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This paper explores the latest methodologies for fine-tuning open-source Large Language Models (LLMs) in enhancing quantitative trading strategies by integrating numerical data (e.g., historical prices, technical indicators) with textual data (e.g., news, earnings reports, social media sentiment). We employ Retrieval-Augmented Generation (RAG) with a vector database to efficiently handle and contextualize textual data, alongside Low-Rank Adaptation (LoRA) techniques for cost-effective and scalable model fine-tuning. The proposed approach aims to create a hybrid trading model that combines the predictive power of LLMs with traditional quantitative methods, improving accuracy and adaptability in financial markets. This study details the implementation process, highlighting practical innovations such as the integration of real-time data pipelines and adaptive model tuning. Experimental results show significant improvements in predictive accuracy and risk-adjusted returns, demonstrating the practical value of these advanced fine-tuning methodologies in finance.

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Advancing Quantitative Trading Strategies Using Fine-Tuned Open-Source Large Language Models: A Hybrid Approach with Numerical and Textual Data Integration Using RAG and LoRA Techniques

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  • 发表日期：

    2025年

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  • Seth H. Huang,
  • Jimin Kim,
  • Ka Lok Kellogg Wong

摘要

This paper explores the latest methodologies for fine-tuning open-source Large Language Models (LLMs) in enhancing quantitative trading strategies by integrating numerical data (e.g., historical prices, technical indicators) with textual data (e.g., news, earnings reports, social media sentiment). We employ Retrieval-Augmented Generation (RAG) with a vector database to efficiently handle and contextualize textual data, alongside Low-Rank Adaptation (LoRA) techniques for cost-effective and scalable model fine-tuning. The proposed approach aims to create a hybrid trading model that combines the predictive power of LLMs with traditional quantitative methods, improving accuracy and adaptability in financial markets. This study details the implementation process, highlighting practical innovations such as the integration of real-time data pipelines and adaptive model tuning. Experimental results show significant improvements in predictive accuracy and risk-adjusted returns, demonstrating the practical value of these advanced fine-tuning methodologies in finance.