English   Danish

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 First 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
  • Face-to-face teaching
Last updated on 18-03-2020

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.
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
It is also possible to write the exam report individually. The lenght of an individual exam report should is max.10 pages.

Students who write the exam report in a group, have to show what their individual contribution are, and in such a way that it is possible to make an individual assessment.
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

In the rapport the students should give an overview of the field of algorithmic trading, as well as; design, test and evaluate their own trading algorithm. The final practical sessions will be devoted to help the students make an algorithm (if neede) and to generate the data required to make the report. The students should clearly describe the rationale behind the presented algorithm and offer a critical evaluation of the algorithm and finally reflect on the performance in testing compared to actual live trading.

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 will, in addition to lectures on the covered topics, provide the students with practical hands-on experience in implementing and evaluating simple trading algorithms in a series of “lab” practicals. The students are expected to have experience with Excel in order to be able to complete the practicals. While experience with programming languages like python or R will be a help for the student, it is not required.
Feedback during the teaching period
Feedback is provided in the practicals accompanying each lecture, where the lecturer will be present and guide the practical. Each practical will provide some time for discussing the current lecture and will provide hands on experience with the current topics. The last two practical will be devoted to the exam projects, making sure that the students have data and to discuss their ideas and questions.
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 18-03-2020