CSCI 4931 -- Financial Data Mining (Undergraduate)

CINF 4931 -- Financial Data Mining (Undergraduate)

CSCI 5931 -- Financial Data Mining (Graduate)

CINF 5931 -- Financial Data Mining (Graduate)
Updated May 7, 2009

Office and Addresses

Delta 171 Phone 281.283.3805
email: boetticher@uhcl.edu
Secretary: Ms. Kim Edwards, Delta 161 281.283.3860

Face-to-Face Class Hours

Monday 7:00 - 9:50, Delta 237.

Office Hours

Mon. 5:30 - 7, Tues. 5:30 - 7, or by appointment. There will be additional time for each group for the term project. If the suite door is locked, then call my extension (last 4 digits) using the phone in the hallway.

Teaching Assistant

TBD, email: tbd@tbd.com
Office Hours: TBD

WebCT link (Most likely will be used for quizzes)

Why take this course?

• More than 98 percent or all college courses teach you how to earn money. This course teaches you how to get your money to work for you. This is your chance to work smarter, not harder.

• The average retirement account has lost 57 percent since October, 2007. Assuming a rate of return of 5 percent, this means you will have to work 18 years to recoup these losses.

• Your company, your stockbroker may mean well, but nobody will care about your financial health more than you.

Why take this course from me?

I have over 9 years experience as a futures trader. In October, 2007 I predicted a downward turn in the market. For 2008, I have 69% winning trades. For 2008 I have averaged 16% profit over 5.3 months for an overall yearly profit of 120%. I took the summer off to travel. On September 24, 2008, at the UHCL Scholarly Lecture Series I accurately predicted a steep downward turn to the UHCL President, UHCL Provost, various Deans, and UHCL faculty. Two weeks later the market dropped 25 percent.

I have had a best paper in financial data mining and I have been a beta-tester for a major commerical financial data mining product.

Results from a financial data mining project in Fall, 2008

Most of the students had very little financial background for the term project. Each student started with $100,000. The table below shows how much money they made in 2.5 years. 60% of the groups made more than 4 million dollars.

GDB Cup - Fall, 2008

Course Description

Mathematically sophisticated financial models are becoming more prevalent in the financial domain. It is possible to manually construct and test various hypotheses; however the process is extremely slow. A preferred approach is to data mine financial instruments in order to identify potentially successful approaches. This course will examine different sources of data (e.g. derivatives, stocks) and how to apply machine learners in order to construct profitable models.

The traditional undergraduate student load is 5 courses. Be prepared to commit 10 to 15 hours per week to this course!

The traditional graduate student load is 3 courses. Be prepared to commit 15 to 20 hours per week to this course!

Course Goals

By the end of the course, you will

• Understand the financial data mining process;

• Understand various technical indicators;

• Have an understanding of various Machine Learners (ML) and how to apply these tools in a financial context (e.g. stock market); and

• Hopefully transcend the market

Prerequisites

The prerequisites for this course are at least one programming course or experience in C, C++, C*, Java, Delphi, or VB using Visual Studio. A class in Data Structures (CSCI3333) is recommended. A class in artificial intelligence, machine learning, pattern recognition, algorithms, or statistics would be helpful, but is not required.

I also encourage students who have a business/finance background. Please talk to me about your particular situation.

This course will assume No previous knowledge of finance.

Methodology

Face-to-face lecture and interactive problem solving.

Appraisal Undergraduate

Appraisal Graduate

Grading:

    93+ = A; 90 = A-; 87+ = B+; 83+ = B; 80+ = B-;

      77+ = C+; 73+ = C; 70 = C-; 67+ = D+; 63+ = D; 60+ = D-; 0+= F

My motto:

Seek the Truth.

Show altruistic love.

Appreciate beauty.

Required Textbook  

There is no required textbook for this course. Readings will be from various papers or tutorials.

References

• KDNuggets: Great resource for data mining.

Schedule

Jan 26 - Data Mining and Financial Overview (3 hours)

Feb 02 - Go over term project (3 hours)

Feb 09/16 - Technical Indicators (6 hours)

Leading versus Lagging indicators

Moving averages, RSI, Bollinger Bands, Stochastics, CCI, MACD, PNF, Elliot Wave, Gann, Fibonacci, Advanced Get, Doji, Support, Resistance, etc.

Technical Indicators Tutorial

Technical Analysis Explained

Technical Analysis Tutorial (Investopedia)

A Primer on Technical Analysis

Saswat Anand, Wei-Ngan Chin, Siau-Cheng Khoo, “Charting patterns on price history,” Proceedings of the sixth ACM SIGPLAN international conference on Functional programming, October, 2001.

Web Pages for Charting

A)   http://stockcharts.com/h-sc/ui?s=IBM

B)   http://www.stockta.com/

Feb 23 - Commercial Financial Data Tools (3 hours)     

AIQ Systems, Amibroker, eSignal, invesTools, Metastock, Tradestation

* Last day to drop a class/withdraw for the semester is March 30^th *

Apr 06 - Backtesting, Validation, Futures, Options, FOREX

Apr 13 Other machine learners and case studies (3 hours)

Apr 20 Other machine learners and case studies (3 hours)

Readings

Tobias Schreck, Tatiana Tekušová, Jörn Kohlhammer, Dieter Fellner, “Trajectory-based visual analysis of large financial time series data,” ACM SIGKDD Explorations Newsletter, 9 2, December, 2007.

