COMP396 - Honours Year Automated Trading Project
Year: 2011-12
Originating department: Computer Science
Faculty: Faculty of Science & Engineering
Semester: Whole Session
Credit level: Level Three
Credit value: 30
External examiner: Prof. David Robertson
Member of staff with responsibility for the module: Dr RSJ Savani
Board of studies: Board of Studies in Computer Science
Mode of delivery: Continuous Assessment
Contact hours: 5 Lectures, 5 Practicals
Pre-requisites: none
Co-requisites: none
Module description
Aims
- To give students the opportunity to work in a team to explore in depth the problem of automated trading from a practical perspective.
- To provide experience of working in a team.
- To provide experience of all aspects of solving a substantial problem, including the production of a final report.
- To enhance communication skills, both oral and written.
Learning outcomes
At the end of this module students should be able to
- work effectively and cooperatively as part of a team while taking on a range of different roles within the team;
- plan, manage, and execute the project as a group within the time available while managing their individual time effectively so as to carry out the group's plan;
- design and implement trading strategies and evaluate critically their performance and robustness;
- locate and make use of information relevant to their project;
- prepare and deliver a formal presentation showing practical competence and demonstrating aspects of the project;
- document the work conducted in the project in a final report.
Teaching and learning strategies
Background: In the project we wish to foster both self and peer guided learning and
team work, under the guidance of a supervisor. Students are divided into teams of five, and each team
is expected to work largely autonomously on the design of automated trading strategies. The trading
strategies of the students will be evaluated in a competition, and the performance of the trading
strategies will contribute towards the final mark of the students.The students will do their research
and development within the R statistical computing language (www.r-project.org). Financial data will
be made available. Via lectures and practicals, students will be introduced to relevant packages
within R, along with tools and techniques for their projects.
Formal Lectures and Practicals: Students will be expected to attend five hours
of formal lectures and five practicals. The lectures will cover:
- Background information on automated trading.
- The design of trading strategies.
- The optimization and evaluation of trading strategies.
- The format of assessment and feeback within the module.
- Report writing and presentation skills.
Teamwork and Supervision: Teams will be expected to hold regular project meetings, the minutes of which will be monitored by staff. Teams will be allocated a first and second supervisor and will hold regular meetings with their supervisors.
Assessment: Projects are assessed at the following three points:
- Design (oral presentation + documentation, week 8-10)
- Evaluation of trading strategies (submitted code, week 18)
- Final report (written, week 24)
Syllabus
All projects should contain the following elements: research, design, realisation and evaluation.
Recommended texts
C. W. Dawson: Essence of computing projects: a student`s guide. Prentice Hall (most recent edition).
W. N. Venebles, D. M. Smith, and the R development team: An Introduction to R. Available electronically
from http://cran.r-project.org/doc/manuals/R-intro.pdf.
Assessment
100% continuous assessment:
20% - Design Presentation / Documentation
30% - Evaluation of trading strategies
50% - Final report