A forecasting method of future stock price based on FP-growth algorithm

Abstract

The invention discloses a method based on FP-Growth algorithm for future stock price forecasting, includes such steps as S101, extracting characteristic factors of single stock: pre-collecting price-earnings ratio, profit fundamental factor, average line and volatility technical factor, converting numerical data into classified data, and determining characteristic factors of single stock as basiccharacteristics of stock price. S103, determining a single target factor: extracting a characteristic factor time point of a single stock in the step S101 in advance, and processing data of a component category with stock price characteristic numerical data three months after the time point to determine a single target factor; S105, determining a single template: combining the characteristic factors of the individual stocks in step S101 and the target factors in step S103, and determining a single sample. The invention has the characteristics of intuitionistic and explainable, and the operatiom speed of the FP-growth algorithm is quite fast; and compared with the common regression and classification algorithm, the FP-growth algorithm has more efficient advantages.

Description

technical field [0001] The present invention relates to the technical field of stock price prediction, in particular to a future stock price prediction method based on FP-growth algorithm. Background technique [0002] As a kind of securities, stock contains economic benefits and can be circulated and transferred through listing. At the same time, when a joint stock limited company raises capital, it can reflect the holder's ownership of some of the company's assets by issuing shares to each shareholder. . The emergence of the trading market can be traced back to the 1960s, and the United States is the earliest place where the modern trading market appeared. The openness of the world's economic and financial markets has increased rapidly since China's accession to the WTO. At the same time, the international market has provided more and more opportunities and space for Chinese companies to list abroad. Therefore, the role of China's securities market in my country's economi...

AI Technical Summary

Problems solved by technology

[0004] In the current traditional stock price prediction methods, the commonly used stock price prediction methods are mainly based on regression and classification algorithms, which involve a large number of nonlinear operations and fitting processes. In the actual application process, there may be good results. However, its disadvantage is that it is not interpretable. It is a black box model, so it cannot effectively explain the stock price movement, and the model is greatly disturbed by infrequent items.

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