With the rapid development of computer technology the application of computer technology is becoming more and more widespread This article proposes a stock price analysis and prediction system based o

Translation:

As computer technology continues to rapidly develop, its applications are becoming increasingly widespread. This article proposes a stock price analysis and prediction system based on Django and LSTM, which provides investors with more accurate stock price prediction results in the current context of the financial industry's development. The article first introduces the background and current state of stock analysis and prediction, analyzes the limitations of traditional stock prediction methods, and proposes a stock prediction model based on LSTM deep learning neural networks. The article then details the principles and implementation of the model.

Next, the article introduces the design and implementation of the stock prediction system based on the Django framework, including the functional modules and interactive interface design of the system. Traditional stock prediction methods based on statistical methods can only consider the influence of some economic indicators, while ignoring the influence of many other factors, leading to inaccurate prediction results. The LSTM deep learning neural network, with its memory function, can capture more rules of time series data and improve the accuracy of stock prediction.

The stock prediction system proposed in this article is based on the Django framework, with good scalability and easy maintenance. The system is divided into user registration and login, data collection, preprocessing, model training, and stock news modules, which realize functions such as data collection, cleaning, prediction, etc. By setting the hyperparameters of the LSTM deep learning neural network model, such as time step, number of neurons, training rounds, etc., the training model is designed and the model parameters are adjusted to ultimately determine the model structure of the deep learning neural network.

This article provides some reference value for predicting the price trend of ten domestic company stocks selected, and the designed system has high prediction accuracy and good stability, which can provide investors with more reliable stock prediction results