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This paper explores the latest methodologies for fine-tuning open-source Large Language Models (LLMs) to enhance 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, enabling LLMs to derive actionable insights from both structured and unstructured data. The proposed approach focuses on fully fine-tuning smaller models, such as GPT-4o Mini, for cost-effective and scalable applications in finance. The study 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. Key innovations include the integration of real-time data pipelines and adaptive model tuning. Experimental results demonstrate significant improvements in predictive accuracy and risk-adjusted returns, showcasing the practical value of these advanced fine-tuning methodologies in finance.

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Integrating LLM-Based Time Series and Regime Detection with RAG for Adaptive Trading Strategies and Portfolio Management

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  • 发表日期：

    2025年

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  • Chenkai Li,
  • Chi Ho Roger Chan,
  • Seth H. Huang,
  • Paul Moon Sub Choi

摘要

This paper explores the latest methodologies for fine-tuning open-source Large Language Models (LLMs) to enhance 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, enabling LLMs to derive actionable insights from both structured and unstructured data. The proposed approach focuses on fully fine-tuning smaller models, such as GPT-4o Mini, for cost-effective and scalable applications in finance. The study 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. Key innovations include the integration of real-time data pipelines and adaptive model tuning. Experimental results demonstrate significant improvements in predictive accuracy and risk-adjusted returns, showcasing the practical value of these advanced fine-tuning methodologies in finance.