Handbook home
Algorithmic Trading (FNCE90078)
Graduate courseworkPoints: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
Availability(Quotas apply) | Semester 2 |
---|---|
Fees | Look up fees |
Global equity markets have changed fundamentally over the last decades Regulatory reforms to promote competition for trading services have led to considerable fragmentation of markets. New entrants and new technology have contributed to innovative new trading mechanisms and pricing structures. Today, markets are overwhelming electronic, with trading occurring using algorithms rather than manually. Graduates wishing to pursue careers in financial markets need to understand the new market structure that exists and have skills to understand and implement trading strategies in this environment. This subject will ensure students develop these skills and knowledge, through a combination of lectures and hands-on experience of manual and robot trading in online experimental markets.
The class is quite unique. Despite growing importance of computerised trading in financial markets, there exist hardly any finance classes that expose students to the issues, let alone allowing them to develop the skills to conceive robot traders themselves through participation in experimental online markets.
Intended learning outcomes
The overall aim is to introduce students to the microstructure of modern financial markets in general, and to algorithmic trading in particular. Algorithmic trading refers to the use of robots (automatic order submission computer program) to accomplish a certain trading goal, such as automatic market making, statistical arbitrage, technical analysis, portfolio rebalancing, etc. Students will be given the opportunity to get hands-on experience in purposely designed online financial markets, as manual traders, or as algorithmic traders, depending on programming skills and career concerns.
On successful completion of this subject students should be able to:
- Successfully trade in a number of different trading systems
- Explain the key features of the microstructure of financial markets
- Differentiate between types of trading strategies
- Back-test algorithmic traders or test them in an experimental setting
- Conceive of, and if with computer skills, program, algorithms for the automatic execution of trading strategies
- Opine in an informed way about the advantages and drawbacks of algorithmic trading
Generic skills
- Oral communication
- Written communication
- Problem solving
- Thinking outside the box
- Team work
- Critical thinking
- Evaluation of data and other information
- Using computer software
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
and one of:
COMP10002 or COMP10001 or equivalent
and one of:
ECOM20001 ECON20003 MAST20005 MAST20004 MAST20006
(Note: FNCE30001 may be taken concurrently)
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Knowledge of Python is a benefit.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
Individual assignment comprising one report of between 2000 and 2500 words
| Week 12 | 40% |
5 in-class, online quizzes, 15 minutes each, throughout semester (30%)
| From Week 1 to Week 12 | 30% |
2 Algorithmic Development Tasks of 1200 words
| From Week 1 to Week 12 | 30% |
Last updated: 3 November 2022
Quotas apply to this subject
Dates & times
- Semester 2
Principal coordinator Nitin Yadav Mode of delivery On Campus (Parkville) Contact hours One 2-hour lecture plus one 1-hour laboratory per week Total time commitment 170 hours Teaching period 3 August 2020 to 1 November 2020 Last self-enrol date 14 August 2020 Census date 21 September 2020 Last date to withdraw without fail 16 October 2020 Assessment period ends 27 November 2020
Time commitment details
Estimated total time commitment is 170 hours.
Additional delivery details
The quota of 30 is a joint quota between this subject and FNCE30010.
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Subject notes
Enrolment in Algorithmic Trading is limited to 30 students per semester inclusive of both this subject and FNCE300010 and enrolment is by application only.
Last updated: 3 November 2022