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

Understand

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.