Deep Learning for Digital Asset Limit Order Books

Rakshit Jha, Mattijs De Paepe, Samuel Holt, James West and Shaun Ng

Papers from arXiv.org

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.

Date: 2020-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-pay
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2010.01241

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