First Presentation Final Year Project 2013 Analyzing Stock

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 Introduction of the application Structure and techs used in this application Preparation Interface

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

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

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

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

Application Structure Download CSV 2 MYSQ LGENERAT OR Processed Data Filter Query (Splitting) Raw data Data processing Information presentation and arithmetic operation

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 CSV 2 MYSQLGENERATOR program, the result is a lengthy sql commands

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

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 CSV 2 MYSQLGENERATOR program, the result is a lengthy sql commands

Data Processing • Attributes Setting -HL_30 Days. Average: Tendency -HL_Change. Daily: Change -HL_Change. Perc: Difference -HL_Vol. Change: Popularity Class: -B_Rise. More 3 Perc 5 Day: Buy Signal

Data Processing • Attributes Setting

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

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

Result

Result Analysis Attributes Setting -HL_30 Days. Average: Tendency -HL_Change. Daily: Change -HL_Change. Perc: Difference -HL_Vol. Change: Popularity

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

Q&A