Deep Learning for Digital Asset Limit Order Books

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 coin-base. Our model can be trained in less than a day on commodity GPUs which could be installed into co-location centers allowing for model sync with existing faster order-book prediction models. We provide source code and data at https://github.com/Globe-Research/deep-order-book.

Keywords: Cryptocurrency, Bitcoin, CNN, Order-book, Market Making, Direction, Prediction, TCN, Temporal Convolutional Network

JEL Classification: G10, G11, G12