FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design


  • Yangyang Yu Stevens Institute of Technology
  • Haohang Li Stevens Institute of Technology
  • Zhi Chen Stevens Institute of Technology
  • Yuechen Jiang Stevens Institute of Technology
  • Yang Li Stevens Institute of Technology
  • Denghui Zhang Stevens Institute of Technology
  • Rong Liu Stevens Institute of Technology
  • Jordan W. Suchow Stevens Institute of Technology
  • Khaldoun Khashanah Stevens Institute of Technology



Financial AI, Large Language Models, Trading Algorithms, Generative AI, Cognitive Science, Financial Technology


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 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, 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 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, FinMem presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.






Symposium on Human-Like Learning