神经网络的时间序列分析.zip

# High-frequency Financial Time Series Analysis Based on BP Neural Networks #### Paper > http://www.lin-baobao.com/static/files/graduate_paper.pdf #### Video for Demonstration > http://www.lin-baobao.com/static/videos/graduate_project.flv #### Application > http://www.lin-baobao.com/bp_finance/php/login/ ### This project includes three sections. - GetData Data crawling. Responsible for crawling and processing the high frequency data of stock transcation in recent years. And the data format would be converted to an appropriate format. - predict Core algorithm. Implement the Back Propagation Neural Networks without using any framework (all code is completely written by myself). - php System integration. Integrate the first two parts into a system. #### Project description > It included data crawling, model design, code implementation, result analysis, experiments and system integration. - 1. Crawled the data of some stocks’ transactions per day. - 2. Chose BP Neural Network as model, deduced the formulas myself and implemented code without using any framework. - 3. The direction accuracy was about 55%-60% and the relative error between the actual and the predicted price maintained at 0.015%. Reached the conclusion that high-frequency data is better for stock price prediction via testing data’s impact at different frequencies. - 4. Integrated the algorithm into a real-time stock price prediction system. #### Deployment > Need to deploy mysql, and the sql file is in [finance.sql](finance.sql) #### Ways to run > Just call the [run.py](run.py) directly.