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-based 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{FinMem}, a novel LLM-based agent framework devised for financial decision-making. It encompasses three core modules: Profiling, to customize the agent's characteristics; Memory, with layered message processing, to aid the agent in assimilating hierarchical financial data; and Decision-making, to convert insights gained from memories into investment decisions. Notably, \textsc{FinMem}'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{FinMem} with various algorithmic agents on a scalable real-world financial dataset, underscoring its leading trading performance in stocks. We then fine-tuned the agent's perceptual span and character setting to achieve a significantly enhanced trading performance. Collectively, \textsc{FinMem} presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

翻译：大型语言模型（LLM）的最新进展在跨领域问答任务中展现出显著成效，其整合海量网络知识的能力推动了基于LLM的自主智能体研究。尽管LLM在解码人类指令及通过整体性处理历史输入推导解决方案方面表现高效，但向目标导向型智能体的转变仍需要补充理性架构来多源信息处理、构建推理链条并优先处理关键任务。针对这一需求，我们提出\textsc{FinMem}——一个面向金融决策的新型LLM智能体框架。该框架包含三大核心模块：档案模块（Profiling）用于定制智能体特性；记忆模块（Memory）通过分层消息处理帮助智能体吸收层级化金融数据；决策模块（Decision-making）将记忆洞察转化为投资决策。值得注意的是，\textsc{FinMem}的记忆模块与人类交易者的认知结构高度契合，具备强可解释性与实时调参能力。其可调节的认知跨度可保留超越人类感知极限的关键信息，从而提升交易绩效。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，并在波动性金融环境中持续优化交易决策。我们首先在可扩展的真实金融数据集上将\textsc{FinMem}与多种算法智能体进行对比，突显其在股票交易中的领先性能；随后通过精细调整智能体的感知跨度与性格参数，显著提升了交易表现。综上，\textsc{FinMem}为自动化交易提供了前沿的LLM智能体框架，有力提升了累积投资回报。