| Unit name | Algorithmic Trading |
|---|---|
| Unit code | ACFIM0003 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Friederich |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
ACFIM0005 - Quantitative Methods, Big Data and Machine Learning |
| Units you must take alongside this one (co-requisite units) |
ACFIM0004 - Empirical Finance for Financial Technology |
| Units you may not take alongside this one |
None |
| School/department | School of Accounting and Finance - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
Numerous hedge funds and investment banks use algorithmic trading as a source of significant profits.As a result, they are a source of asset price movements. Analysing how they operate supports a solid understanding of how financial markets function and why they can break down (such as the occurrence of ‘flash crashes’). The development and testing of trading rules also represent a key application of machine learning tools.
How does this unit fit into your programme of study?
This unit builds upon the Quantitative Methods, Big Data and Machine Learning studied in Teaching Block 1, to present, understand and operationalise algorithmic trading rules using a programming language. The unit makes use of the statistical models taught in TB1 and concurrently in Empirical Finance for Financial Technology to develop and appraise trading rules. It is likely that many students will employ the techniques and practical skills learned on this unit in their dissertations.
An overview of content
The unit will begin by examining the market structures that exist and explaining how an order book operates. It will then proceed to present and discuss different types of trading strategies that can be utilised. The characteristic features of high frequency financial data will be examined. Students will be taught how to code up a trading strategy, back-test it and evaluate its performance using a range of metrics. Techniques for risk measurement and risk management of trading strategies will be illustrated using real data. The potential impact of algorithmic trading on the efficiency of asset markets and its interaction with the wider market will be highlighted and discussed.
How will students, personally, be different as a result of the unit
Students will deepen their analytical abilities and will be able to translate abstract trading ideas into functioning coded models. In so-doing, students will develop new industry-relevant trading and programming skills.
Independent learning outcomes
Upon successful completion, students will be able to:
The conceptual details of market microstructure and trading strategy development will be presented in lectures. Via case studies and computer-based simulations of financial market variables, students will learn how hedge funds and other market participants detect and exploit predictable patterns in financial data. This unit will be closely linked with industry practices and will be highly practical, with one of its primary purposes to provide students with the tools to be able to implement their own algorithms. Computer lab sessions will be used for this purpose, where students will be provided with existing code which they can then modify to suit different objectives, applying it to real financial data. The implementations will not be mechanical, but will begin from a high-level understanding of how to design and structure code from first principles.
Tasks which help you to learn and prepare you for summative tasks (formative):
Students will participate in tutorials where non-assessed problem sets that they have prepared in advance will be discussed and generic oral feedback will be offered. There will also be computer lab classes where students will work individually and in small groups to see how financial markets function and they will implement and evaluate trading strategies using a programming language (such as Python).They will be provided with oral feedback on their progress and solution code will be distributed on problems not covered during the lab sessions.
Tasks which count towards your unit mark (summative):
Students will individually implement relevant application of algorithmic trading, which could, for example, involve setting up a simulated market or technical trading rule, which they will then test in a programming language (e.g., Python), ILO1, ILO2, ILO3 and ILO4. Students will write a report of up to 2,000 words describing their methods and findings. They will be provided with the opportunity to attend clinics during the time that they are preparing their projects to gain support and feedback on any problems they are encountering. Students will submit their code and report for assessment, which together will constitute 100% of the mark for the unit.
When assessment does not go to plan
Students will work on a further individual project along the same lines as the original assessment and will submit their code and report for grading.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ACFIM0003).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
