Method of analyzing stock trends

Abstract

The invention discloses a method for analyzing a stock trend. The method comprises steps of obtaining stock data through the method, and forming a feature data set; secondly, initializing the speed and position information of the stock data set, updating a local optimal value point of each particle point and the iteration step number and the self-adaptive speed value of each particle in each dimension, updating the speeds and positions of all the particle points, and finding a global optimal value point of the population from historical local optimal value points of all the particles; and finally, constructing a data matrix according to the optimal features, performing RF classification on the data matrix to obtain a classification result, performing stock prediction, and comparing the prediction result accuracy with a BPSO-RF algorithm. According to the method, the optimized discrete binary particle swarm is utilized to improve the random forest algorithm, remove redundant features, screen optimal features and input the optimal features into the RF algorithm for stock prediction, so the prediction precision is improved, the method provided by the invention has a high convergence rate, can find a better optimal value of the target function in the same iteration step, and reduces the stock prediction time.

Description

technical field [0001] The invention belongs to the field of finance and machine learning, and specifically relates to a method for analyzing stock trends based on ADB particle swarm optimization random forest algorithm Background technique [0002] On July 22, 2019, stocks on the Science and Technology Innovation Board were officially traded on the Shanghai Stock Exchange. Analyzing stock market prices is very challenging due to the uncertainty of stock market conditions. Generally speaking, there are two ways to analyze stock price trends. One is fundamental, which mainly considers the company’s annual rate of return; the other is technical, which is mainly based on mathematical analysis of past stock data, including statistical calculations and machine learning. The most important methods of machine learning are support vector machines, neural networks and random forest algorithms. These methods are widely used in the financial field and have achieved good results. Amo...

AI Technical Summary

Problems solved by technology

In order to solve the shortcomings of the original BPSO-RF model, through the introduction of multi-point velocity vectors and the improvement of adaptive velocity values ​​in the discrete binary particle swarm, the global optimal value search ability and the convergence speed of complex optimization problems are improved, thereby improving the random forest Algorithmic Accuracy and Reduced Prediction Time

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