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CSCI 5832 -- Financial Data Mining (Graduate)

CINF 5832 -- Financial Data Mining (Graduate)
Updated  February 7, 2018

Office and Addresses

Delta 171 Phone 281.283.3805
email: boetticher@uhcl.edu
Secretary: Ms. Caroline Johnson Delta 161 281.283.3860

Face-to-Face Class Hours

Wednesday 7:00 - 9:50, Room: Delta 241

Office Hours

Wed 4 - 7, Thurs 1 - 4, or by appointment. If the suite door is locked, then call my extension (last 4 digits) using the phone in the hallway. Students who have an appointment will have priority over those students who don't.

Teaching Assistants

Ms. Hajera Razvi
Email: KasimRazviH3548@UHCL.edu

Hours: Tuesday 5 - 7; Wednesday 1 - 4; Thursday 2 - 4 (Dr. Boetticher's students have priority)

Hours: Monday 4 - 7; Thursday 4 - 6 (Dr. Findler's students have priority)

 

 

 

 

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.

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);

  • Construct and backtest a financial model;

  • Apply a financial model against live data;

  • Construct a model capable of automated trading (Robo trader)

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:

 GDB Cup

40% of the total

 Final:

25% of the total

 Programming Assignments

25% of the total

 Homework/Quizzes: 

10% of the total

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:

Show disciplined, altruistic, passion.

Required Textbook  

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

References

Schedule (Tentative)

 

Jan 17 - Overview Financial Data Mining Terminology and Concepts - Part 1, GDB Cup

Terms for this week: Financial Data Mining, Temporal data, Time-series data, symbol, bid price, bid size, ask price, ask size, change, trade, Depth Of Market Execution (DOME), tick data, Open, High, Low, Close, Volume, bar graph, candlestick graph, long trade, short trade, what to buy, when to buy,  Market order, limit order, drawdown, stop, day order, bracket trade, types of markets (bull, bear, sideways), stock, index, shares, contracts, leverage, eMinis, expiration date, technical analysis, technical indicators, Simple Moving Average, trading style, position trading, swing trading, day trading, scalp trading, discretionary, system trading, semi-automated trading, automated trading,

 

************************************************************

***   Course materials are located on the Google Drive   ***

************************************************************

 

Readings for this week

·  Read:  Week 01 Notes: Unit 1 - Financial Data Mining Introduction

·   Read:  Jim Henderson, Man saw futures back in tiny town / Commodities bring Panhandle wealth

·   Read:  Alton Hill, Day Trading Salary – How much money can you really make?

·   Read:  Top 8 Technical Analysts of All Time Share Their Secrets

 

Assign Assignment 1 - Getting Started with AmiBroker - Up to 2 persons

Point value: 100 points

Due date:  Wednesday, February 7th at 7 PM.

 

Readings and concepts for next week

·  Read:  Installing AmiBroker and loading your own dataset

             Install AmiBroker on a laptop and bring it to class next week.

 

Jan 24 - Technical Analysis (Simple example), AmiBroker Overview

 

Terms for this week:

 

Readings and concepts for next week

·  Read:  Installing AmiBroker and loading your own dataset

             Bring AmiBroker to class next week.

·  Read:  Week 03 Notes: Unit 2 - Timing the Market: Technical Indicators

·  Read:  Steven B. Achelis, Technical Analysis from A to Z

·  Read:  Introduction to Technical Indicators and Oscillators

·  Read:  Technical-Indicators

·  Read:  Technical Analysis Tutorial (Investopedia)

·  Read:  Does Tech Analysis Work?

 

·   Reference:  Reference: Glossary of Technical Indicators

·   Reference:  TA Books bibliography

Terms for next week: Fundamental Analysis, Technical Analysis, Technical Indicator, Formula-based indicators, Function-based indicators, Formation-based indicators, overlay indicators, separate indicators, indicator - desired features (robust, reliable, early entry), market indicators, individual indicators, technical analysis, triggers, crossover of 2 or more indicators, crossing a threshold, positive (or negative) divergence, financial model

 

Web Pages for Charting

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

B)   http://www.stockta.com/

  

Jan 31 - Introduction to Technical Indicators

 

·   Quiz 1 Search the web and create a list of all technical indicators that you can find. Ignore parameter

              permutations. Save it as a txt file. Email it to boetticher@uhcl.edu by Tuesday, February 6th (7 PM)

 

GDB Cup - Team Identification due at break time

 

Feb 07 - Technical Indicators, Part 2

 

Assign Assignment 2 - Triggers - Up to 2 persons

Point value: 100 points

Due date:  Wednesday, February 21st at 7 PM.

 

·   Assignment 1 Due

 

Readings for next week

·   Read:  Week 05 PapersA PNF_Tutorial

·   Read:  Week 05 PapersB CorePointAndFigureChartPatterns

·   Read:  Week 05 PapersC - Charting Patterns on Price History

      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.

 

Terms for next week: Double Top, Triple Top, Double Bottom, Triple Bottom, Triangles, Wedges, Flags, Pennant, Head and Shoulders

 

Feb 14 - Chart patterns and P&F Charting

 

Readings for next week

·   Read:  TBD

 

 

Feb 21 -  Model Optimization and Forward Testing

 

Assign Assignment 3 -  Model Optimization and Forward Testing- Up to 2 persons

Point value: 100 points

Due date:  Wednesday, March 7th at 7 PM.

 

·   Assignment 2 Due: Triggers

 

Readings for next week

·   Read:  Week 09 PapersA Genetic-Based Trading Rules - A New Tool to Beat the Market With?

