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Algorithmic Trading (FNCE30010)
Undergraduate level 3Points: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
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:
- Explain the key features of the microstructure of financial markets
- Successfully trade in a number of different trading systems
- Conceive of, and if with computer skills, program, algorithms for the automatic execution of trading strategies
- Differentiate between types of trading strategies
- Back-test algorithmic traders or test them in an experimental setting
- Opine in an informed way about the advantages and drawbacks of algorithmic trading
Generic skills
On successful completion of this subject, students should have improved the following 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: 11 April 2024
Eligibility and requirements
Prerequisites
Algorithmic Trading is limited to 30 students per semester. Permission of the Subject Coordinator must be granted prior to enrolment. Students must email the Subject Coordinator stating how they meet the eligibility requirements. All applicants will also be invited to a 15-minute interview.
Students must also have completed:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
FNCE30001 | Investments |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
AND
One of (FNCE30001 may be taken concurrently):
and one of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
ECOM20001 | Econometrics 1 |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
ECON20003 | Quantitative Methods 2 |
Summer Term (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
MAST20005 | Statistics |
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
MAST20004 | Probability |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
MAST20006 | Probability for Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
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: 11 April 2024
Assessment
Additional details
- A Group Assignment (at most 3 per group/term report) comprising one report of between 2000 and 2500 words, due week 12, (40%)
- 5 in-class, online quizzes, 15 minutes each, throughout semester (30%)
- 2 Algorithmic Development Tasks of 1200 words (30%)
Last updated: 11 April 2024
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 120 hours Teaching period 29 July 2019 to 27 October 2019 Last self-enrol date 26 November 2018 Census date 31 August 2019 Last date to withdraw without fail 27 September 2019 Assessment period ends 22 November 2019 Semester 2 contact information
Time commitment details
Estrimated total time commitment is 170 hours.
Additional delivery details
Algorithmic Trading is limited to 30 students per semester. Enrolment is by email application only to the Subject Coordinator, stating how you meet the eligibility requirements. All applicants will be invited to a 15-minute interview.
Last updated: 11 April 2024
Further information
- Texts
Prescribed texts
You will be advised of prescribed texts by the subject coordinator.
- Subject notes
Enrolment in Algorithmic Trading is capped at 30 students per semester. To enroll in this subject, please email the subject coordinator specifying how you meet the subject requirements.
- Breadth options
This subject is available as breadth in the following courses:
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 11 April 2024