STAT8020A - Quantitative strategies and algorithmic trading

Semester 2, 2022-23

Professor
Eric Li
Syllabus Quantitative trading is a systematic investment approach that consists of identification of trading opportunities via statistical data analysis and implementation via computer algorithms.  This course introduces various methodologies that are commonly employed in quantitative trading.

The first half of the course focuses at strategies and methodologies derived from the data snapshotted at daily or minute frequency.  Some specific topics are: (1) techniques for trading trending and mean-reverting instruments, (2) statistical arbitrage and pairs trading, (3) detection of “time-series” mean reversion or stationarity, (4) cross-sectional momentum and contrarian strategies, (5) back-testing methodologies and corresponding performance measures, and (6) Kelly formula, money and risk management.  The second half of the course discusses statistical models of high frequency data and related trading strategies.  Topics that planned to be covered are: (7) introduction of market microstructure, (8) stylised features and models of high frequency transaction prices, (9) limit order book models, (10) optimal execution and smart order routing algorithms, and (11) regulation and compliance issues in algorithmic trading.
Course Objectives

The course is divided into 2 portions with the first one focuses mainly at investment strategies and methodologies derived from the data snapshotted at daily or minute frequency and the second portion discusses statistical models of high frequency data and related trading strategies.

The course objectives are:

  1. To learn some stylized features of financial data;
  2. To build statistical models that capture those stylized features;
  3. To understand some basic theories of quantitative trading;
  4. To be able to implement some trading strategies by using statistical software;
  5. To backtest corresponding strategies;
  6. To learn some regulation and risk management aspects of the business of quantitative trading.
Learning Outcomes
Course Learning Outcomes
CLO1. Upon successful completion of the course, students should be able to demonstrate ability to analyze financial market data.
CLO2. Upon successful completion of the course, students should be able to use a spectrum of modeling skills to investigate and summarize stylized features of the market data.
CLO3. Upon successful completion of the course, students should be able to demonstrate skills in designing and implementing systematic investment trading strategies.
CLO3. Upon successful completion of the course, students should be able to illustrate knowledge in algorithmic trading operations and regulations.
 
Pre-requisites Pass in STAT6013 Financial data analysis or equivalent
Compatibility Nil
Topics covered
Course Content
1. Techniques for trading trending and mean-reverting instruments
2. Statistical arbitrage and pairs trading
3. Detection of “time-series” mean reversion or stationarity
4. Cross-sectional momentum and contrarian strategies
5. Back-testing methodologies and corresponding performance measures
6. Money and risk management
7. Introduction of market microstructure
8. Stylized features and models of high frequency transaction prices
9. Limit order book models
10. Optimal execution and smart order routing algorithms
11. Regulation and compliance issues in algorithmic trading
 
Assessment
Description Type Weighting * Tentative Assessment Period /
Examination Period ^
Written Assignment and project Continuous Assessment 50% -
Written exam covers all taught content in the course Written Examination 50% 3 - 23 May 2023
* The weighting of coursework and examination marks is subject to approval
^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students must oblige to the examination schedule.  Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course.  There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials Selected course materials will be posted on the Moodle.
Session dates
Date Time Venue Remark
Session 1 20 Jan 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 2 3 Feb 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 3 10 Feb 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 4 17 Feb 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 5 24 Feb 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 6 3 Mar 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 7 17 Mar 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 8 24 Mar 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 9 31 Mar 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 10 14 Apr 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 11 21 Apr 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
Session 12 28 Apr 2023 (Fri) 7:00pm - 10:00pm KK-202 Face-to-face
KK - K.K. Leung Building
Add/drop 16 January, 2023 - 4 February, 2023
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