Human-in-the-Loop Alpha: How Alpha-GPT 2.0 Rewrites the Quant Research Engine
From factor mining to risk filtering, a multi-agent LLM architecture that compresses the entire quantitative investment pipeline into a faster, smarter, and continuously learning system
Quantitative investment has always promised industrial scale intelligence. In theory, data flows in, alphas are mined, models are trained, portfolios are optimized, and capital compounds with discipline. In reality, the bottleneck has never been data. It has been research capacity.
The study on Alpha-GPT 2.0 introduces a new paradigm that directly attacks this bottleneck: Human-in-the-Loop AI for Quantitative Investment. Instead of replacing researchers with brute force automation, it proposes something far more strategic. It builds an interactive, multi-agent system where large language models and human experts co-drive the entire quant pipeline, from alpha discovery to risk filtering.
