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

ArXiv ID: 2510.15949 “View on arXiv”

Authors: Charidimos Papadakis, Angeliki Dimitriou, Giorgos Filandrianos, Maria Lymperaiou, Konstantinos Thomas, Giorgos Stamou

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 fundamentals to support robust trading decisions. Within ATLAS, the central trading agent operates in an order-aware action space, ensuring that outputs correspond to executable market orders rather than abstract signals. The agent can incorporate feedback while trading using Adaptive-OPRO, a novel prompt-optimization technique that dynamically adapts the prompt by incorporating real-time, stochastic feedback, leading to increasing performance over time. Across regime-specific equity studies and multiple LLM families, Adaptive-OPRO consistently outperforms fixed prompts, while reflection-based feedback fails to provide systematic gains.

Keywords: Large Language Models (LLM), Multi-agent framework, Adaptive-OPRO, Order-aware action space, Reinforcement learning from human feedback, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper presents a novel multi-agent framework (ATLAS) with Adaptive-OPRO, relying on conceptual integration and prompt engineering rather than advanced mathematical derivations. It demonstrates high empirical rigor through regime-specific equity studies, backtests across multiple LLM families, and reported performance comparisons, though it lacks explicit code or dataset disclosures.

  flowchart TD
    A["Research Goal: Deploying LLMs as Autonomous Trading Agents"] --> B["Key Methodology: ATLAS Framework"]
    B --> C{"Core Components"}
    C --> D["Adaptive-OPRO<br/>Dynamic Prompt Optimization"]
    C --> E["Order-Aware Action Space<br/>Executable Market Orders"]
    C --> F["Multi-Agent Coordination"]
    
    G["Data Inputs: Market Data, News, Corporate Fundamentals"] --> B
    
    subgraph H["Computational Processes"]
        D --> I["Feedback Loop: Real-time, Stochastic Rewards"]
        F --> J["Heterogeneous Information Synthesis"]
    end
    
    I --> K{"Key Findings"}
    J --> K
    E --> K
    
    K --> L["Adaptive-OPRO outperforms fixed prompts"]
    K --> M["Order-aware actions bridge model outputs to execution"]
    K --> N["Reflection-based feedback fails to provide systematic gains"]