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

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 Raw dataData processing Information presentation and arithmetic operation Download CSV2MYSQ LGENERAT OR Processed Data Filter Query (Splitting)

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 Attributes Setting -HL_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: Popularity Class: -B_RiseMore3Perc5Day: Buy Signal

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