Copy trading has become the dominant entry strategy in meme coin markets. However, due to the market's extreme illiquid and volatile nature, the strategy exposes an exploitable attack surface: adversaries deploy manipulative bots to front-run trades, conceal positions, and fabricate sentiment, systematically extracting value from naïve copiers at scale. Despite its prevalence, bot-driven manipulation remains largely unexplored, and no robust defensive framework exists. We propose a manipulation-resistant copy-trading system based on a multi-agent architecture powered by a multi-modal, explainable large language model (LLM). Our system decomposes copy trading into three specialized agents for coin evaluation, wallet selection, and timing assessment. Evaluated on historical data from over 6,000 meme coins, our approach outperforms zero-shot and most statistic-driven baselines in prediction accuracy as well as all baselines in economic performance, achieving an average return of 14% for identified smart-money trades and an estimated copier return of 3% per trade under realistic market frictions. Overall, our results demonstrate the effectiveness of agent-based defenses and predictability of trader profitability in adversarial meme coin markets, providing a practical foundation for robust copy trading.

翻译：跟单交易已成为迷因币市场中的主流进入策略。然而，由于该市场极度缺乏流动性且波动剧烈，该策略暴露了一个可利用的攻击面：对手部署操纵性机器人进行抢先交易、隐藏持仓并伪造市场情绪，从而系统性地大规模从缺乏经验的跟单者处提取价值。尽管此类操纵行为普遍存在，但机器人驱动的操纵在很大程度上仍未得到充分研究，且目前尚无稳健的防御框架。我们提出了一种抗操纵的跟单交易系统，该系统基于一个由多模态、可解释的大语言模型驱动的多智能体架构。我们的系统将跟单交易分解为三个专门化的智能体，分别负责代币评估、钱包选择和时机判断。在超过6,000种迷因币的历史数据上进行评估后，我们的方法在预测准确性上优于零样本基准和大多数基于统计的基准，并且在经济绩效上优于所有基准，在识别出的聪明钱交易中实现了14%的平均回报，并在现实市场摩擦下估计每笔交易可为跟单者带来3%的回报。总体而言，我们的结果证明了基于智能体的防御在对抗性迷因币市场中的有效性以及交易者盈利能力的可预测性，为稳健的跟单交易提供了实践基础。