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CSCI 5832 -- Financial Data Mining (Graduate)

CINF 5832 -- Financial Data Mining (Graduate)
Updated  November 28, 2023

Office and Addresses

Delta 171 Phone 281.283.3805
Secretary: Ms. Caroline Johnson Delta 161 281.283.3860

Face-to-Face Class Hours

Wednesday 7:00 - 9:50, Room: Delta 241, or via Zoom (If necessary)
Zoom information may be found in the Google folder for this course.

Office Hours

Wed 12 - 4 PM, Thurs 9 - 10 AM, or by appointment. Students with appointments have priority. 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. A Zoom session is also possible.

Teaching Assistants

Mr. Angelo Gomez

TA Hours: Monday 10-12, 5-8, Tuesday 1-5, 7-10, Wednesday 1 - 3





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 and how to apply machine learners in order to construct profitable models. The culminates in the development of an Trading Bot.

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;

  • Understand what is a backtest;

Course Deliverables


By the end of the course, you will created the following deliverables in Python:

  • A tool for reading historic financial data, or live financial data

  • Create a set of technical indicators and chart patterns

  • Use a set of technical indicators and chart patterns to make effective buy/sell decisions

  • Construct a Backtester to assess various financial models;

  • Optimize a trading model.

  • Apply one or more Machine Learners (ML) to make buy/sell decisions

  • Construct a Trading Bot capable using live streaming data


The prerequisites for this course are at least one programming course or experience in Python. 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 assumes no previous knowledge of finance.



Face-to-face lecture and interactive problem solving.


 Base Grade


 Homework/Quizzes (Cumulative average=80% or higher)


 Deliverables (There will be documentation that explains these deliverables.


      Code at least 5 technical indicators in Python (Common Group)


      Code at least 3 technical indicators in Python (Advanced Set)


      Code two simple triggers in Python and one advanced trigger in Python


      Create a general-purpose Backtest program that uses realistic settings


      Create a financial model using ChapGPT3 (or higher) (3 if it profitable)


      Create a financial model using one mach. learner (GA, NN, Reinf. Learner)


      Create a realistic automated trading system




 GDB Cup (Conservative: Interactive Brokers setting)


      First Place and you perform better than Dr. Boetticher

Automatic A + $100/ per member

      First Place


      Second Place


      Third Place


      Financial model is overall profitable


 GDB Cup (Aggressive: Infinity Futures settings):


     First Place


     Financial model is overall profitable


Grades will be based solely on criteria listed above. No other factors will be considered.

Grading Scale:

    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 and/or tutorials.


Schedule (Tentative)



***   All course materials are located on the Google Drive.          ***

***   You are expected to bring a copy of the notes to all lectures. ***

***   I highly recommend you place the notes in a 3-ring binder.     ***



Aug 23 - Overview Financial Data Mining Terminology and Concepts


Assignment 01: Read and Display Financial Data

Due date:  Wednesday, September 6th at 7 PM.


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,


Readings for this week

·  Read: WK01 Notes - Introduction

·   Read: WK01A Paper - Man saw futures back in tiny town

·   Read: WK01B Paper - Day Trading Salary

·   Read: WK01C Paper - Algorithmic Trading Review


Aug 30 - Overview Financial Data Mining Terminology and Concepts - Part 2, Code examples, GDB Cup


Quiz 1 Due at noon via email.  See week 1 notes page 4.


Readings and concepts for next week

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

·  Read:  WK03 PapersA - Introduction to Technical Analysis

·  Read:  WK03 PapersB - Technical Indicators

·  Read:  WK03 PapersC - Technical indicators - The tools of the trade

·  Read:  WK03 PapersD - 12 Types of Technical Indicators Used by Stock Traders

·  Read:  WK03 PapersE - Best 25 Technical Indicators Every Trader Should Know

·  Read:  WK03 PapersF - Python Backtrader_ A Comprehensive Guide to Algorithmic Trading and Backtesting


·   Reference:  WK03 Reference01 - Glossary of Technical Indicators

·   Reference:  WK03 Reference02 - TA Books bibliography

·   Reference:  WK03 Reference03 - List of Technical Indicators

·   Reference:  WK03 Reference04 - What Is TA-LIB


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




Sep 06 - Timing the Market with Technical Indicators, Triggers


Quiz 2 Due by Noon


GDB Cup: Team Identification Form needed by the end of the break


Assignment 1 Due


Assignment 2: Assignment 02 - Create a Set of Technical Indicators

Due date:  Wednesday, September 20th at 7 PM.


