Deep Learning for Digital Asset Limit Order Books
Statistical Finance
2020-10-06 v1
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.
Cite
@article{arxiv.2010.01241,
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},
journal= {arXiv preprint arXiv:2010.01241},
year = {2020}
}
Comments
9 pages, 7 figures