In financial automatic feature construction task, genetic programming (GP) is the state-of-the-art technique . It employs reverse polish expression to represent features and then simulate the evolution process. However, with the development of deep learning, more choices to design this algorithm are available. This paper proposes Alpha Discovery Neural Network (ADNN), equipped 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. ADNN effectively enrich the current factor pool because in all cases, ADNN can construct more informative and diversified features than GP. Moreover, features constructed by ADNN can always improve original strategy return, Sharpe ratio and max draw-down  .
Genetic programming (GP) is the state-of-the-art in financial automated feature construction task . It employs reverse polish expression to represent features and then conducts the evolution process. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge. 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 better at extracting information from the financial time series than the convolution neural network. In real practice, features constructed by ADNN can always improve multi-factor strategies' revenue, sharpe ratio, and max draw-down , compared with the investment strategies without these factors .
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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
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, but convolution network structure can not effectively extract this information. ADNN effectively enrich the current factor pool because in all cases, ADNN can construct more informative and diversified features than GP. Moreover, features
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