The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and less transparent decision-making. Therefore, we propose a multi-agent LLM trading framework that explicitly decomposes investment analysis into fine-grained tasks, rather than providing coarse-grained instructions. We evaluate the proposed framework using Japanese stock data, including prices, financial statements, news, and macro information, under a leakage-controlled backtesting setting. Experimental results show that fine-grained task decomposition significantly improves risk-adjusted returns compared to conventional coarse-grained designs. Crucially, further analysis of intermediate agent outputs suggests that alignment between analytical outputs and downstream decision preferences is a critical driver of system performance. Moreover, we conduct standard portfolio optimization, exploiting low correlation with the stock index and the variance of each system's output. This approach achieves superior performance. These findings contribute to the design of agent structure and task configuration when applying LLM agents to trading systems in practical settings.

翻译：大语言模型（LLM）的进步加速了自主金融交易系统的发展。尽管主流方法部署了模仿分析师和经理角色的多智能体系统，但它们通常依赖于抽象的指令，忽视了现实世界工作流程的复杂性，这可能导致推理性能下降和决策透明度降低。因此，我们提出了一种多智能体LLM交易框架，该框架明确将投资分析分解为细粒度任务，而非提供粗粒度的指令。我们在一个泄漏可控的回测设置下，使用日本股票数据（包括价格、财务报表、新闻和宏观信息）对所提出的框架进行了评估。实验结果表明，与传统的粗粒度设计相比，细粒度任务分解显著提高了风险调整后收益。关键的是，对智能体中间输出的进一步分析表明，分析输出与下游决策偏好之间的一致性，是系统性能的关键驱动因素。此外，我们进行了标准的投资组合优化，利用了与股票指数的低相关性以及每个系统输出的方差。这种方法取得了卓越的性能。这些发现为在实际场景中将LLM智能体应用于交易系统时，智能体结构和任务配置的设计提供了参考。