ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

High frequency algorithm and its back-testing results based on GAN

  • 1.

    School of Management, University of Science and Technology of China, Hefei 230026, China

  • 2.

    School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.06.012
  • Received Date: 06 March 2020
  • Accepted Date: 21 June 2020
  • Rev Recd Date: 21 June 2020
  • Publish Date: 30 June 2020
    Abstract

  • Abstract

    In the financial classification mission, due to the big noise and low information-ratio in financial data, traditional supervised-learning regime may extend the noise influence because of the over dependent on the data label. GAN(generative adversarial network) can learn the data characters and reduce the influence of noise. When it is used to analyze the financial data, it has great results. We apply GAN to the high frequency trading: set the data labeled or unlabeled based on its volatility, then use the adversarial training between generative network G and discriminative network D to learn the intrinsic characters of the data, finally use the well trained D to get the up and down classification model and the quantization strategy. The sample is based on the future data, and the final results show that the LSTM model training by GAN is better than the deep learning models such as LSTM with supervised training and the Logistic regression model.

    Abstract

    In the financial classification mission, due to the big noise and low information-ratio in financial data, traditional supervised-learning regime may extend the noise influence because of the over dependent on the data label. GAN(generative adversarial network) can learn the data characters and reduce the influence of noise. When it is used to analyze the financial data, it has great results. We apply GAN to the high frequency trading: set the data labeled or unlabeled based on its volatility, then use the adversarial training between generative network G and discriminative network D to learn the intrinsic characters of the data, finally use the well trained D to get the up and down classification model and the quantization strategy. The sample is based on the future data, and the final results show that the LSTM model training by GAN is better than the deep learning models such as LSTM with supervised training and the Logistic regression model.
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    • References(24)
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    孙达昌, 毕秀春. 基于深度学习算法的高频交易策略及其盈利能力[J]. 中国科学技术大学学报, 2018, 48(11): 58-67.
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    KINGMA D P, BA J. Adam: A method for stochastic optimization[DB/OL]. [2020-02-10]. https://arxiv.org/abs/1412.6980.)
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    [1]
    王锴. 主成分Logistic回归模型在国债期货跨品种套利中的应用[N]. 期货日报, 2020-06-15.
    [2]
    ALDRIDGE I. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems[M]. Hoboken, NJ: Wiley, 2010.
    [3]
    BROGAARD J, HENDERSHOTT T, RIORDANR R. High frequency trading and price discovery[J]. The Review of Financial Studies, 2014, 27(8): 2267-2306.
    [4]
    ANGEL J, MCCABE D. Fairness in financial markets: The case of high frequency trading[J]. Journal of Business Ethis, 2013, 112: 585-595.
    [5]
    HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18: 1527-1554.
    [6]
    GOODFELLOW I, BENGIO Y, COURVILLE A. Deep Learning[M]. Cambridge, MA: The MIT Press, 2017.
    [7]
    WANG M, DENG W. Deep face recognition[C]// [2020-02-10]. https://arxiv.org/abs/1804.06655.
    [8]
    KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2014: 1725-1732.
    [9]
    DING Y, LIU Yang, LUAN H, et al. Visualizing and understanding neural machine translation[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada: ACL, 2017: 1150-1159.
    [10]
    QIAN Y, BI M, TAN T,et al. Very deep convolutional neural networks for noise robust speech recognition[C]// IEEE/ACM Transactions on Audio, Speech, and Language Processing. IEEE, 2016, 42(12): 2263-2276.
    [11]
    GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]// Advances in Neural Information Processing Systems 27. ACM, 2014.
    [12]
    SALIMANS T, GOODFELLOW I, ZAREMBA W, et al. Improved techniques for training GANs[C]// Advances in Neural Information Processing Systems 29. ACM, 2016: 2234-2242.
    [13]
    YANG J, KANNAN A, BATRA D, et al. LR-GAN: Layered recursive generative adversarial networks for image generation[DB/OL]. [2020-02-10]. https://arxiv.org/abs/1703.01560.
    [14]
    HOANG Q, NGUYEN T D, LE T, et al. MGAN: Training generative adversarial nets with multiple generators[C]// 6th International Conference on Learning Representations. La Jolla, CA: International Conference on Representation Learning, 2018.
    [15]
    PERSIO L D, HONCHAR O. Artificial neural networks architectures for stock price prediction: Comparisons and applications[J]. International Journal of Circuits, Systems and Signal Processing, 2016, 10: 403-413.
    [16]
    龙奥明, 毕秀春, 张曙光. 基于LSTM 神经网络的黑色金属期货套利策略模型[J]．中国科学技术大学学报, 2018, 48(2): 125-132.
    [17]
    杨青, 王晨蔚. 基于深度学习LSTM神经网络的全球股票指数预测研究[J]. 统计研究, 2019, 36(3): 67-79.
    [18]
    孙达昌, 毕秀春. 基于深度学习算法的高频交易策略及其盈利能力[J]. 中国科学技术大学学报, 2018, 48(11): 58-67.
    [19]
    LAKSHMINARAYANAN S K, MCCRAE J. A comparative study of SVM and LSTM deep learning algorithms for stock prediction[DB/OL]. [2020-02-10]. http://CEURWS.org/Vol-2563/aics 41.pdf.
    [20]
    DEV S, WESLEY C, FARHANA H Z. A comparative study of LSTM and DNN for stock market forecasting[C]// 2018 IEEE International Conference on Big Data. IEEE, 2018.
    [21]
    ARVAND F, SHERIDAN H. Deep learning for the prediction of stock market trends[C]// 2019 IEEE International Conference on Big Data. IEEE, 2019.
    [22]
    陆广泉, 谢扬才, 刘星,等.一种基于KNN的半监督分类改进算法[J].广西师范大学学报（自然科学版）, 2012, 30 (1): 48-52.
    [23]
    刘蓉. 半监督学习的Co-training算法研究[J]. 电脑编程技巧与维护, 2010(14): 6-7.
    [24]
    KINGMA D P, BA J. Adam: A method for stochastic optimization[DB/OL]. [2020-02-10]. https://arxiv.org/abs/1412.6980.)
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