In quantitative finance, useful features are constructed by human experts. However, this method is of low efficient. Thus, automatic feature construction algorithms have received more and more attention. The SOTA technic in this field is to use reverse polish expression to represent the features, and then use genetic programming to reconstruct it . In this paper, we propose a new method , alpha discovery neural network , which can automatically construct features by using neural network . In this work, we made several contributions. Firstly, we put forward new object function by using empirical knowledge in financial signal processing, and we also fixed its undifferentiated problem . Secondly, we use model stealing technic to learn from other prior knowledge , which can bring enough diversity into our network . Thirdly, we come up with a method to measure the diversity of different financial features. Experiment shows that ADN can produce more diversified and higher informative features than GP. Besides, if we use GP's output to serve as prior knowledge, its final achievements will be significantly improved by using ADN .
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's 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's evolution process. According to neural network's 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 .
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quantitative finance, useful features are constructed by human experts. However, this method is of low efficient. Thus, automatic feature construction algorithms have received more and more attention. The SOTA technic in this field is to use
automatic financial feature construction task, the state of the art technic leverages
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and
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to reconstruct it
(GP) to conduct its evolution process
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method
framework based on neural network
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, which can automatically construct features by using neural network
(ADNN)
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we put forward new object function by using empirical knowledge in financial signal processing, and we also fixed its undifferentiated problem
in this task, we make full use of neural network's overwhelming advantage in feature extraction to construct highly informative features
R
model stealing technic to learn from other prior knowledge , which can bring enough diversity into our network
domain knowledge to design the object function, batch size, and sampling rules
R
come up with a method to measure the diversity of different financial features.
use pre-training to replace the GP's evolution process. According to neural network's universal approximation theorem, pre-training can conduct a more effective and explainable evolution process.
R
ADN can
ADNN can remarkably
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if we use GP's output to serve as prior knowledge, its final achievements will be significantly improved by using ADN
ADNN can serve as a data augmentation algorithm. It further improves the the performance of financial features constructed by GP