23725

Deep Learning for Digital Asset Limit Order Books

Rakshit Jha, Mattijs De Paepe, Samuel Holt, James West, Shaun Ng
University of Cambridge
arXiv:2010.01241 [q-fin.ST], (3 Oct 2020)

@misc{jha2020deep,

   title={Deep Learning for Digital Asset Limit Order Books},

   author={Rakshit Jha and Mattijs De Paepe and Samuel Holt and James West and Shaun Ng},

   year={2020},

   eprint={2010.01241},

   archivePrefix={arXiv},

   primaryClass={q-fin.ST}

}

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.
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