论文标题
数字资产限制订单的深度学习
Deep Learning for Digital Asset Limit Order Books
论文作者
论文摘要
本文表明，临时CNNS准确地从限制订单簿数据中预测比特币点价格变动。在2秒的预测时间范围内，我们在流行的加密货币交易所Coinbase上实现了71 \％的步行准确性。我们的模型可以在不到一天的时间内对商品GPU进行培训，该模型可以安装到托管中心中，以允许与现有更快的订单预测模型进行模型同步。我们在https://github.com/globe-research/deep-orderbook上提供源代码和数据。
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.
