With the development of electronic markets, many investment firms are increasing their spending on technology for electronic trading. In particular, traders are relying more on software and algorithms to analyse market conditions and then execute their orders. High-frequency trading (HFT) is a special form of electronic and algorithmic trading characterised by the use of complex computer algorithms and fast reallocation or turnover of trading. This course will present some classic high-frequency trading algorithms and their underlying mathematics. The students will also be trained to be familiar with programming and data analytics skills which are essential for prospective algorithmic trading professionals.

One two-hour lecture per week for 10 weeks (on-campus)

One one-hour lab per week for 10 weeks (on-campus)

Asynchronous learning activities will include algorithmic trading and directed reading for approximately 5 hours.

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Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The Hackathon and presentation assessment cannot be reassessed as it is a group task

This course aims at providing students with:

■ An in-depth understanding of the market micro-structure of modern electronic financial markets.

■ The ability to perform and develop high-frequency trading algorithms and deal with high-frequency financial data.

By the end of this course students will be able to:

1. Describe different mechanics of the order book and the operation of different types of auctions at different times of the day.

2. Apply important order types.

3. Collect and analyse high-frequency financial data in a professional environment.

4. Implement and develop high-frequency trading algorithms using Python and C++ and real financial data.

5. Evaluate the performance of a trading algorithm from different aspects.

6. Develop teamworking skills through collaboration with peers on solving complex trading tasks.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.