    ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination

    arXiv:2510.15949v4 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 noi…

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