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CN 34-1054/N

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High-frequency trading strategies based on deep learning algorithms and their profitability

  • Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2018.11.008
  • Received Date: 15 March 2018
  • Accepted Date: 30 May 2018
  • Rev Recd Date: 30 May 2018
  • Publish Date: 30 November 2018
    Abstract

  • Abstract

    As an important algorithm, deep learning has been applied successfully to image processing, speech recognition, machine translation and other fields. Here, deep learning algorithms were applied to high-frequency trading. Convolutional neural network(CNN) and long short-term memory(LSTM) neural network were selected to build up and down classification models, respectively. Based on the models, high-frequency trading strategies were proposed. Then the data of bitumen futures contract was used for back-testing and empirically analyzing the superiority of the strategies. In back-testing, deep learning algorithms were compared with artificial neural network(ANN). The results show that both strategies based on CNN and LSTM neural network exhibit better profitability and generalization ability. In addition, the winning rates and expected returns of the two strategies are also better.

    Abstract

    As an important algorithm, deep learning has been applied successfully to image processing, speech recognition, machine translation and other fields. Here, deep learning algorithms were applied to high-frequency trading. Convolutional neural network(CNN) and long short-term memory(LSTM) neural network were selected to build up and down classification models, respectively. Based on the models, high-frequency trading strategies were proposed. Then the data of bitumen futures contract was used for back-testing and empirically analyzing the superiority of the strategies. In back-testing, deep learning algorithms were compared with artificial neural network(ANN). The results show that both strategies based on CNN and LSTM neural network exhibit better profitability and generalization ability. In addition, the winning rates and expected returns of the two strategies are also better.
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    • References(34)
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    HUBEL D H, WIESEL T N. Receptive fields,binocular and functional architecture in the cat’s visual cortex[J]. The Journal of Physiology, 1962, 160(1): 106-154.
    [29]
    FUKUSHIMA K.Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J]. Bioological Cybernetics, 1980, 36(4): 193-202.
    [30]
    LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [31]
    HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[EB/OL]. [2012-07-03] https://arxiv.org/abs/1207.0580.
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    GRAVES A. Supervised Sequence Labelling with Recurrent Neural Networks[M]. Berlin: Springer, 2012.
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    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation，1997, 9(8): 1735-1780.
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    GERS F A, SCHMIDHUBER J, CUMMINS F. Learning to forget:Continual prediction with LSTM[J]. Neural Computation, 2000, 12(10): 2451-2471.)
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    [1]
    BROGAARD J A. High frequency trading and its impact on market quality[R]. Evanston, IL: Northwestern University, 2010.
    [2]
    ALDRIDGE I. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems[M]. Hoboken, NJ: Wiley, 2010.
    [3]
    MARTINEZ V H, ROSU I. High Frequency Traders, News and Volatility[C]// AFA 2013 San Diego Meetings Paper. Aldan, PA: American Finance Association, 2013.
    [4]
    BROGAARD J, HENDERSHOTT T, RIORDANR. High frequency trading and price discovery[J]. The Review of Financial Studies, 2014, 27(8): 2267-2306.
    [5]
    ANGEL J, MCCABE D. Fairness in financial markets: The case of high frequency trading[J]. Journal of Business Ethics, 2013, 112: 585-595.
    [6]
    HINTON G E, OSINDERO S, TEH Y W.A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18: 1527-1554.
