Before revision:  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.
After revision:  We mainly made three contributions. First, we use domain knowledge in quantitative trading to design the sampling rules and object function. Second, pre-training and model pruning has been used to replace genetic programming, because it can conduct more efficient evolution process. Third, the feature extractors in ADNN can be replaced by different feature extractors and produce different functions. The experiment results show that ADNN can construct more informative and diversified features than GP, which can effectively enriches the current factor pool. The 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.