Stock risk early warning system based on blockchain, big data and algorithm

Abstract

The invention discloses a stock risk early warning system based on blockchain, a big data and a algorithm, and the system comprises a stock prediction model establishment module, a blockchain storage module and a stock prediction module; the stock prediction model establishment module is used for establishing a stock prediction model, and the blockchain storage module is used for storing a sample set for training and testing the stock prediction model; the stock prediction module intercepts a trading day before the prediction day and historical index data of the trading day before the trading day according to a preset sub-sequence length to form a sub-sequence, the normalized sub-sequence serves as an input value and is input into the established stock prediction model, and an output value of the stock prediction model is the stock trend of the prediction day. The invention has the beneficial effects that the BP neural network is trained and tested by using the stock historical index data, and the stock prediction model based on the BP neural network is established to predict the stock trend, so that investors can timely avoid investment loss according to the predicted stock rising and falling trend.

Description

technical field [0001] The invention relates to the financial field, in particular to a stock risk early warning system based on blockchain, big data and algorithms. Background technique [0002] With the rapid development of the process of global economic integration, the stock market plays an increasingly important role in the global economy, and accurate forecasting of the stock market has important social and economic value. The stock market has massive and heterogeneous data from a wide range of sources. With the development of information technology, the use of data mining algorithms can dig out potential and valuable knowledge and information in the data to support management and decision-making activities. Through data mining and predictive analysis, we can understand the future trend of the stock market, which can help investors adjust their investment strategies, and can avoid investment losses in time according to the predicted stock ups and downs. [0003] With ...

AI Technical Summary

Problems solved by technology

The BP neural network model has the advantages of strong nonlinear mapping ability and strong fault tolerance, but it also has the defects of being sensitive to the initial value and easy to fall into local optimum. It has the defect of unstable prediction accuracy. In view of the above defects, the cuckoo algorithm is proposed to optimize the initial weight and threshold of the BP neural network, which makes up for the shortcomings of the BP neural network that are sensitive to the initial value and easy to fall into local extremum. When the optimized BP neural network is applied to stock forecasting, it has high forecasting accuracy, which can play an early warning role

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