标题： 基于大型语言模型的多智能体自动化加密资产组合管理系统 显示英文标题

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

摘要： 由于与传统资产相比加密货币的历史较短，需要整合来自各种模态的大量数据，并且需要复杂的推理，因此加密货币投资本质上具有挑战性。 尽管深度学习方法已被应用于解决这些挑战，但它们的黑箱性质引发了关于信任和可解释性的担忧。 最近，大型语言模型（LLMs）因其能够理解多模态数据并生成可解释的决策而在金融应用中显示出希望。 然而，单一LLM在复杂的综合性任务（如资产管理）中面临限制。 这些限制在加密货币投资中更为明显，因为LLMs在其训练语料库中缺乏特定领域的知识。 为了解决这些挑战，我们提出了一个针对加密货币投资的可解释、多模态、多代理框架。 我们的框架使用专门的代理，在团队内部和跨团队协作以处理子任务，例如对市值排名前30的加密货币进行数据分析、文献集成和投资决策制定。 专家训练模块利用多模态历史数据和专业投资文献微调代理，而多代理投资模块则利用实时数据做出明智的加密货币投资决策。 独特的团队内和团队间协作机制通过根据代理团队内的置信水平调整最终预测并促进团队之间的信息共享来提高预测准确性。 使用2023年11月至2024年9月的数据进行的经验评估表明，与单代理模型和市场基准相比，我们的框架在分类、资产定价、投资组合和可解释性性能方面表现更优。 显示英文摘要

摘要： 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.