    [7]
    毛勇华, 桂小林, 李前,等. 深度学习应用技术研究[J]. 计算机应用研究, 2016, 33(11): 3201-3205.
    MAO Yonghua, GUI Xiaoling, LI Qian, et al. Study on application technology of deep learning[J]. Application Research of Computers, 2016, 33(11): 3201-3205.
    [8]
    LECUN Y, BENGIO Y, HINTON GE. Deep learning[J]. Nature, 2015, 521(7553): 436.
    [9]
    CIRESAN D, MEIER U, MASCI J, et al. Multi-column deep neural network for traffic sign classification[J]. Neural Networks, 2012, 32: 333-338.
    [10]
    TAIGMAN Y, YANG M, RANZATO M, et al. DeepFace: closing the gap to human-level performance in face verification[C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2014: 1939-1946.
    [11]
    JI S, XU W, YANG M,et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 221-231.
    [12]
    KARPATHY A, TODERICI G, SHETTY S, et al. Pedestrian detection with unsupervised multi-stage feature learning[C]// Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2013: 3626-3633.
    [13]
    HADSELL R, SERMANET P, BEN J, et al. Learning long-range vision for autonomous off-road driving[J]. Journal of Field Robotics, 2009, 26(2): 120-144.
    [14]
    COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12: 2493-2537.
    [15]
    BAHDANAU D, CHO K, BENGIOY. Neural machine translation by jointly learning to align and translate[EB/OL]. [2016-5-19]. https://arxiv.org/abs/1409.0473.
    [16]
    WU Y, SCHUSTER M, CHENZ, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation[EB/OL]. [2016-10-08] https://arxiv.org/abs/1609.08144.
    [17]
    王宣承. 基于LASSO和神经网络的量化交易智能系统构建—以沪深300股指期货为例[J]. 投资研究, 2014, 33(9): 23-29.
    WANG Xuancheng.Construct intelligent quantitative trading systems based on LASSO and ANNs: A case study of CSI300 futures[J]. Review of Investment Studies, 2014, 33(9): 23-29.
    [18]
    张贵勇. 改进的卷积神经网络在金融预测中的应用研究[D]. 郑州: 郑州大学, 2016.
    [19]
    MAKNICKIEN N, MAKNICKAS A. Application of neural network for forecasting of exchange rates and Forex trading[C]// The 7th International Scientific Conference “Business and Management 2012”. Vilnius, Lithuanian: Vilnius Gediminas Technical University, 2012: 122-127.
    [20]
    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.
    [21]
    LU D W. Agent inspired trading using recurrent reinforcement learning and LSTM neural networks[EB/ OL]. [2017-07-23] https://arxiv.org/abs/1707.07338.
    [22]
    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.
    LONG Aoming, BI Xiuchung, ZHANG Shuguang.An arbitrage strategy model for ferrous metal futures based on LSTM neural network[J]. Journal of University of Science and Technology of China, 2018, 48(2): 125-132.
    [23]
    范翔. 基于自动化交易平台的高频交易及统计套利分析和研究[D]. 上海: 复旦大学, 2014.
    [24]
    SILVA E, CASTILHO D, PEREIRA A, et al. A neural network based approach to support the market making strategies in high-frequency trading[C]// 2014 International Joint Conference on Neural Networks. IEEE, 2014: 845-852.
    [25]
    张德丰. MATLAB神经网络应用设计[M]. 北京:机械工业出版社,2012.
    [26]
    RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
    [27]
    KRIZHEVSKY A, SUTSKEVER I, HINTON G E．Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems．Red Hook，NY: Curran Associates，2012: 1097-1105.
    [28]
    HUBEL D H, WIESEL T N. Receptive fields,binocular and functional architecture in the cat’s visual cortex[J]. The Journal of Physiology, 1962, 160(1): 106-154.
    [29]
    FUKUSHIMA K.Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J]. Bioological Cybernetics, 1980, 36(4): 193-202.
    [30]
    LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [31]
    HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[EB/OL]. [2012-07-03] https://arxiv.org/abs/1207.0580.
    [32]
    GRAVES A. Supervised Sequence Labelling with Recurrent Neural Networks[M]. Berlin: Springer, 2012.
    [33]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation，1997, 9(8): 1735-1780.
    [34]
    GERS F A, SCHMIDHUBER J, CUMMINS F. Learning to forget:Continual prediction with LSTM[J]. Neural Computation, 2000, 12(10): 2451-2471.)
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