A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data

This paper presents a deep learning framework based on Long Short-term Memory Network(LSTM) that predict price movement of cryptocurrencies based on a trailing window. It is the first to use trade-by-trade data to predict short term price changes in fixed time horizions. By carefully designing features and detailed searching for best hyper-paramenters, the model is trainged to achieve high performance on nearly a year of trade-by-trade data. The optimal model delivers stable high performance(over 60% accuracy) on out-of-sample test periods. 

In a realistic trading simulation setting, the prediction made by the model could be easily monetized.