Analyzing Stock Quotes using Data Mining TechniquesName of Student: To Yi FunUniversity Number: 2010149103

First Presentation, Final Year Project, 2013

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 setto provide a classifier for classifying testing set

Application Structure

Raw data Data processing

Information presentation and arithmetic operation

Download

CSV2MYSQLGENERATOR

Processed Data

Filter Query (Splitting)

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

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 CSV2MYSQLGENERATOR program, the result is a lengthy sql commands

Data Processing • Attributes Setting -HL_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: Popularity

Class: -B_RiseMore3Perc5Day: 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_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: 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