AthenaraPapers / finmem-paper
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Proposes an LLM trading agent whose layered memory and configurable character profile let it adapt to new market information over multiple time horizons.
- llm-agent
- memory
- cognitive-architecture
- profiling
added 2026-08-15 · external
Use this
$ git clone https://github.com/pipiku915/FinMem-LLM-StockTradingPrerequisites
Details
| authors | Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Denghui Zhang, Rong Liu, Jordan W. Suchow, Khaldoun Khashanah |
|---|---|
| origin | external |
| year | 2023 |
| venue | ICLR 2024 Workshop on LLM Agents |
| arxiv | 2311.13743 |
| markets | equities |
Presents a three-module agent architecture combining profiling, layered memory processing that imitates human trader cognition, and decision-making. The design lets the agent self-evolve its domain knowledge, react to new investment cues, and refine trading decisions over time.
Experiments on real-world financial data report performance above algorithmic baselines, with results varying by the agent’s cognitive span and personality configuration — an early demonstration that memory design, not just model choice, moves trading performance.
Connections
Describes: FinMem
Extended by: InvestorBench