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 trading strategies, liquidation strategies, arbitrage strategies, and machine learning enhanced strategies. The data mining, machine learning, and artificial intelligencebased trading strategies include, but not limited to, weak classifier method, decision tree, neural network, and genetic programming algorithmic, and other deep learning methods. The course will also introduce emerging technologies in trading decisions such as such as Explainable AI, Large Language Models (LLMs), and Environmental Social and Governance (ESG) investing through use case studies.

Prerequisite:

  • FE570 – Market Microstructure and Trading Strategies

Campus Fall Spring Summer
On Campus X
Web Campus

Instructors

Professor Email Office
Steve Yang
syang14@stevens.edu Babbio 536

More Information

Course Outcomes

After successful completion of this course, students will:

  • Understand the mathematical rationale and algorithms among the major trading strategies including fundamental factors, portfolio trading, execution, and arbitrage strategies.
  • Able to apply advanced statistical, machine learning, and artificial intelligence techniques to the major trading strategies to enhance the trading outcomes.
  • Understand the major machines learning and artificial intelligence techniques and apply them effectively in implementing the major trading strategies using Python programming language.
  • Obtain fundamental knowledge in algorithmic trading and be able to read latest research papers to acquire recent development in the field and enhance the baseline trading algorithms.
  • Understand the full lifecycle of developing a trading system where advanced trading strategies can be effectively incorporated and updated for better performance and operation efficiency.
  • Learn emerging concepts around quantitative investment decision making using Explainable AI, LLMs, and ESG analytics, etc. Students will be equipped with Python programming skills to explore new techniques.

Course Resources

Textbooks

Raja Velu, Maxence Hardy, Daniel Nehren, Algorithmic Trading and Quantitative Strategies, Chapman and Hall/CRC Financial Mathematics Series, 1st Edition (2020) [Required]

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

Stefan Jansen, Machine Learning for Algorithmic Trading: Predictive models to extract signals, Packt Publishing, Ltd. 2nd Edition (2021) [Optional]

Agostino Capponi, Charles-Albert Lehalle, “Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices” 1st Edition (2023) [Optional]

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


Grading

Grading Policies

Weights
1 Homework Assignment 40%
2 Midterm Exam 30%
3 Final Project 30%
4 Total Grade 100%

Lecture Outline

Topic Reading HW
Week 1 An Overview of Trading and Markets Velu, Hardy, Nehren [1],
Barry Johnson [1,2]
Week 2 Basic Models and Empirics Velu, Hardy, Nehren [2,3],
Jansen [2,3]
Week 3 Factor Models and Estimation Fabozzi, Focardi, Kolm [5], Jansen [4] HW1
Week 4 Barra Alpha Factor Trading Strategies Fabozzi, Focardi, Kolm [6-7], Jansen [7]
Week 5 Cross-Sectional Models, Factor Zoo, and Advanced Factor Strategies Fabozzi, Focardi, Kolm [8] Capponi and Lehalle [32]
Week 6 Mean Variance Portfolio Theory Fabozzi, Focardi, Kolm [ Fabozzi, Focardi, Kolm [9] HW 2
Week 7 Portfolio Theory Beyond Markowitz Fabozzi, Focardi, Kolm [10] EXAM 1
Week 8 Robust Portfolio Optimization Velu, Hardy, Nehren [4, 5]
Week 9 Machine Learning and Artificial Intelligence in Trading Research Papers, Jansen [11, 12]
Week 10 Machine Learning Trading Strategies:Random Forests, Ada Boosting,Gradient Boosting Research Papers, Jansen [15, 16, 18, 19] HW 3
Week 11 Trading Strategies Based on Alternative Data: Topic Model and Sentiment Analytics Research Papers, Jansen [20, 21, 22]
Week 12 Deep Learning Strategies: ANN, LSTM,and Recurrent Reinforcement Learning Velu, Hardy, Nehren [8], Capponi and Lehalle [11] Proposal
Week 13 Statistical Arbitrage and Pairs Trading Strategies Barry Johnson [7, 8, 9], Research Papers HW 4
Week 14 Transaction Cost and Trading Execution Barry Johnson [10], Velu, Hardy, Nehren [11, 12], Capponi and Lehalle [12]
Week 15 Final Project Presentation EXAM 2