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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
| Date | Stars |
|---|---|
| 2026-07-31 | 931 |
| 2026-08-02 | 932 |
| 2026-08-06 | 932 |
Today
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growth rate 0.00%/day
# FINMEM: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
[](https://www.python.org/downloads/release/python-3100/) [](https://opensource.org/licenses/MIT) [](https://github.com/ambv/black) [](https://arxiv.org/abs/2311.13743)
```text
"So we beat on, boats against the current, borne back ceaselessly into the past."
-- F. Scott Fitzgerald: The Great Gatsby
```
This repo provides the Python source code for the paper:
[FINMEM: A Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design](https://arxiv.org/abs/2311.13743) [[PDF]](https://arxiv.org/pdf/2311.13743.pdf)
```bibtex
@misc{yu2023finmem,
title={FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design},
author={Yangyang Yu and Haohang Li and Zhi Chen and Yuechen Jiang and Yang Li and Denghui Zhang and Rong Liu and Jordan W. Suchow and Khaldoun Khashanah},
year={2023},
eprint={2311.13743},
archivePrefix={arXiv},
primaryClass={q-fin.CP}
}
```
**📢 Update (Date: 01-16-2024)**
🚀 We're excited to share that our work, "FINMEM: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design," has been selected for an extended abstract at the AAAI Spring Symposium on Human-Like Learning!
**📢 Update (Date: 03-11-2024)**
🚀 We're thrilled to announce that our paper, "FINMEM: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design", has been accepted by ICLR Workshop LLM Agents!
**📢 Update (Date: 06-16-2024)**
🎉 Thank you to all the participants and organizers of the IJCAI2024 challenge, "Financial Challenges in Large Language Models - FinLLM". Our team, FinMem, was thrilled to contribute to Task 3: Single Stock Trading.
As the challenge wrapped up yesterday (06/15/2024), we reflect on the innovative approaches and insights gained throughout this journey. A total of 12 teams participated, each bringing unique perspectives and solutions to the forefront of financial AI and Large Language Models.
We invite the community to continue engaging with us as we look forward to further developments and collaborations in this exciting field.
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, 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, 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 Excerpt of 10,819 characters
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matched fp:cf0f630cbc7e5dbb, llm:Repository description: 'FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design' (Python).
matched fp:cf0f630cbc7e5dbb, llm:Repository description: 'FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design' (Python).