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Analyzing Stock Quotes using Data Mining Techniques

First Presentation, Final Year Project, 2013. Analyzing Stock Quotes using Data Mining Techniques. Name of Student: To Yi Fun University Number: 2010149103. Flow of Presentation. Aim of the this classification for stock trade Theory of Classification Decision Tree making

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Analyzing Stock Quotes using Data Mining Techniques

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  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

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