FE670 Algorithmic Trading Strategies
Course Catalog Description
This course will introduce methods used in quantitative trading strategies with emphasis on automated trading and quantitative finance-based approaches to enhance the trade-decision making mechanism.
Topics explore markets, financial modeling and its pitfalls, factor model-based strategies, high frequency trading, portfolio optimization techniques, order execution strategies and modeling market impact.
We will discuss the standard and alternative datasets used to generate “alpha”. The pro-jects will include the research and development of quantitative trading strategies using industry standards.
High level overview: This course gives an introduction to quantitative trading strategies, execution strategies and their performance measurement.
Prerequisites: Basic knowledge of markets, statistics, time series analysis and reasonable fluency in one of the programming languages: Python or R. R is strongly encouraged.
The course is one of the courses required for the Algo Trading certificate in Financial En-gineering (FE).
- The students will learn the tools and common methodology used in research and devel-opment of quantitative trading strategies.
- The process of finding new “alphas” will be illustrated using available datasets, the pro-jects will illustrate the details of “backtesting” and systematic portfolio construction.
- Most common trading strategies will be discussed in detail, while the exercises and pro-jects will offer the creative opportunities to refine the models.
- At the end of the course the students will be able to analyze and develop strategies inde-pendently, will develop the skills to build optimal portfolios, perform hedging and re-search new non-conventional ideas.
[Required:] Anatoly B. Schmidt, Financial Markets and Trading: An Introduction to Market Micro-structure and Trading Strategies, Wiley, 2010.
[Required:] Frank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm, Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010).
[Required:] Barry Johnson, Algorithmic Trading & DMA, 4Myeloma Press London, 2010.
Extra material will include academic articles and research notes provided by Wall Street Firms.
Additional readings will be provided, as needed.
Assignments will be provided throughout the semester, consisting of problems related to the material taught in the lectures. They are to be handed in on time. No late assignments, without prior approval, will be accepted. There is a project for the course, and a final ex-am. The total grade is a weighted average of the attendance, assignments, project and final exam.
Participation/Class Challenges: 10%
Preliminary Grading Scheme
- A : 93%-100%
- A- : 90%-93%
- B+ : 87%-90%
- B : 83%-87%
- B- : 80%-83%
- C+ : 77%-80%
- C : 73%-77%
- C- : 70%-73%
- D+ : 67%-70%
- D : 65%-67%
- F : 0%-65%
||Course logistics and review of R cod-ing ;
Overview of algorithmic trading, main concepts and key words
| Week 2
||Modern financial markets and trading
| Week 3
||Basics of econometrics ;
Financial price dynamics ;
Overview of time series modeling used in algorithmic trading
Fabozzi, Focardi, Kolm [2,3]
| Week 4
||Price and volatility forecasting
| Week 5
||Performance measures and Technical Analysis
| Week 6
||Sampling theory and arbitrage strate-gies
||Schmidt [11,12.2-3] ;
| Week 7
||Momentum strategies and pair trading
||Research papers will be provided
| Week 8
||Mean variance portfolio theory
||Fabozzi, Focardi, Kolm [5,7]
| Week 9
||Portfolio theory beyond Markowitz
||Fabozzi, Focardi, Kolm [9,10]
| Week 10
||Factor models and smart betas
||Fabozzi, Focardi, Kolm 
| Week 11
||Trading strategies based on alternative data
||Research papers will be provided
| Week 12
||Market making and HFT
| Week 13
||Optimal execution strategies
||Schmidt  , Johnson [5-9]
| Week 14
| Week 15
||Additional topics to be discussed
2020 Fall FE670 Algorithmic Trading Strategies