用轻量大模型和分层强化学习，融合新闻情绪与市场数据优化投资组合。
HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
- 分三层结构：基础智能体处理多源数据，元智能体聚合决策，超智能体综合市场与情绪信号。
- 2018-2024年回测中年化收益26%，夏普比率1.2，优于基准和等权策略。
- 适合关注金融量化、多模态融合与可复现AI投资的读者。
本文提出一种新型分层框架用于投资组合优化，将轻量级大语言模型（LLM）与深度强化学习（DRL）结合，融合金融新闻情绪信号与传统市场指标。该三阶段架构包含：基础强化学习智能体处理混合数据，元智能体聚合其决策，超智能体基于市场数据与情感分析融合决策。在2018至2024年数据上评估，训练期为2000-2017年，框架实现26%的年化收益率与1.2的夏普比率，优于等权重及标普500基准。主要贡献包括可扩展的跨模态融合机制、提升稳定性的分层强化学习结构，以及开源可复现性。
原文摘要 · Abstract (English)
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL agents to process hybrid data, meta-agents to aggregate their decisions, and a super-agent to merge decisions based on market data and sentiment analysis. Evaluated on data from 2018 to 2024, after training on 2000-2017, the framework achieves a 26% annualized return and a Sharpe ratio of 1.2, outperforming equal-weighted and S&P 500 benchmarks. Key contributions include scalable cross-modal integration, a hierarchical RL structure for enhanced stability, and open-source reproducibility.
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