Ascertaining price formation in cryptocurrency markets with DeepLearning

TL;DR

This paper explores the use of deep learning to predict short-term price movements in cryptocurrency markets, demonstrating promising accuracy in live high-frequency data analysis.

Contribution

It introduces a deep learning approach for real-time prediction of cryptocurrency price direction, a novel application in high-frequency cryptocurrency trading.

Findings

Achieved 78% accuracy in predicting Bitcoin mid-price movements.

Demonstrated the feasibility of applying deep learning to live cryptocurrency data.

Provided insights into the characteristics of cryptocurrency market dynamics.

Abstract

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.