Forecasting and trading cryptocurrencies with machine learning under changing market conditions

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.

A Novel Cryptocurrency Prediction Method Using Optimum CNN

In recent years,cryptocurrency has become gradually more significant in economic regions worldwide.In cryptocurrencies,records are stored using a cryptographic algorithm.The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media.Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels.Therefore,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet sentiments.This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and Ethereum.The results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.