用多智能体融合金融多模态数据，提升交易收益与抗噪能力
F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading
- 分层专用智能体提取各模态信号，动态融合跨模态依赖
- 在6种资产上平均年化收益率提升超20%，最高达148.41%
- 适合关注多模态金融建模与量化交易的开发者
随着信息来源日益多样且异构，高效利用多模态数据对高质量金融交易至关重要。尽管基于大语言模型（LLM）的智能体已能处理多模态输入，现有方法仍难以捕捉细粒度跨模态依赖，且易受市场噪声干扰，原因在于多模态建模能力有限、融合机制低效以及鲁棒性不足。为此，我们提出F²Agent，一种由金融智能体融合驱动的新颖多模态代理范式。F²Agent首先部署分层专用智能体以全面提取模态特异性信号；进一步引入模态感知自适应融合机制与噪声鲁棒一致性正则化，动态捕捉细粒度跨模态依赖并生成抗噪交易信号。在六只股票及加密货币资产上的大量实验表明，F²Agent在多个交易指标上持续优于16个基线模型，平均年化收益率相对提升超过20%。值得注意的是，其在GOOG上实现120.48%的回报率，在TSLA上达到148.41%，充分展现其在不同市场动态下的有效性与鲁棒性。
原文摘要 · Abstract (English)
With increasingly diverse and heterogeneous information sources, effectively leveraging multimodal data is becoming pivotal for high-quality financial trading. Although recent advancements in Large Language Model (LLM)-based agents have enabled the ingestion of multimodal inputs, existing methods fail to capture nuanced cross-modal dependencies and remain vulnerable to market noise, due to limited multimodal modeling, ineffective fusion mechanisms, and inadequate robustness. To address these challenges, we propose F$^2$Agent, a novel multimodal agentic paradigm driven by the Financial Fusion of Agentic Intelligence. F$^2$Agent first deploys a hierarchy of specialized agents to comprehensively extract modality-specific signals. It further introduces a modality-aware adaptive fusion mechanism coupled with noise-robust consistency regularization to dynamically capture fine-grained inter-modality dependencies and generate noise-resilient trading signals. Extensive experiments on six stocks and cryptocurrency assets demonstrate that F$^2$Agent consistently outperforms 16 competitive baselines across multiple trading metrics, with over 20% relative improvement in annualized return on average. Notably, F$^2$Agent delivers returns of 120.48% on GOOG and 148.41% on TSLA, demonstrating its efficacy and robustness in varying market dynamics.
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