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

TL;DR

This paper introduces a deep learning LSTM-based framework that accurately predicts short-term cryptocurrency price movements from trade-by-trade data, demonstrating high performance and transferability across different assets.

Contribution

The study develops a novel LSTM-based model trained on extensive trade-by-trade data, achieving superior prediction accuracy and demonstrating universal feature extraction for cryptocurrencies.

Findings

Over 60% accuracy in out-of-sample tests

Model predictions can be monetized in trading simulations

Features learned are transferable across different cryptocurrencies

Abstract

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.