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Automate Strategy Finding with LLM in Quant Investment

Author

Listed:
  • Zhizhuo Kou
  • Holam Yu
  • Junyu Luo
  • Jingshu Peng
  • Xujia Li
  • Chengzhong Liu
  • Juntao Dai
  • Lei Chen
  • Sirui Han
  • Yike Guo

Abstract

We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17% cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market.

Suggested Citation

  • Zhizhuo Kou & Holam Yu & Junyu Luo & Jingshu Peng & Xujia Li & Chengzhong Liu & Juntao Dai & Lei Chen & Sirui Han & Yike Guo, 2024. "Automate Strategy Finding with LLM in Quant Investment," Papers 2409.06289, arXiv.org, revised May 2025.
  • Handle: RePEc:arx:papers:2409.06289
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    File URL: http://arxiv.org/pdf/2409.06289
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    References listed on IDEAS

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    1. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    2. Feng Zhang & Ruite Guo & Honggao Cao, 2020. "Information Coefficient as a Performance Measure of Stock Selection Models," Papers 2010.08601, arXiv.org.
    3. Lezhi Li & Ting-Yu Chang & Hai Wang, 2023. "Multimodal Gen-AI for Fundamental Investment Research," Papers 2401.06164, arXiv.org.
    4. 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.
    5. Yi Yang & Yixuan Tang & Kar Yan Tam, 2023. "InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning," Papers 2309.13064, arXiv.org.
    6. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    7. Li, Weiping & Mei, Feng, 2020. "Asset returns in deep learning methods: An empirical analysis on SSE 50 and CSI 300," Research in International Business and Finance, Elsevier, vol. 54(C).
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    Cited by:

    1. Yichen Luo & Yebo Feng & Jiahua Xu & Paolo Tasca & Yang Liu, 2025. "LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management," Papers 2501.00826, arXiv.org, revised Jan 2025.
    2. Junhua Liu, 2024. "A Survey of Financial AI: Architectures, Advances and Open Challenges," Papers 2411.12747, arXiv.org.
    3. Kuan-Ming Liu & Ming-Chih Lo, 2025. "LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading," Papers 2501.09636, arXiv.org, revised Jan 2025.

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