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2020/2021  KAN-CDASV1903U  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 Second Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 120
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
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 17-08-2020

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 as part of the project and the report should reflect on critical awareness of the methodological choices with written skills to accepted academic standards.
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 Written product to be submitted on specified date and time.
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 students will be evaluated based on a report on algorithmic trading by giving an overview of the topics covered in the course. The report should discuss the following:

The report should at least discuss the following topics from the course:

  1. main products traded in financial markets 
  2. The various actors in financial markets, how they are connected, and an overview of the strategies they use.
  3. A description of the various venues
  4. Discussion of orders types and data available.
  5. Give an overview of the so-called stylized facts
  6. Describe how agent-based modeling can is used to model financial markets
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. These are to be completed during the week and are followed by the class session at the end of the week.
Feedback during the teaching period
Feedback is provided in the weekly class sessions, that follows up on the mini-projects and lectures. This will also allow the students to post follow up questions and requests for clarifications.
Student workload
Class teaching 32 hours
Preperation for classes 144 hours
exam 30 hours
Expected literature

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

Last updated on 17-08-2020