Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.

翻译：大型语言模型（LLM）与智能体系统的最新进展展现出卓越的决策能力，为自主金融领域揭示了巨大潜力。当前金融交易智能体主要模拟拟人化角色，这无意中引入了情感偏差并依赖于边缘信息，同时在部署过程中受限于持续推理的必要性。本文率先实现了智能体战略深度与量化交易所需机械理性之间的协调。为此，我们提出TiMi（Trade in Minutes）——一个理性驱动的多智能体系统，其架构将策略开发与分钟级部署解耦。TiMi在完整的“策略-优化-部署”链中，充分利用了LLM在语义分析、代码编程与数学推理方面的专项能力。具体而言，我们提出了从宏观模式到微观定制的双层分析范式、用于交易机器人实现的分层编程设计，以及由数学反思驱动的闭环优化机制。在股票与加密货币市场超过200个交易对上进行的广泛评估，实证验证了TiMi在波动市场环境下实现稳定盈利、行动效率与风险控制的有效性。