Virtual currency trading strategy based on ARIMA and AHP-PSO

Abstract

As the price of virtual currency fluctuates greatly, precise prediction and appropriate trading strategies can bring investors best returns. This paper predicted the price of Ethereum and Bitcoin in the light of autoregressive integrated moving average model (ARIMA) and get a R2 of 0.995 and 0.993 respectively, which indicates the model can yield reasonable predictions. Then their investment ratios are set to 0.88 and 1.12 respectively by analytic hierarchy process (AHP). Particle swarm optimization (PSO) is used to solve the daily revenue function formed by the predicted price and the current price. Finally, the paper compared the returns yielded by the PSO trading strategy optimized by AHP and the strategy without optimization. It can be concluded that the AHP has a possibility of 64.66 per cent to yield more returns when used.