LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management

ArXiv ID: 2501.00826 “View on arXiv”

Authors: Unknown

Abstract

Cryptocurrency investment is inherently difficult due to its shorter history compared to traditional assets, the need to integrate vast amounts of data from various modalities, and the requirement for complex reasoning. While deep learning approaches have been applied to address these challenges, their black-box nature raises concerns about trust and explainability. Recently, large language models (LLMs) have shown promise in financial applications due to their ability to understand multi-modal data and generate explainable decisions. However, single LLM faces limitations in complex, comprehensive tasks such as asset investment. These limitations are even more pronounced in cryptocurrency investment, where LLMs have less domain-specific knowledge in their training corpora. To overcome these challenges, we propose an explainable, multi-modal, multi-agent framework for cryptocurrency investment. Our framework uses specialized agents that collaborate within and across teams to handle subtasks such as data analysis, literature integration, and investment decision-making for the top 30 cryptocurrencies by market capitalization. The expert training module fine-tunes agents using multi-modal historical data and professional investment literature, while the multi-agent investment module employs real-time data to make informed cryptocurrency investment decisions. Unique intrateam and interteam collaboration mechanisms enhance prediction accuracy by adjusting final predictions based on confidence levels within agent teams and facilitating information sharing between teams. Empirical evaluation using data from November 2023 to September 2024 demonstrates that our framework outperforms single-agent models and market benchmarks in classification, asset pricing, portfolio, and explainability performance.

Keywords: multi-agent systems, large language models (LLMs), crypto investing, multi-modal data, explainable AI, crypto

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper presents an empirical framework with real-time data, backtests against benchmarks, and specific performance metrics, indicating high implementation and data rigor. However, its mathematical complexity is low, relying on LLM architectures and confidence aggregation rather than dense mathematical derivations or advanced statistical theory.

  flowchart TD
    A["Research Goal:<br>Explainable Automated<br>Crypto Portfolio Management"] --> B["Methodology:<br>Multi-Agent LLM Framework"]

    B --> C{"Key Components"}

    C --> D["Specialized Agents"]
    C --> E["Expert Training Module<br>Fine-tuning with multi-modal data"]
    C --> F["Multi-Agent Investment Module<br>Real-time decision making"]
    C --> G["Collaboration Mechanisms<br>Intrateam & Interteam coordination"]

    D & E & F & G --> H["Computational Process:<br>Agent collaboration &<br>Confidence-weighted prediction"]

    H --> I["Key Outcomes<br>2023-2024 Evaluation"]
    
    I --> J{"Performance Results"}
    
    J --> K["Outperforms benchmarks<br>in classification & pricing"]
    J --> L["Superior portfolio performance"]
    J --> M["Enhanced explainability"]