  This paper presents a deep learning framework based on Long Short-term Memory
Network(LSTM) that predicts price movement of cryptocurrencies from
trade-by-trade data. The main focus of this study is on predicting short-term
price changes in a fixed time horizon from a looking back period. By carefully
designing features and detailed searching for best hyper-parameters, the model
is trained 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. Moreover, this study
shows that the LSTM model could extract universal features from trade-by-trade
data, as the learned parameters well maintain their high performance on other
cryptocurrency instruments that were not included in training data. This study
exceeds existing researches in term of the scale and precision of data used, as
well as the high prediction accuracy achieved.
