Recent advancements in Large Language Models (LLMs) have exhibited notable efficacy in question-answering (QA) tasks across diverse domains. Their prowess in integrating extensive web knowledge has fueled interest in developing LLM autonomous agents. While LLMs are efficient in decoding human instructions and deriving solutions by holistically processing historical inputs, transitioning to purpose-driven agents requires a supplementary rational architecture to process multi-source information, establish reasoning chains, and prioritize critical tasks. Addressing this, we introduce \textsc{FinMe}, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, to outline the agent's characteristics; Memory, with layered processing, to aid the agent in assimilating realistic hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{FinMe}'s memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. Its adjustable cognitive span allows for the retention of critical information beyond human perceptual limits, thereby enhancing trading outcomes. This framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. We first compare \textsc{FinMe} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks and funds. We then fine-tuned the agent's perceptual spans to achieve a significant trading performance. Collectively, \textsc{FinMe} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

翻译：近年来，大型语言模型（LLMs）在跨领域问答任务中展现出显著效能。其整合海量网络知识的能力激发了针对LLM自主智能体的研究兴趣。尽管LLM在处理人类指令、通过整体分析历史输入得出解决方案方面表现高效，但转向目标驱动型智能体仍需补充理性架构以处理多源信息、建立推理链并优先处理关键任务。针对这一问题，我们提出\textsc{FinMe}——一种专为金融决策设计的基于LLM的新型智能体框架，包含三个核心模块：特征刻画模块（Profiling），用于勾勒智能体特性；分层记忆模块（Memory），通过分层处理帮助智能体吸收现实中的层级化金融数据；以及决策制定模块（Decision-making），将记忆所获洞察转化为投资决策。值得注意的是，\textsc{FinMe}的记忆模块与人类交易者的认知结构高度契合，具备强大的可解释性与实时调优能力。其可调节的认知跨度可保留超出人类感知极限的关键信息，从而提升交易效果。该框架使智能体能够自主进化专业知识，敏捷响应新投资信号，并在波动的金融环境中持续优化交易决策。我们首先将\textsc{FinMe}与多种算法智能体在可扩展的真实金融数据集上进行对比，凸显其在股票与基金交易中的领先性能。随后，通过微调智能体的感知跨度，我们实现了显著的交易性能提升。总体而言，\textsc{FinMe}为自动化交易提供了一种前沿的LLM智能体框架，有效提高了累计投资收益。