  The multi-factor model is a widely used model in quantitative investment. The
success of a multi-factor model is largely determined by the effectiveness of
the alpha factors used in the model. This paper proposes a new evolutionary
algorithm called AutoAlpha to automatically generate effective formulaic alphas
from massive stock datasets. Specifically, first we discover an inherent
pattern of the formulaic alphas and propose a hierarchical structure to quickly
locate the promising part of space for search. Then we propose a new Quality
Diversity search based on the Principal Component Analysis (PCA-QD) to guide
the search away from the well-explored space for more desirable results. Next,
we utilize the warm start method and the replacement method to prevent the
premature convergence problem. Based on the formulaic alphas we discover, we
propose an ensemble learning-to-rank model for generating the portfolio. The
backtests in the Chinese stock market and the comparisons with several
baselines further demonstrate the effectiveness of AutoAlpha in mining
formulaic alphas for quantitative trading.
