Before revision:  In this work, we put prior knowledge into alpha discovery neural network, combined with different kinds of feature extractors to do this task. We find that in the same type of network, simple network structure can produce more informative features than sophisticated network structure, and it costs less training time. However, complex network is good at providing more diversified features. In both experiment and real business environment, fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information  .
After revision:  In this work, we put prior knowledge into alpha discovery neural network, combined with different kinds of feature extractors to construct diversified financial technical factors based on prior knowledge. The experiment result shows that both fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information .