Apr 27 - Automated Trading (3 hours)

Kearns, M.   Ortiz, L., The Penn-Lehman automated trading project, Nov-Dec 2003, Vol. 18, Issue: 6, Pp. 22- 31

Wong, R.K.   Ng, P.N. , “A hybrid approach for automated trading systems,” Second Australian and New Zealand Conference on Intelligent Information Systems, 1994, Dec. 1994, Pp. 278-282.

Antonia Azzini, Andrea Tettamanzi, “Automated trading on financial instruments with evolved neural networks,” 9th annual conference on Genetic and evolutionary computation (GECCO), 2007: 2252.

May 1 - Term Papers Due (Friday at 8 PM)

May 4 - Term paper presentations (3 hours), Take home Final Exam

May 8 - 11:59 PM - Part 1 of the final is due

May 10 - 7 PM - Part 2 of the final is due

May 10 - Midnight - Term Project is due

May 11 - Go over term projects

GDB Cup - Spring, 2009

Other Policies

Homework, Projects, Research Paper

• Homework and projects are due exactly at the prescribed time (usually the beginning of class). As soon as a homework or project is collected, then all others are considered 1 day late (even if it only 3 minutes). In the event you might be running late, you might want to email the assignment. Also, when preparing your assignment, be mindful of possible backlogs at the printer, jammed printer, printer out of toner, etc.

• Late homework/projects are accepted with a penalty of 10% deduction per 24-hour period after the due date. No late project will be accepted one week after the due date. The last homework/project cannot be late.

• There will be no extra-credit homework or projects in this course.

• All homework and projects must be typed not hand-written.

• A cover page is expected for all homework and projects.

• VERY IMPORTANT! All assignments will be group assignments. The groups will be the same for all homework, project, and term project. You may only work with members in your group only and not with anyone not in your group, nor may you contact former students who have taken my data mining course. You may not discuss, use, email, show, give, buy, sell, borrow, trade, steal, etc. in whole or part, any of the homework or projects with anyonein any manner not prescribed by the instructor. Penalty for cheating will be extremely severe and may result in an F for this course. This condition applies even after you complete this course! Penalty for cheating will be extremely severe and may result in an F for this course. 

• Handing in an assignment for another student is considered cheating. Penalty for cheating will be extremely severe and may result in an F for this course. 

• VERY IMPORTANT! Failing to report to the instructor any incident in which a student witnesses an alleged violation of the Academic Honesty Code is considered a violation of the academic honesty code. Please see me to discuss any incidents.

• VERY IMPORTANT! Purchasing, or otherwise acquiring and submitting as one's own work any research paper or any other writing assignment prepared by others constitutes cheating. Penalty for cheating will be extremely severe and may result in an F for this course.

• Standard academic honesty procedure will be followed. See the following link for additional information: http://b3308-adm.uhcl.edu/PolicyProcedures/Policy.html

Tests and Quizzes

• There are no make-up tests except in verified medical emergencies and with immediate notification. Rescheduling a final exam in order to catch a plane flight is unacceptable. Make up exams are harder, and different, than original exams.

• There are no make-up quizzes. Allow plenty of additional time in the event that webCT crashes.

• You are responsible for all required readings assigned throughout the semester.

• Students are to work on test and quizzes individually.  Students may not discuss, show, give, sell, borrow, trade, share, etc. their tests or quizzes. Penalty on cheating will be extremely severe. Standard academic honesty procedure will be followed.

• VERY IMPORTANT! Providing answers for any assigned work or examination when not specifically authorized by the instructor to do so. Or, informing any person or persons of the contents of any examination prior to the time the examination is given is considered cheating. Penalty for cheating will be extremely severe and may result in an F for this course.

• VERY IMPORTANT! Failing to report to the instructor any incident in which a student witnesses an alleged violation of the Academic Honesty Code is considered a violation of the academic honesty code. Please see me to discuss any incidents.

Miscellaneous

• Any person with a disability who requires a special accommodation should inform me and contact the Disability services office or call 281 283 2627 as soon as possible.

• You are expected to come fully prepared to every class!

• Incomplete grades or administrative withdrawals occur only under extremely rare situations.

• The ringing, beeping, buzzing of cell phones, watches, and/or pagers during class time is extremely rude and disruptive to your fellow students and to the class flow. Please turn off all cell phones, watches, and pagers prior to the start of class.

• This is not a Web-based class. You are expected to attend every class! Missing 1 class is OK. Minus 1 for each additional miss.

• I am willing to provide letters of recommendation/references only if you have attained an 'A' in one of my classes, or two 'A-' in two of my classes.

• I highly recommend that you seek out your advisor and complete you Candidate Plan of Study (CPS) as soon as possible. I am normally not available for advising during the summer months.

• Pay very careful attention to your email correspondence. It reflects on your communication skills. Below is a compilation of email errors I have received during the past year.

  dear sir.

  wen r u gonna grad the homework, bcoz i have a doubt about the third problem

  Some student

  Common problems:

     *   wen instead of when

     *   bcoz instead of because

     *   r instead of are

     *   u instead of you

     *   lowecase i instead of I

     *   starting a sentence with a lowercase letter

     *   doubt instead of question

• I immediately discard anonymous emails.

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2700 Bay Area Boulevard
Delta Building. Office 171
Houston, Texas 77058
Voice: 281-283-3805
Fax: 281-283-3869
boetticher@uhcl.edu

© 2002-2009 Boetticher: Financial Data Mining Course, All Rights Reserved.