In financial automatic feature construction task, genetic programming is the state-of-the-art-technic. It uses reverse polish expression to represent features and then uses genetic programming to simulate the evolution process. With the development of deep learning, there are more powerful feature extractors for option. And we think that comprehending the relationship between different feature extractors and data shall be the key. 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  .
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 .
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there are more powerful feature extractors for option. And we think that comprehending the relationship between different feature extractors and data shall be the key. In this work, we put prior knowledge into alpha discovery neural network, combined
more choices to design this algorithm are available. This paper proposes Alpha Discovery Neural Network (ADNN), equipped
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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,
construct diversified financial technical factors based on prior knowledge. The experiment result shows that both
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. 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