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Deep Learning for Digital Asset Limit Order Books

Author

Listed:
  • Rakshit Jha
  • Mattijs De Paepe
  • Samuel Holt
  • James West
  • Shaun Ng

Abstract

This paper shows that temporal CNNs accurately predict bitcoin spot price movements from limit order book data. On a 2 second prediction time horizon we achieve 71\% walk-forward accuracy on the popular cryptocurrency exchange coinbase. Our model can be trained in less than a day on commodity GPUs which could be installed into colocation centers allowing for model sync with existing faster orderbook prediction models. We provide source code and data at https://github.com/Globe-Research/deep-orderbook.

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  • Rakshit Jha & Mattijs De Paepe & Samuel Holt & James West & Shaun Ng, 2020. "Deep Learning for Digital Asset Limit Order Books," Papers 2010.01241, arXiv.org.
    • Handle: RePEc:arx:papers:2010.01241
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    References listed on IDEAS

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    1. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 852-866, December.
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    Cited by:

    1. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.

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