Blockchain financial investment based on deep learning network algorithm

Abstract

In order to study the use of in-depth learning to process financial data, it is proposed that the related technology of neural network and deep learning can be applied to financial data, and real stock index and futures data can be used to explore the application effect of neural network and in-depth learning. Firstly, the theory and model of in-depth learning and neural network are introduced in detail. Secondly, a simple neural network and in-depth learning model are used in the stock index and futures price forecast. The data used in the input of the model include the price of a stock in the current trading and some data indicators, and the closing price of a stock in the next time. The decline will be reflected in output. If the output is up for 1 and down for 0, new data will be input after the training of the model. Finally, the output data can be compared with the real data to judge the application effect of the model, after comparing e and analyzing the application effect of the model. The results show that the research in this study can help investors to build an automated investment model and the investment strategy of the stock market. The construction can be used for reference to improve investors’ investment strategy and return rate.