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The multi-factor model is a widely used model in quantitative investment. Thesuccess of a multi-factor model is largely determined by the effectiveness ofthe alpha factors used in the model. This paper proposes a new evolutionaryalgorithm called AutoAlpha to automatically generate effective formulaic alphasfrom massive stock datasets. Specifically, first we discover an inherentpattern of the formulaic alphas and propose a hierarchical structure to quicklylocate the promising part of space for search. Then we propose a new QualityDiversity search based on the Principal Component Analysis (PCA-QD) to guidethe search away from the well-explored space for more desirable results. Next,we utilize the warm start method and the replacement method to prevent thepremature convergence problem. Based on the formulaic alphas we discover, wepropose an ensemble learning-to-rank model for generating the portfolio. Thebacktests in the Chinese stock market and the comparisons with severalbaselines further demonstrate the effectiveness of AutoAlpha in miningformulaic alphas for quantitative trading.

AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment