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Mining profitable alpha factors via convolution kernel learning

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Abstract

An automatic alpha factor mining method is proposed in this paper to assist expert traders in finding profitable alpha factors efficiently. Unlike finding qualified alpha factors by directly enumerating all possible combinations, the mining task is formulated as an iterative convolution kernel learning problem. Each kernel to be learned is associated with a unique alpha factor. To better solve the learning problem, the sparsity is introduced at the mutation step of the learning process to find simple and interpretable solutions efficiently and relieve the overfitting risks in real-world trading. A theorem is proposed to prove that the designed learning process can complete automatically in finite iterations as all convolution kernel vectors converge to zero vectors. In addition, a score function based on win rate, expected return and trade frequency is designed to evaluate the performance of market entry signals generated by the alpha factors practically. The convolution kernels with high score values are recorded and exported as the mined alpha factors. The experiment results show that the proposed method can achieve superior performance on both the China government bond dataset and the gold dataset.

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Availability of data and materials

The datasets generated and analyzed during the current study are available from the corresponding author at reasonable request and can be downloaded at https://github.com/szy1900/autoAlpha/

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

    Zhenyi Shen, Xiaohu Yang & Dan Zhao

  2. Bank of Hangzhou Co., Ltd., Hangzhou, Zhejiang, People’s Republic of China

    Zhenyi Shen, Xiahong Mao & Dan Zhao

Authors
  1. Zhenyi Shen
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  2. Xiahong Mao
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  3. Xiaohu Yang
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  4. Dan Zhao
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Correspondence to Dan Zhao.

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Appendix

Appendix

1.1 A. List of abbreviations

The abbreviations used in this paper are summarized in Table 10.

Table 10 List of abbreviations
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1.2 B. List of symbols

The symbols used in this paper are summarized in Table 11.

Table 11 List of symbols
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1.3 C. Summary of 28 basic patterns

The name of all 28 basic patterns used as the columns of each input are provided in Table 12. For a given trading day, the possible value for each basic pattern is either 0 or 1, indicating the occurrence and absence of the basic pattern, respectively.

Table 12 The name of 28 basic patterns used in the input matrix
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Shen, Z., Mao, X., Yang, X. et al. Mining profitable alpha factors via convolution kernel learning. Appl Intell 53, 28460–28478 (2023). https://doi.org/10.1007/s10489-023-05014-4

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