  In automatic financial feature construction task, the state-of-the-art
technic leverages reverse polish expression to represent the features, then use
genetic programming (GP) to conduct its evolution process. In this paper, we
propose a new framework based on neural network, alpha discovery neural network
(ADNN). In this work, we made several contributions. Firstly, in this task, we
make full use of neural network overwhelming advantage in feature extraction to
construct highly informative features. Secondly, we use domain knowledge to
design the object function, batch size, and sampling rules. Thirdly, we use
pre-training to replace the GP evolution process. According to neural network
universal approximation theorem, pre-training can conduct a more effective and
explainable evolution process. Experiment shows that ADNN can remarkably
produce more diversified and higher informative features than GP. Besides, ADNN
can serve as a data augmentation algorithm. It further improves the the
performance of financial features constructed by GP.
