FE670 Algorithmic Trading Strategies

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Course Catalog Description


This course investigates statistical methods implemented in multiple quantitative trading strategies with emphasis on automated trading and based on combined technical-analytic and fundamental indicators to enhance the trade-decision making mechanism. Topics explore high-frequency finance, markets and data, time series, microscopic operators, and micro-patterns. Methodologies include, but not limited to, Bayesian classifiers, weak classifiers, boosting and general meta-algorithmic emerging methods of machine learning applied to trading strategies. Back-testing and assessment of model risk are explored. Prerequisites: FE 545, FE 570
Campus Fall Spring Summer
On Campus X X
Web Campus X X


Professor Email Office
Steve Yang
steve.yang@stevens.edu Babbio 536

More Information

Course Description

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.

Course Resources


[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 Policies

Assignments - 30% Final project - 30% Midterm exam - 30% Paper reviews - 10% 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).

Final Project: You need to form a project team with 3-4 people at most. You will pick a topic related to the course content, and an 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 – May 18 (tentatively)!!!

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]
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]
Week 6 Robust Portfolio Optimization Frank J. Fabozzi [10]
Week 7 Transaction Costs & Trade execution Frank J. Fabozzi [11]
Week 8 Transaction Costs & Optimal Strategies Barry Johnson [7,9]
Week 9 Order Placement & Execution Tactics Barry Johnson [8,9]
Week 10 Enhancing Trading Strategies Barry Johnson [10]
Week 11 Behavioral Finance and News Events-based Trading Strategies Academic papers
Week 12 Pattern Recognition Models: Neuron Network, Genetic Programming Academic papers
Week 13 Pattern Recognition Models: Support Vector Machine & Reinforcement Learning. Switching Experts Academic papers
Week 14 Project Presentation