1 / 18

Analyzing Stock Quotes using Data Mining Techniques

This project presentation explores the implementation of data mining techniques to classify stock quotes using decision trees. The aim is to develop a model that groups companies based on historical stock prices, helping to predict future trends. We’ll discuss the theory behind classification, demonstrate the application interface for data processing, and analyze stock data from various companies like Pfizer and Exxon. The final outcomes include enhanced user interfaces, visualization of decision trees, and the next steps to improve the model's usability and effectiveness.

dylan
Télécharger la présentation

Analyzing Stock Quotes using Data Mining Techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. First Presentation, Final Year Project, 2013 Analyzing Stock Quotes using Data Mining Techniques Name of Student: To Yi Fun University Number: 2010149103

  2. Flow of Presentation • Aim of the this classification for stock trade • Theory of Classification • Decision Tree making • Introduction of the application • Structure and techs used in this application • Preparation • Interface

  3. Flow of Presentation • Demonstration • Data Analysis • What to do next • Q&A

  4. Aim • Find a model for class attribute as a function of others to group a class for previously unseen records • e.g. find out the classifier for historic stock price; Group companies into different classes for inspection • classier: decision tree, rule-based classifier

  5. Theory for Decision Tree • A series of test conditions making to sort the instances into class • Greedy, split record based on attribute that best suit the criterion • Attribute (discrete) setting, 2-way split; multiple-way split

  6. Theory for Decision Tree • Best split -Gini Index, generalization of variance impurity -Entropy, amount of impurity on a set • Aim: using a training set to provide a classifier for classifying testing set

  7. Application Structure CSV2MYSQLGENERATOR Processed Data Download Filter Query (Splitting) Information presentation and arithmetic operation Raw data Data processing

  8. Preparation • Downloading the stock historic data: for 30 DOM shares e.g. Pfizer, Bank of America, America Express, Exxon • Convert to .csv file to be processed by the CSV2MYSQLGENERATOR program, the result is a lengthy sql commands

  9. Data Processing • Categories into different type of stock by its industries • Dow 30 as training set and 8 more stocks as testing set, mainly large scale company

  10. Data Processing • Downloading the stock historic data: for 30 DOM shares e.g. Pfizer, Bank of America, America Express, Exxon • Convert to .csv file to be processed by the CSV2MYSQLGENERATOR program, the result is a lengthy sql commands

  11. Data Processing Class: -B_RiseMore3Perc5Day: Buy Signal • Attributes Setting -HL_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: Popularity

  12. Data Processing • Attributes Setting

  13. User Interface • Make Use of the mysql connector to input the processed data into the C# • Three Major Components: • -Input • -Result Log • -Test

  14. Demonstration • Make Use of the mysql connector to input the processed data into the C# • Three Major Components: • -Input • -Result Log • -Test

  15. Result

  16. Result Analysis Attributes Setting -HL_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: Popularity

  17. What to do Next • Implement a more user friendly UI for presenting the stock price, visualize the tree and provide query service • Implement an splitting Algorithm using Gini and compare the difference of the results generated by these Algorithms

  18. Q & A

More Related