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Analyzing Stock Quotes using Data Mining TechniquesName of Student: To Yi FunUniversity Number: 2010149103
First Presentation, Final Year Project, 2013
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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
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Flow of Presentation
•Demonstration•Data Analysis•What to do next•Q&A
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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
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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
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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
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Application Structure
Raw data Data processing
Information presentation and arithmetic operation
Download
CSV2MYSQLGENERATOR
Processed Data
Filter Query (Splitting)
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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
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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
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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
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Data Processing • Attributes Setting -HL_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: Popularity
Class: -B_RiseMore3Perc5Day: Buy Signal
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Data Processing • Attributes Setting
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User Interface• Make Use of the mysql connector to input the processed
data into the C#
• Three Major Components:
-Input -Result Log -Test
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Demonstration• Make Use of the mysql connector to input the processed
data into the C#
• Three Major Components:
-Input -Result Log -Test
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Result
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Result Analysis
Attributes Setting -HL_30DaysAverage: Tendency -HL_ChangeDaily: Change -HL_ChangePerc: Difference -HL_VolChange: Popularity
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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
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Q & A