Paper

Formulaic alpha factor mining hits a wall once the library grows past a few dozen factors: new candidates have decent IC, but correlation against the existing library always exceeds the threshold, so nothing gets admitted. AlphaGPT, AlphaForge, AlphaAgent, and QuantFactor REINFORCE all attack the “generate more candidates” side, while the “the bigger the library, the harder to add new stock” problem still gets little treatment. FactorMiner (A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discovery, 2026/02, arXiv 2602.14670) wraps the mining loop into an agent skill and maintains an experience memory that records “which directions kept hitting walls, which templates keep paying off,” so every round the agent samples with a prior built from past wins and losses. On CSI500, the top-40 library reaches IC 8.25% and ICIR 0.77, about 40% relative to AlphaAgent’s 5.90%/0.46. ...

AlphaAgent tackled “LLM-generated factors look too similar, accelerating alpha decay” with factor-level regularization. QuantaAlpha (arXiv 2602.07085) operates one level up: instead of constraining a single generation, treat the full “hypothesis → factor → backtest” pipeline as a trajectory, and apply mutation and crossover between trajectories. On CSI 300 with GPT-5.2 it lands IC 0.0472, ARR 4.68%, MDD 11.8%. Zero-shot transfer to CSI 500 and S&P 500 keeps 40.28% and 19.1% cumulative excess return respectively. ...

The previous AlphaGPT review left an open question: when everyone uses LLMs to mine factors, how long can those factors stay effective? AlphaAgent (paper, KDD 2025) tackles this head-on. Its core observation: LLM-generated factors lean too heavily on existing knowledge, producing homogeneous signals that crowd the same trades and accelerate alpha decay. The fix is three regularization constraints injected into the factor generation process, forcing the model to explore structurally novel, logically coherent, and complexity-controlled factors. ...

One of the core tasks in quantitative investing is mining alpha factors — finding signals that predict asset returns. The traditional approach relies on researchers manually constructing factor expressions, or using automated search methods like Genetic Programming (GP) to brute-force combinations in the operator space. The former depends on human experience and intuition — low efficiency but high interpretability. The latter is efficient but produces deeply nested operator expressions that are nearly impossible for researchers to interpret. AlphaGPT (paper) brings large language models into the factor mining pipeline, using an LLM as the factor “generator.” The follow-up work, AlphaGPT 2.0 (paper), further introduces a human-in-the-loop closed cycle. ...