Forecasting and trading cryptocurrencies with machine learning under changing market conditions

作     者：Helder Sebastiao Pedro Godinho 

作者机构：Univ CoimbraCeBERFaculty of EconomicsAv.Dr.Dias da Silva1653004‑512 CoimbraPortugal 

出 版 物：《Financial Innovation》 (金融创新（英文）)

年 卷 期：2021年第7卷第1期

页      面：61-90页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：This work has been funded by national funds through FCT-Fundaçao para a Ciência e a Tecnologia I.P. Project UIDB/05037/2020 

主　　题：Bitcoin Ethereum Litecoin Machine learning Forecasting Trading 

摘      要：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 *** 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,*** the test period,five out of 18 individual models have success rates of less than 50%.The trading strategies are built on model *** 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%,*** 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.