From Deep Learning to LLMs: A Survey of AI in Quantitative Investment

The survey takes alpha strategy as its representative example and follows how AI has entered each stage of the quantitative investment pipeline. It moves from human-crafted features and traditional statistical models, through deep learning applied across the whole pipeline from data processing to order execution, to large language models empowering autonomous agents that process unstructured data, generate alphas, and support self-iterative workflows. The authors are at HKUST (Guangzhou), HKUST, and IDEA Research, with Jian Guo as corresponding author.

Its practical value here is as a map. The text discusses FinMem, FinAgent, TradingAgents, FinRobot, FinGPT, PIXIU and InvestorBench by name — all indexed in this registry — alongside Alpha-GPT and BloombergGPT, describing FinMem as the framework that established the three-component agent architecture, TradingAgents as a multi-agent system for financial trading, and InvestorBench as a benchmark for evaluating LLM-based agents. Two obvious neighbours are absent: neither Qlib nor FinRL is mentioned anywhere in the paper.

Treat it strictly as a 2025 arXiv preprint (arXiv:2503.21422). It was submitted in March 2025 with no later revisions, carries no journal reference, DOI or peer-reviewed version, and has no code or companion repository of any kind. Citations remain in the low single digits, so its standing rests on its coverage rather than on uptake.