AlphaPSC: A Pool-Subspace Conditioned Generative Framework for Complementary Formulaic Alpha Mining

Formulaic alphas are symbolic predictive expressions widely used in quantitative trading, where newly discovered factors are typically integrated into an existing factor pool and linearly combined into a composite trading signal. Consequently, an effective factor must provide incremental predictive information beyond what the pool already captures. However, existing mining frameworks often generate candidates independently of the evolving factor library, yielding redundant and highly correlated representations. We propose AlphaPSC, a $\text{P}$ool-$\text{S}$ubspace $\text{C}$onditioned generative framework for complementary formulaic alpha mining. AlphaPSC conditions generation on a continuous summary of the predictive directions already occupied by the current pool, and scores candidates by their incremental predictive contribution beyond that pool. On CSI300, CSI500, and CSI1000 under a shared expanding-window combiner with validation-selected and frozen hyperparameters, AlphaPSC improves combined-factor correlation and frictionless backtest metrics over representative formulaic miners. CSI300 ablations confirm that pool-subspace conditioning is important for these gains.