随着计算机技术的快速发展对于计算机技术的应用越来越广泛。本文提出了一种基于Django和LSTM的股票价格分析预测系统在当前金融行业发展的背景下为投资者提供更为准确的股票价格预测结果。本文首先介绍了股票分析预测的背景和现状分析了传统股票预测方法的局限性随后提出了基于LSTM深度学习神经网络的股票预测模型并介绍了该模型的原理和实现方法接着本文介绍了基于Django框架搭建的股票预测系统的设计和实现包

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 on Django and LSTM. In the current context of the development of the financial industry, it provides investors with more accurate stock price prediction results.

This article first introduces the background and current situation of stock analysis and prediction, analyzes the limitations of traditional stock prediction methods, and then proposes a stock prediction model based on LSTM deep learning neural network. The principle and implementation method of the model are introduced. Then, this 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.

In traditional stock prediction methods, stock prediction models based on statistical methods can only consider the influence of some economic indicators, while ignoring the influence of many other factors, so the prediction results are not accurate enough. The LSTM deep learning neural network, as a neural network model with 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 the functions of 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