The rapid evolution of artificial intelligence has ushered in a transformative era for quantitative investment, driven by the emergence of autonomous AI agents. This study investigates application paradigms, challenges, and future directions of AI agents in quantitative finance through empirical analysis. We identify three core deployment frameworks: foundation models, fine-tuned systems, and retrieval-augmented generation (RAG). These frameworks enable AI agents to enhance efficiency across the investment pipeline, including signal generation, portfolio optimization, and order execution. Key innovations include multimodal data integration and human-AI collaboration. However, challenges such as integration complexity and model interpretability hinder widespread adoption. We propose that advancements in hybrid architectures and explainable AI will accelerate deployment.
Wen Shuo (Wed,) studied this question.
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