English   Danish

2022/2023  KAN-CDSCV1004U  Introduction to Algorithmic Trading: agent-based simulation and high-frequency trading

English Title
Introduction to Algorithmic Trading: agent-based simulation and high-frequency trading

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 120
Study board
Master of Science (MSc) in Business Administration and Data Science
Course coordinator
  • Nicholas Skar-Gislinge - Department of Management, Politics and Philosophy (MPP)
Main academic disciplines
  • Finance
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 01-02-2022

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Summarize different fundamental concepts, techniques and methods of algorithmic trading
  • Design, implement and evaluate a trading algorithm.
  • Demonstrate basic understanding of mathematical and statistical foundations used in algorithmic trading. In particular financial time series.
  • Demonstrate understanding of the use and limitations of agent-based modelling for modelling financial markets
  • Exhibit deeper knowledge and understanding of the topics and reflect on the course topics, with written skills to accepted academic standards.
  • Display critical awareness of the methodological choices in the mandatory assignment
Course prerequisites
The programming language python will be used throughout the course. While its not required that the students are familiar with python, they must expect that the course will require extra work if they are not. Alternatively its possible to use excel with some constraints of the amount of data that can be used.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 1
Oral presentations etc.
Each students has to submit a short report (max. 4 pages), presenting and testing a trading algorithm of their own making. The report can be made individually or in groups of 2-4 students. Students may chose to use the work done in the class sessions and mini projects. In addition to the report, each student has to provide constructive feedback on another student/groups report in a peer feedback setup.

If a student cannot hand in / participate due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the ordinary exam: a written report of 10 pages on a topic assigned by the course instructor. Please note that a blank report is not considered making a real attempt.
Examination
Introduction to Algorithmic Trading: Agent based modelling and high-frequency trading:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Report
Duration 7 days to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Autumn
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The exam is a written product of no more than 10 pages adhering to the CBS standard for witten products. The student will be asked to discuss a number of questions based on the topics covered in the course.

 

Course content, structure and pedagogical approach

The financial sector is becoming increasing reliant on algorithms to conduct and identify trading opportunities. This course provides an introduction to algorithmic trading, covering the key strategies employed in algorithmic trading and hands-on experience with design of simple trading algorithms.

 

Furthermore, this course provides knowledge about:

 

•      Overview of electronic trading venues  

•      Overview of players in modern markets

•      Statistical features of markets

•      High frequency trading

•      The role of latencies

•      Design of a trading algorithm

•      Machine learning in algorithmic trading

•      The role of regulation in electronic markets

•      Agent based modelling

 

After completing the course, the students will know the key strategies used in algorithmic trading and be able to design and test simple algorithms.

Description of the teaching methods
The course follows a blended learning format and each week will contain the following components, one or more pre-recorded lectures on the topic of the week, a mini-project for the student to work on, and a class session where we will discuss the lectures and the mini-project for this week.

The mini projects will be released online with the lectures and aim to give the students a hands-on experience with the topics in the course. They will give the students the tools and components that the students need to make, test and evaluate a simple trading algorithm in the last mini-project and the final report. These are to be completed during the week and are followed by the class session at the end of the week.

During the class session the students will work on the the mini-projects. In addition some time will be set aside to discuss the mini-project from last week as well as the current lecture.

The students need to submit a short report presenting and testing a trading algorithm of their own making, before the exam. This may be done either individually or as a group. They may choose to use an algorithm from the class sessions. Some time in the class sessions will be dedicated for this. In addition each student is required to provide feedback for another report.
Feedback during the teaching period
Feedback to the students as a group is provided in the weekly class sessions, here some time will be assigned for discussing and following up on questions for the weekly lectures and the mini-project from last week.

Feedback on an individual (or small group) basis is available during the part of the class session where the students work on their mini-projects.

Finally it is possible to schedule a meeting during office hours.

Furthermore the students will also provide peer-feedback on their report of a trading algorithm.
Student workload
Class teaching 32 hours
Preperation for classes 144 hours
exam 30 hours
Expected literature

The course follows the book All about High-Frequency Trading, by Michael Durbin,  (2011 McGraw Hill). Some chapters in the book will be supplimented by a number of academic research papers in order to provide more in-depth/or up-todate coverage of selected topics. These will be provided on canvas.

 

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any materials.

Last updated on 01-02-2022