The launch of \$Trump coin ignited a wave in meme coin investment. Copy trading, as a strategy-agnostic approach that eliminates the need for deep trading knowledge, quickly gains widespread popularity in the meme coin market. However, copy trading is not a guarantee of profitability due to the prevalence of manipulative bots, the uncertainty of the followed wallets' future performance, and the lag in trade execution. Recently, large language models (LLMs) have shown promise in financial applications by effectively understanding multi-modal data and producing explainable decisions. However, a single LLM struggles with complex, multi-faceted tasks such as asset allocation. These challenges are even more pronounced in cryptocurrency markets, where LLMs often lack sufficient domain-specific knowledge in their training data. To address these challenges, we propose an explainable multi-agent system for meme coin copy trading. Inspired by the structure of an asset management team, our system decomposes the complex task into subtasks and coordinates specialized agents to solve them collaboratively. Employing few-shot chain-of-though (CoT) prompting, each agent acquires professional meme coin trading knowledge, interprets multi-modal data, and generates explainable decisions. Using a dataset of 1,000 meme coin projects' transaction data, our empirical evaluation shows that the proposed multi-agent system outperforms both traditional machine learning models and single LLMs, achieving 73% and 70% precision in identifying high-quality meme coin projects and key opinion leader (KOL) wallets, respectively. The selected KOLs collectively generated a total profit of \$500,000 across these projects.

翻译：特朗普币的发行引发了模因币投资热潮。跟单交易作为一种策略无关的方法，无需深厚的交易知识，在模因币市场迅速获得广泛欢迎。然而，由于操纵性机器人的盛行、被跟单钱包未来表现的不确定性以及交易执行的滞后性，跟单交易并不能保证盈利。近年来，大型语言模型通过有效理解多模态数据并生成可解释的决策，在金融应用中展现出潜力。然而，单个LLM难以处理资产配置等复杂的多层面任务。这些挑战在加密货币市场中更为突出，因为LLM的训练数据往往缺乏足够的领域专业知识。为应对这些挑战，我们提出了一种用于模因币跟单交易的可解释多智能体系统。受资产管理团队结构的启发，我们的系统将复杂任务分解为子任务，并协调专业智能体协作解决。通过采用少样本思维链提示技术，每个智能体获取专业的模因币交易知识，解读多模态数据，并生成可解释的决策。基于1,000个模因币项目的交易数据集，我们的实证评估表明，所提出的多智能体系统在识别高质量模因币项目和关键意见领袖钱包方面，分别实现了73%和70%的精确率，其表现优于传统机器学习模型和单LLM模型。所选KOL钱包在这些项目中累计产生了50万美元的总利润。