AI正推动量化投资从人工特征到智能代理的范式变革
From Deep Learning to LLMs: A survey of AI in Quantitative Investment
- 以选股策略为例，展示AI如何贯穿数据处理到交易执行全流程
- 深度学习实现全链条自动化建模，提升预测与执行效率
- 大模型使系统能自主处理非结构化数据并生成投资策略
量化投资是技术驱动的资产管理新范式，正受到人工智能发展的深刻影响。近年来，深度学习与大语言模型（LLMs）在量化金融中的应用显著提升了预测建模能力，并实现了基于智能体的自动化，预示着该领域的潜在范式转变。本文以阿尔法策略为代表，探讨AI如何融入量化投资全流程。首先回顾早期量化研究阶段，以人工设计特征和传统统计模型为基础的成熟阿尔法开发流程。随后分析深度学习的兴起，其使模型可规模化应用于从数据处理到订单执行的全链条。在此基础上，重点阐述大语言模型在扩展AI能力方面的新兴作用：不仅限于预测，更支持智能体处理非结构化数据、生成阿尔法信号，并实现自我迭代的工作流。
原文摘要 · Abstract (English)
Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows.
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