Stock investment method based on weighted dense connection convolution neural network deep learning

Abstract

The invention relates to a stock investment method based on weighted dense connection convolution neural network deep learning. According to the method, feature extraction is conducted on input stockdata through weighted dense connection convolution, different initial weight values are endowed with dynamic adjustment weight values in the training process through cross-layer connection and featurepatterns of different layers, the feature patterns are more effectively used, information flow between all layers in the network is increased, and the problem that the layer is too deep and thus convergence of gradient disappearance results in the training process is difficult is solved to some extent. Through the Q value output by the weighted dense connection convolution network, appropriate stock trading action is selected, a corresponding reward value is obtained, the reward value and states are stored in a experience pool, at the time of training, batch sampling is randomly conducted inthe experience pool, and the weighted dense connection convolution neural network is used for approaching a Q value function of the Q-learning algorithm. By directly learning the environmental factorsof a stock market, a trading decision is directly given.

Description

technical field [0001] The invention belongs to the technical field of financial big data, and in particular relates to a stock investment method based on deep learning of a weighted densely connected convolutional neural network. Background technique [0002] Since the reform and opening up, my country's market economy has developed rapidly. People's financial awareness and investment awareness are gradually increasing, and more and more people choose to invest in the stock market to realize their asset appreciation. However, stock prices fluctuate, and the stock market is unpredictable. If investors want to win rich investment returns in the stock market and become a successful investor, they must carefully study the history, performance and development prospects of listed companies, and analyze them in detail. For the financial status of listed companies, establish an investment philosophy based on fundamental analysis and supplemented by technical analysis, find stocks ...

AI Technical Summary

Problems solved by technology

The idea is simple as this is an example of supervised learning, more precisely a regression problem, but the accuracy of predicting price movements is often difficult to achieve

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