AlphaEvolve overview.
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AlphaEvolve overview.
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• We first introduce a new class of alphas with intriguingstrengths: like formulaic alphas, these alphas can modelscalar features and thus are simple to mine into a weaklycorrelated set, but, like machine learning alphas, they arehigh-dimensional data-driven models utilizing long-termfeatures. We then propose a novel alpha mining framework,AlphaEvolve, to generate the new alphas. To the best of ourknowledge, we are the first to solve the stock prediction problem based on AutoML and the first to tackle the problem ofmining weakly correlated alphas.• We enable AlphaEvolve to selectively inject relational domain knowledge without any strong structural assumptionin an alpha.• We propose an optimization technique to accelerate alphamining by pruning redundant alphas.• We conduct extensive experimental study on AlphaEvolveusing the stock price data of NASDAQ. The results show thatAlphaEvolve generates alphas with weakly correlated highreturns.
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