Copy trading has become the dominant entry strategy in meme coin markets. However, due to the market's extremely 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 large language model (LLM) and chain-of-thought (CoT) reasoning. Our approach outperforms zero-shot and most statistic-driven baselines in prediction accuracy as well as all baselines in economic performance, achieving an average copier return of 3% per meme coin investment 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：跟单交易已成为模因币市场中的主流进入策略。然而，由于该市场流动性极差且波动剧烈，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交易前置、隐藏头寸并伪造市场情绪，从而系统性地大规模从天真跟单者处提取价值。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，且目前缺乏稳健的防御框架。我们提出了一种抗操纵的跟单交易系统，其基于由多模态大语言模型（LLM）和思维链（CoT）推理驱动的多智能体架构。我们的方法在预测准确性上优于零样本及多数统计驱动基线，并在经济绩效上超越所有基线，在现实市场摩擦下实现了每项模因币投资平均3%的跟单者收益。总体而言，我们的结果证明了基于智能体的防御策略在对抗性模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用基础。