Design And Implementation Of Prediction System About Prices Of Domestic Crude Oil Futures

The futures market has many key roles in stabilizing the market economy and promoting market development.The futures market can not only be used as an important investment tool,but also can reasonably use social idle funds to avoid market risks.The futures about Crude oil also hold an important position in the world oil market and their importance continues to grow day by day.The establishment of the crude oil futures market,on the one hand,can regulate production according to its market supply and demand,on the other hand,it can be used as a financial instrument to avoid the risk of market possibility.In this paper,the global search capability of the genetic algorithm is used to find the optimal initial weights and thresholds of the BP neural network,which in turn optimizes the convergence speed and accuracy of the BP neural network.Using a genetic algorithm to optimize weights and thresholds based on BP neural networks,we can get a better futures price of crude oil.At the same time,in addition to the model,it also uses Python and SQL Server software to design and implement the system's presentation layer,logic layer and data layer to form a complete set of domestic crude oil futures price forecasting system.Experimentally,it was found that the BP neural network has good predictive power for crude oil futures data,with better prediction accuracy and simulation yield compared to the LSTM model and the BP neural network model with unprocessed data.The prediction of crude oil futures prices can not only be used as a tool for risk avoidance,but also to enhance trading income based on the original futures strategy.The prediction model constructed in this paper can meet the needs of energy institutions,distributors,private equity and other participants in the energy industry for the prediction of crude oil futures prices.