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Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading

Author

Listed:
  • Wo Long
  • Wenxin Zeng
  • Xiaoyu Zhang
  • Ziyao Zhou

Abstract

This research develops a sentiment-driven quantitative trading system that leverages a large language model, FinGPT, for sentiment analysis, and explores a novel method for signal integration using a reinforcement learning algorithm, Twin Delayed Deep Deterministic Policy Gradient (TD3). We compare the performance of strategies that integrate sentiment and technical signals using both a conventional rule-based approach and a reinforcement learning framework. The results suggest that sentiment signals generated by FinGPT offer value when combined with traditional technical indicators, and that reinforcement learning algorithm presents a promising approach for effectively integrating heterogeneous signals in dynamic trading environments.

Suggested Citation

  • Wo Long & Wenxin Zeng & Xiaoyu Zhang & Ziyao Zhou, 2025. "Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading," Papers 2510.10526, arXiv.org.
    • Handle: RePEc:arx:papers:2510.10526
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    File URL: http://arxiv.org/pdf/2510.10526
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    References listed on IDEAS

    as
    1. Paul Glasserman & Caden Lin, 2023. "Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis," Papers 2309.17322, arXiv.org.
    2. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
    3. Paul Glasserman & Harry Mamaysky & Jimmy Qin, 2023. "New News is Bad News," Papers 2309.05560, arXiv.org.
    4. Ziyao Zhou & Ronitt Mehra, 2025. "An End-To-End LLM Enhanced Trading System," Papers 2502.01574, arXiv.org.
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