  Alphas are stock prediction models capturing trading signals in a stock
market. A set of effective alphas can generate weakly correlated high returns
to diversify the risk. Existing alphas can be categorized into two classes:
Formulaic alphas are simple algebraic expressions of scalar features, and thus
can generalize well and be mined into a weakly correlated set. Machine learning
alphas are data-driven models over vector and matrix features. They are more
predictive than formulaic alphas, but are too complex to mine into a weakly
correlated set. In this paper, we introduce a new class of alphas to model
scalar, vector, and matrix features which possess the strengths of these two
existing classes. The new alphas predict returns with high accuracy and can be
mined into a weakly correlated set. In addition, we propose a novel alpha
mining framework based on AutoML, called AlphaEvolve, to generate the new
alphas. To this end, we first propose operators for generating the new alphas
and selectively injecting relational domain knowledge to model the relations
between stocks. We then accelerate the alpha mining by proposing a pruning
technique for redundant alphas. Experiments show that AlphaEvolve can evolve
initial alphas into the new alphas with high returns and weak correlations.
