Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Helder Sebastião,Pedro Godinho

doi:10.1186/s40854-020-00217-x

Abstract

This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

Highlights

Since its inception, coinciding with the international crisis of 2008 and the associated lack of confidence in the financial system, bitcoin has gained an important place in the international financial landscape, attracting extensive media coverage, as well as the attention of regulators, government institutions, institutional and individual investors, academia, and the public in general
This study examines the predictability of the returns of major cryptocurrencies and the profitability of trading strategies supported by machine learning (ML) techniques
Besides the success rate that is given by the relative number of times that the model predicts the right signal of the one-day ahead return and can be computed both for the regression and classification models, we report the mean absolute error (MAE), the root mean square error (RMSE), and Theil’s U2

Summary

Introduction

Since its inception, coinciding with the international crisis of 2008 and the associated lack of confidence in the financial system, bitcoin has gained an important place in the international financial landscape, attracting extensive media coverage, as well as the attention of regulators, government institutions, institutional and individual investors, academia, and the public in general. Initially designed to be a peer-to-peer electronic medium of payment (Nakamoto 2008), bitcoin, and other cryptocurrencies created afterward, rapidly gained the reputation of being pure speculative assets Their prices are mostly idiosyncratic, as they are mainly driven by behavioral factors and are uncorrelated with the major classes of financial assets; their informational efficiency is still under debate. This study examines the predictability and profitability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—using ML techniques; it contributes to this recent stream of literature on cryptocurrencies These three cryptocurrencies were chosen due to their age, common features, and importance in terms of media coverage, trading volume, and market capitalization (according to CoinMarketCap, together, these three cryptocurrencies represent currently about 75% of the total market capitalization of all types of cryptocurrencies)

Objectives

Methods

Conclusion