·   Read:  Week 09 PapersB A real-time adaptive trading system using genetic programming

·   Read:  Week 09 PapersC Empirical Study of GP Generated Rules

·   Read:  Week 09 PapersD Technical Market Indicators Optimization using Evolutionary Algorithms   

·   Read:  Week 09 PapersE GENETIC ALGORITHMS FOR ROBUST OPTIMIZATION IN FINANCIAL APPLICATIONS

·   Read:  Week 09 PapersF Comparison of Trade Decision Strategies in an Equity Market GA Trader

 

   

Feb 28 -  Genetic Algorithms - Lecture

 

Readings for next week

·   Read:  TBD

 

 

Mar 07 - Genetic Algorithms and AmiBroker

 

·   Assignment 3 Due: FDM Optimization

 

Assignment 4 - Genetic Algorithm Mini-Project

 

GDB Cup - Practice Round - Due Thursday, March 9th, 8 AM (Mail to Boetticher@uhcl.edu)

 

Readings for next week

·   Read:  Week 09 Notes - Genetic Algorithms

 

Mar 14 - ********************** SPRING BREAK **********************

 

Readings for next week

·   Read:  Week 10 Notes - Neural Networks

·   Read:  Week 10 PapersA Applications of ANNs in the Stock Market - A Survey

·   Read:  Week 10 Week 10 PapersB Application of a NN to Tech Analysis Of Stock Market Prediction

·   Read:  Week 10 PapersC Intraday Stock Forecasting

·   Read:  Week 11 Notes - Particle Swarm Optimization

·   Watch Week 11 - Particle Swarm Optimization by Eberhart

·   Read:  Week 11 PapersB Prediction of the S&P 500 and DJIA Stock Indices using PSO

·   Read:  Week 11 PapersC A PSO Approach to Search for Adaptive Trading Rules in the EUA Futures Mkt

·   Read:  Week 11 Week 11 PapersD Design of Stock Trading System for Historical Market Data Using Multiobjective PSO of Tech Indicators

 

Mar 21 - Genetic Algorithms - Lecture (Papers)

 

GDB Cup - Week 01 - Due Thursday, March 22rd, 8 AM (Mail to Boetticher@uhcl.edu)

 

 

Mar 28 - Neural Networks and Particle Swarms Optimization (PSO)

 

Readings for next week

·   Read:  TBD

 

GDB Cup - Week 02 - Due Thursday, March 29th, 8 AM (Mail to Boetticher@uhcl.edu)

 

 

Apr 04 - How to assess a financial model? 

 

GDB Cup - Week 03 - Due Thursday, April 5th, 8 AM (Mail to Boetticher@uhcl.edu)

 

Readings for next week

 

 

******** April 10 – Last day to withdraw ********

 

Apr 11 - Automated Trading

 

 

GDB Cup - Week 04 - Due Thursday, April 12th, 8 AM (Mail to Boetticher@uhcl.edu)

 

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 18 -  Psychology of Trading

 

GDB Cup - Week 05 - Due Thursday, April 19th, 8 AM (Mail to Boetticher@uhcl.edu)

 

Apr 25 - GDB Cup Summary, Money Management, Review for final

 

FOR NEXT WEEK (IF NOT SOONER)

·   Submit:   Final questions by Tuesday, May 1st, 7 PM. This is optional.

                Use the template found on the Google Drive

                Strip out any identifying information (Your name, Student ID number)

                Specify whether you want me to post your questions on the Google Drive.

 

·   Study!

 

 

May 02 - Final Exam

 

 

 

GDB Cup Results - Spring, 2018

Each week resets to 100K

Money Management Constraints Imposed

Team Weekly Results (In Percent)

Final Total

100K Initial

Projected ARR

3/28 4/4 4/11 4/18 4/25
A            --- TBD
Fund Raisers           --- TBD
Market Mogul           --- TBD
Market Trenders            --- TBD
Risky Business            --- TBD
The Money Team           --- TBD
Trend Riders           --- TBD
Veni_Vidi_Vici            --- TBD
Zig Zag           --- TBD

 

 

 

GDB Cup Results - Spring, 2018 - Unrestricted

Each week resets to 100K

Team Weekly Results (In Percent)

Final Total

100K Initial

Projected ARR

3/28 4/4 4/11 4/18 4/25
A            --- TBD
Fund Raisers           --- TBD
Market Mogul           --- TBD
Market Trenders            --- TBD
Risky Business            --- TBD
The Money Team           --- TBD
Trend Riders           --- TBD
Veni_Vidi_Vici            --- TBD
Zig Zag           --- TBD

 

 

 

GDB Cup - Spring, 2016

 

Created with flickr badge.

 

 

GDB Cup - Spring, 2015

.

 

 

GDB Cup - Spring, 2014

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.

  • 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.

  • 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.

  • Attendance Policy: You are expected to attend every class. If you miss more than 1 class, then your course grade will be reduced by 2 points for each lecture missed. Coming late to class on a regular basis will impact your course participation grade. Missing the final presentations will result in a 0 for your final presentation.

  • 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 an actual email I received  from a student. How many errors can you find?

Dear boeticher,

Is there any chance of regrading my final grade. As i'am very nervous in exam i couldn't be able to attempt properly. you know how attentive in class and can u please grade me considering my class participation also or do i have a chance of re exam because c grade draws my gpa low which results in loosing my scholorship, Please consider my request.

Thanks and Regards

Some Student

Common problems:

*   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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boetticher@uhcl.edu


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