Sep 13 - Technical Indicators in greater details


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


Sep 20 - Charting, Chart Patterns and PNF


Assignment 2A Due


Assignment 2B: Assignment 03 - Triggers

Due date:  Wednesday, October 4th at 7 PM.


Readings for next week

·   Read:  TBD


Sep 27Introduction to Backtesting - Part 1


Assignment 2B Due


Assign Assignment 4 -  Backtesting

Point value: 100 points

Due date:  Wednesday, October 11th at 7 PM.


Readings for next week

·   Read:  Week 09 Notes - Genetic Algorithms

·   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 PapersF Comparison of Trade Decision Strategies in an Equity Market GA Trader



Oct 04 - Introduction to Backtesting - Part 2


Assignment 3 Due


Assign Assignment 4 -  Backtesting

Point value: 100 points

Due date:  Wednesday, October 11th at 7 PM.


Readings for next week

·   Read:  TBD


Oct 11 - How to assess a financial model?



Readings for next week


Oct 18 - Model Optimization and Walk Forward Testing


Assignment 4 Due


Readings for next week


Oct 25 - Machine Learning – Part 1


Readings for next week


Nov 01 - Machine Learning – Part 2


GDB Cup - Practice Round - Due Thursday, November 2nd, 8 AM (Mail to


Readings for next week

·   Read:  Course materials on the Google drive

Terms for next week: Maximum Drawdown (MDD), Sharpe Ratio, Sortino Ratio, Sterling Ratio,


******** November 6 – Last day to withdraw ********



Nov 08 - Automated Trading: Building a Trading Bot




GDB Cup - Round 1 - Due Thursday, November 9th, 8 AM (Mail to


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


Nov 15 -  Money Management, Risk Mitigation


GDB Cup - Round 2 - Due Thursday, November 16th, 8 AM (Mail to


Nov 22 - Thanksgiving - No Class


GDB Cup - Round 2 - Due Thursday, November 23rd, 8 AM (Mail to



Nov 29 - Market Scanners


GDB Cup - Round 4 - Due Thursday, November 30th, 8 AM (Mail to





Dec 06 - GDB Cup Final Results


Peer Review form due at 7 PM via email.


GDB Cup Final results



GDB Cup Results - Fall, 2023

Each week resets to 100K

Money Management Constraints Imposed (IB)

Team Weekly Results (In Percent)

Current Total

100K Initial

Projected ARR

in Percent

11/22 11/29 11/30
AlgoTraders (0.051) (0.046)   $99,903  
Capital Miners (3.143) (0.351)   $96,517  
Freshmen of Finance 2.522 (8.224)   $94,090  
KAZ (0.804) (7.209)   $92,045  
The Invisible Foot (1.045) (4.148)  



        Blue background = Incurred a penalty for that week.


GDB Cup Results - Fall, 2023

Each week resets to 250K

Unrestricted (Infinity Futures)

Team Weekly Results (In Percent)

Current Total

250K Initial

Projected ARR

in Percent

11/22 11/29 11/30
AlgoTraders (0.003) (0.0566)   $249,851  
Capital Miners 1.122 0.662   $254,479  
Freshmen of Finance 0.152 (5.248)   $237,241  
KAZ (6.543) (8.377)   $214,070  
The Invisible Foot (1.853) (1.900)   $244,797  

        Blue background = Incurred a penalty for that week.

Other Policies

This class has 6 simple rules:

1) Be respectful of others.

2) Be very passionate about your learning and do your best.

3) Be fearless - ask lots of questions in class.

4) Don't be late on anything.

5) Don't ever cheat.

6) Have fun!


  • 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!

  • If there is any religious observance that may interfere with any scheduled exam, homework due date, or attending class, please notify me of the situation during the first 2 weeks of class so that adjustments can be made at that time.

  • Please turn off all cell phones, and pagers prior to the start of class.

  • 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 your Candidate Plan of Study (CPS) as soon as possible. I am normally not available for advising during the summer months.

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

© 2009 - 2023 Boetticher: Financial Data Mining Course, All Rights Reserved.

Undergrad courses taught by Dr. Boetticher
Graduate courses taught by Dr. Boetticher