FE670 Algorithmic Trading Strategies



Course Catalog Description

Introduction

This course investigates methods implemented in multiple quantitative trading strategies with emphasis on automated trading and quantitative finance based approaches to enhance the trade-decision making mechanism. The course provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. Topics explore markets, financial modeling and its pitfalls, factor model based strategies, portfolio optimization strategies, and order execution strategies. The data mining and machine learning based trading strategies are also introduced, and these strategies include, but not limited to, weak classifier method, boosting, neural network and genetic programming algorithmic emerging methods

Campus Fall Spring Summer
On Campus X X
Web Campus

Instructors

Professor Email Office
Sheung Yin Kevin Mo
smo@stevens.edu Babbio 536

More Information

Course Description

TThis course investigates methods implemented in multiple quantitative trading strategies with emphasis on automated trading and quantitative finance based approaches to enhance the trade-decision making mechanism. The course provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. Topics explore markets, financial modeling and its pitfalls, factor model based strategies, portfolio optimization strategies, and order execution strategies. The data mining and machine learning based trading strategies are also introduced, and these strategies include, but not limited to, weak classifier method, boosting, neural network and genetic programming algorithmic emerging methods.


Course Resources

Textbook

[Required:] Frank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm, Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010).

[Optional:] Barry Johnson, Algorithmic Trading & DMA, 4Myeloma Press London, 2010.

Grading

Grading Policies

Assignments - 20% Class Challenges - 20% Midterm exam - 30% Final project - 30% Total Grade - 100%

Exams: Two Exams. (Mid-term) EXAM I: March 23 - (Thursday). (Final Presentation) EXAM II: May. 18 - (Thursday).

Exam Honor Policy: You are not allowed to discuss any of the exam questions with one another or to show any of your solutions. The work must be done independently and pledged.

Homework: There will be 4 homework assignments (approximately every 2-3 weeks).

Homework Honor Policy: You are allowed to discuss the problems between yourselves, but once you begin writing up your solution, you must do so independently, and cannot show one another any parts of your written solutions. The HW is to be pledged (that it adheres to this).

Class Challenges: There will be several in-class challenges that students will form groups to work on small projects. It is expected that groups will finish the given task by the end of the class session. These challenges enhance understanding of course materials and advance progress for the final project.

Final Project: You need to form a project team with 2-3 people at most. You will pick a topic related to the course content, and a one-page project proposal needs to be submitted right after the midterm. If you do it right, this can be an immensely satisfying experience. You will turn in this project - I don't want the computer output, but descriptions of the results IN YOUR OWN WORDS - 3 single-spaced pages, including plots, at most. We will talk more about this as the semester proceeds. You will each give a brief presentation on your project to the class, during the last week - Attendance is MANDATORY at the presentations.

Attendance will be taken randomly (e.g., 6-7 times during the semester) and will determine "which direction" the resulting grade will “fall”, for those grades, which are borderline (e.g., between B+ or A-).


Lecture Outline

Topic Reading
Week 1 An Overview of Trading and Markets Barry Johnson [1,2,3]
Week 2 Common Pitfalls in Financial Modeling Frank J. Fabozzi [4], HW1
Week 3 Factor Models and Their Estimation Frank J. Fabozzi [5]
Week 4 Factor-Based Trading Strategies Frank J. Fabozzi [6-7]
Week 5 Portfolio Optimization & Black-Litterman Model Frank J. Fabozzi [8-9], HW2
Week 6 Robust Portfolio Optimization Frank J. Fabozzi [10]
Week 7 Transaction Costs & Trade execution Frank J. Fabozzi [11], HW3
Week 8 Transaction Costs & Optimal Strategies Barry Johnson [7,9]
Week 9 Order Placement & Execution Tactics Barry Johnson [8,9], Exam-1
Week 10 Enhancing Trading Strategies Barry Johnson [10], Project Proposal Due
Week 11 Behavioral Finance and News Events-based Trading Strategies Academic papers
Week 12 Pattern Recognition Models: Neuron Network, Genetic Programming Academic papers, HW4
Week 13 Pattern Recognition Models: Support Vector Machine & Reinforcement Learning. Switching Experts Academic papers
Week 14 Project Presentation
Week 15 Final Project