FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
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References listed on IDEAS
- Eero P䴤ri & Mika Vilska, 2014. "Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2851-2872, August.
- Fischer, Thomas G., 2018. "Reinforcement learning in financial markets - a survey," FAU Discussion Papers in Economics 12/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
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- Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Jun 2024.
- Xiangyu Li & Yawen Zeng & Xiaofen Xing & Jin Xu & Xiangmin Xu, 2025. "HedgeAgents: A Balanced-aware Multi-agent Financial Trading System," Papers 2502.13165, arXiv.org.
- Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
- Yupeng Cao & Zhi Chen & Qingyun Pei & Fabrizio Dimino & Lorenzo Ausiello & Prashant Kumar & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data," Papers 2404.07452, arXiv.org.
- Tao Ren & Ruihan Zhou & Jinyang Jiang & Jiafeng Liang & Qinghao Wang & Yijie Peng, 2024. "RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search," Papers 2402.07080, arXiv.org, revised Feb 2024.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2024-01-08 (Artificial Intelligence)
- NEP-CMP-2024-01-08 (Computational Economics)
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