Abstract
In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results.
Highlights
Scholars have proposed a variety of financial analysis and forecasting methods, including (1) financial forecasting based on economics and its improved forecasting methods, such as the financial forecasting method based on grey linear regression combination model, which has the characteristics of less data demand and accurate forecasting model; (2) forecasting methods and models based on mathematics and statistics, such as improved hidden Markov model and its application in financial forecasting, financial forecasting based on Bayesian maximum likelihood estimation, etc. [6, 7]
In the financial analysis and prediction methods combined with computer, both the traditional prediction methods and the deep learning financial prediction methods based on convolution neural network have strong innovation and have high accuracy and reliability in specific fields or environments [10]
If the model is built on the historical data of long time series, the memory and analysis ability of the model on the time dependence of data is poor, which cannot meet the requirements of financial data analysis on long time series. is system creatively uses the long-term and short-term memory neural (LSTM) network of the recurrent neural network (RNN) to build the financial prediction model
Summary
Polynomial regression analysis interface of data cleaning module is called to check the integrity of the returned data and clean the data. (5) Refresh after the latest dimension reduction result of extreme point. If there is no suspension, judge whether there is an outlier point, such as 0 being negative, or the stock with a one-day increase of more than 10%, or the stock with a decrease of more than −10%. In case of abnormal value, sina tushare interface should be called to conduct a second query to get its true value. If both queries are abnormal values, an error will be reported to the user. E final result is the real value without outliers and missing data, including the external interface If the data pass the test, the stock sequence is normalized by min niax to remove the difference caused by the different value of different stocks. e final result is the real value without outliers and missing data, including the external interface
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
