Learning Symbolically Composable Representations for Formulaic Alpha Factor Mining

Formulaic alpha mining seeks explicit scoring expressions that rank stocks by future returns. Its effectiveness depends on both the search algorithm and the input variables from which formulas are constructed. We investigate whether learning suitable representations before symbolic search can improve alpha discovery. We propose a two-stage framework that combines a feature-axis encoder (FAE) with genetic programming (GP). The FAE uses feature-axis convolutions and coarse-grained return classification to learn low-dimensional representations.GP then performs fine-grained search for alpha formulas over the learned features, using RankIC to evaluate candidate formulas. Experiments on CSI500 and CSI1000 show that the proposed framework improves out-of-sample predictive performance compared with symbolic search over raw indicators. Controlled comparative experiments and ablation studies further support the contribution of the learned representations beyond dimensionality reduction and validate the proposed encoder design. The resulting formulas expose how learned features are combined, providing inspectable scoring rules at the representation level. These findings highlight the value of learning suitable search variables for formulaic alpha discovery.