A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data
This work addresses price prediction for cryptocurrency traders, but it is incremental as it applies an existing LSTM method to a new type of financial data.
The paper tackles predicting short-term cryptocurrency price movements using trade-by-trade data with an LSTM-based deep learning framework, achieving over 60% accuracy in out-of-sample tests and demonstrating transferability to unseen cryptocurrencies.
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.