Application Research Of Crude Oil Futures Price Forecasting Model Based On EEMD-GRU Neural Network

Crude oil is an important chemical raw material,and its price changes arouse the close attention of governments,enterprises and investors.Crude oil futures prices and spot prices often have a linkage relationship and futures have the function of price discovery.Therefore,it is of practical significance to predict crude oil futures prices as accurately as possible.At present,the research on China crude oil futures market mostly uses low-frequency data,but the application of high-frequency data in the international market is more extensive.In order to meet the requirements of the open development of the financial market,the experimental data selected in this article is the closing price of 1minute.For the past few years,with the rapid improvement of the computer performance,neural network models are used in many different industries,and the use of recurrent neural networks to predict price time series is also a hot topic in academic research.This paper uses ensemble empirical mode decomposition(EEMD)and gated recurrent neural network(GRU)to construct a hybrid forecasting model to forecast Shanghai crude oil futures(SC)and London Brent crude oil futures(Brent)respectively.In terms of data frequency,crude oil price series are complex and non-linear,and contain a lot of noise.Therefore,this article first uses EEMD to decompose the original data into intrinsic module functions(IMF)representing different meanings.The sequence establishes a neural network for training prediction.High-frequency data has high requirements for model efficiency in practical applications,and it is necessary to make predictions as quickly as possible.Therefore,this paper chooses the GRU neural network,which has a simpler structure and less error than LSTM.At the same time,this paper chose Nadam as the optimization of the neural network.To improve the training speed of the model.Based on the constructed forecasting model,this article proposes a quantitative trading strategy suitable for Shanghai crude oil futures.This strategy combines actual price trends and the forecast results of the model to formulate corresponding trading signals,and then opens and closes positions based on the trading signals operating.In this paper,the representative hyperparameters are determined by the traversal method,and then the high-frequency data for the next week is used for back-testing.In the empirical research,this paper selects multiple types of error evaluation indicators.The experimental results show that proposed model in this paper is more precise than models that do not use EEMD decomposition and other recurrent neural networks.And training speed has also been improved.The results of the trading backtest show that the trading strategy achieves risk control while achieving stable returns,which verifies the effect of the prediction model in this article.