A hybrid method for crude oil price direction forecasting using multiple timeframes dynamic time wrapping and genetic algorithm

This study proposes a hybrid method based on similarity measurement of time series from multiple timeframes to predict direction changes of crude oil price, as well as executing simulated trading. Except daily timeframe data, it is essential for utilizing the information from various representations of the same data source; hence weekly data are also used. For the proposed method, firstly, it uses the Multiple Dynamic Time Wrapping (MDTW) to collect similar time series from daily and weekly data, and direction changes and returns of them one week later. Next, it calculates a comprehensive expected return based on the expected return results of two timeframes and their weights. Then, the proposed method predicts the direction change of current time series for one week later, and executes simulation trading upon the prediction results. Lastly, the proposed method adopted the genetic algorithms to optimize several model parameters for trading strategy. Experimental results showed that the proposed method achieved excellent performances in terms of hit ratio, accumulated return and Sharpe ratio, and the results are significantly superior to that of benchmark methods. The proposed method can provide beneficial advises for investors, energy-related enterprises, and government officers engaged in policy decisions.