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CSCI 5931 -- Financial Data Mining
(Graduate) CINF 5931 -- Financial Data Mining
(Graduate)
Updated January 25, 2012
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
Wednesday 7:00 - 9:50, Room: Delta 236
Office Hours
TBD,
or by appointment. If the suite door is locked, then call
my extension (last 4 digits) using the phone in the hallway.
Teaching
Assistant
TBD, email:
TBD
Office Hours: TBD
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.
-
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 12
years experience as a futures trader. Since June 1, 2011, I have had
83 out of 88 winning trading days (94.3%). My percent winners is 70.6%. Below
is a daily profit/loss graph.
In terms of Money at Risk (MAR), this
equates to the following monthly gains:
|
Month |
Monthly MAR Gain |
| June, 2011 |
20.44% |
| July, 2011 |
11.28% |
| August,
2011 |
35.71% |
| Sept.,
2011 |
15.61% |
| Oct., 2011 |
14.60% |
| Nov., 2011 |
85.21% |
| Dec., 2011 |
8.57% |
The projected Annual Rate of Return (ARR)
for the whole account is 91.71%.
For 2012 I have
had 6 out of 7 winning trading days (87%). My percent winners is 72.2%. Below
is a daily profit/loss graph.
I have had a best
paper in financial data mining and I have been a beta-tester for a
major commercial 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.
|
Team |
Amount |
|
Three Million |
$63,111,143 |
|
Dough |
$60,363,715 |
|
HSN |
$19,854,218 |
|
Beta |
$19,729,375 |
|
Money Miners |
$4,460,940 |
|
WI$E GUYS |
$125,765
|
|
UHCL Miners |
$100,000 |
|
UHCL-DM |
$100,000 |
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 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
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.
Face-to-face lecture and interactive problem solving.
Appraisal
Graduate
|
Homework/Projects/Quizzes: |
10% of the total |
|
Term Project |
50% of the total |
|
Enhancements to the term project (Grad students only) |
20% of the total |
|
Midterm: |
10% of the total |
|
Final: |
10% of the total |
My motto:
Show disciplined, altruistic, passion.
Required Textbook
There is no required textbook for this
course. Readings will be from various papers or tutorials.
References
Jan 18 -
Data Mining and Financial Overview, Go over term project
-
The financial data
mining process
-
Financial data
mining versus other types of data mining
-
Terms: OHLC, Bar
chart, candle stick, Drawdown, Types of market(Trending/Trading),
Long, Short, Drawdown, Indexes, Sectors, Stocks, Futures,
Strategies of trading
-
General
strategies: Buy-and-hold, What to buy (Analysts, News(Reuters)),
When to buy
Readings for next week
Fan, M.,
Stallaert., J., A.B. Whinston, The Internet and the future of
financial markets, Communications of the ACM, 43 11, Nov. 2000, Pp.
82 - 88.
Suhua Liu,
“Electronic Trading (ET): new method in financial markets,” ACM 7th
International Conference on Electronic commerce, 2005, Pp. 900 –
903.
Stock Trading 101
Jan 25
-
Data Cleansing, Term Project
Term Project: Team Identification
forms are due
Readings and concepts for the
next 2 weeks
Moving averages,
RSI, Bollinger Bands, Stochastics, CCI, MACD, PNF, Doji, Support,
Resistance
Technical
Indicators Tutorial
Technical Analysis
Explained
Technical Analysis
Tutorial (Investopedia)
A Primer on
Technical Analysis, Lagging, Leading
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 01 -
Technical Indicators
Feb
08
-
Technical Indicators
Term Project: Data Cleansing
Functionality is due
Feb 15 -
Technical Indicators
Term Project
Milestone: Clean Data
Readings for next week
Charting, P&F
Charting
Feb 22
- Chart patterns
Assign Homework assignment
Due: March 31st or sooner
Feb
29 -
Review Term Project
Term Project: Technical
Indicators are due
Readings for next week
Elliot Wave, Gann,
Fibonacci, Advanced Get
Mar 07 -
Technical Indicators, Part 3
Mar
14 -
Spring Break
- No Class
Mar 21 -
Genetic Algorithms (3 hours);
Assign Midterm
Term Project: Backtest Workbench
is due
* Last day to
drop a class/withdraw for the semester is April 1st *
Mar
28 - Go over midterm,
Neural Networks
Apr 04 - Backtesting, Validation, Futures, Options, FOREX
Readings for next week
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.
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.
Apr
11 - Automated Trading
Term Project: Machine Learner
functionality is due
Readings and concepts for next
week
AIQ Systems,
Amibroker,
BestDirect,
eSignal,
invesTools,
Metastock,
Tradestation
Apr 18
-
Commercial Financial Data Tools - Mainly Amibroker
Term Project: Automated Trading
functionality is due
Readings for next week
51 Reasons Why
Most Traders Lose Money
What type of
trader are you?
Principles of
Successful Trading
Lakhani, J.,
Discipline, Mental Skills and the Psychology of Trading
LO, Andrew W.,
Dmitry V. REPIN, and Brett N. STEENBARGER, 2005. Fear and Greed in
Financial Markets: A Clinical Study of Day-Traders. American
Economic Review, 95(2), 352–359.
Brett N.
Steenbarger, Behavioral Patterns That Sabotage Traders
Stewart Mayhew,
“Problems in financial engineering: security price dynamics and
simulation in financial engineering,” Proceedings of the 34th
conference on Winter simulation: exploring new frontiers, December
2002
Apr 19 - Start of the GDB Cup competition
Apr
25 - Money Management; and Psychology of Trading, Assign Final Exam
Term Project
Milestone: GDB Cup
Apr 28 - Midnight - Term Project is due
Apr 30 - Midnight - Final Exam is
due
May 02 - Go over Final and GDB Cup
Term Project: Peer review forms
are due
|
Team |
Clean Data |
TI |
Workbench |
ML |
GDB Cup |
Best
Model(Starting
with 100k) |
|
Due Dates |
2/8 |
2/29 |
3/21 |
4/11 |
4/18 |
|
|
Destiny |
|
|
|
|
|
100,000 |
|
HFC Trading |
|
|
|
|
|
100,000 |
|
SPWE
Stack Paper with Ease |
|
|
|
|
|
100,000 |
|
SS Trading |
|
|
|
|
|
100,000 |
|
Team GJ |
|
|
|
|
|
100,000 |
|
Tradezilla |
|
|
|
|
|
100,000 |
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.
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VERY IMPORTANT!
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 anyone in
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 Blackboard crashes.
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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 from your final grade for each additional
absense. -
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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• SCE
2700 Bay Area
Boulevard
Delta Building. Office 171
Houston, Texas 77058
Voice: 281-283-3805
Fax: 281-283-3869
boetticher@uhcl.edu
© 2002-2012 Boetticher: Financial Data Mining Course, All Rights Reserved.
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