基于Django和LSTM的股票价格分析预测系统

随着计算机技术的快速发展，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，包括系统的功能模块和交互界面设计。

在传统的股票预测方法中，基于统计学方法的股票预测模型通常只能考虑一些经济指标的影响，而忽略了其他许多因素的影响，因此预测结果不够准确。而LSTM深度学习神经网络作为一种具有记忆功能的神经网络模型，能够捕捉到更多的时间序列数据的规律，提高了股票预测的准确性。

本文提出的股票预测系统基于Django框架，具有良好的可扩展性和易维护性，系统分为用户注册登录、数据采集、预处理、模型训练、股票新闻等模块，通过这几个模块实现对股票数据的采集、清洗、预测等功能。通过设置LSTM深度学习神经网络模型的超参数，从时间步长、神经元个数、训练轮数等方面来设计训练模型，调整模型参数，最终确定深度学习神经网络的模型结构。本文对选取的十支国内公司股票价格走势预测提供一定的参考价值，设计出的系统具有较高的预测准确率和良好的稳定性，能够为投资者提供更为可靠的股票预测结果。

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.