Deep Learning for Digital Asset Limit Order Books
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 76% 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.
See paper at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3704098