COMP0051 Algorithmic Trading

This database contains the 2018-19 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.

Syllabuses from the 2017-18 session are available here.

Academic session

2018-19

Module

Algorithmic Trading

Code

COMP0051

Module delivery

1819/A7P/T2/COMP0051 Postgraduate

Related deliveries

None

Prior deliveries

COMPG015

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Barucca, Paolo

Contributors

Barucca, Paolo

Firoozye, Nick

Module administrator

Nolan, Martin

Aims

The module aims at introducing algorithmic trading or risk premia strategies, their rationales, properties, design and use. These are presented as an introduction to the primary strategies and common themes in algorithmic trading, together with areas for further study and development, including the latest machine-learning methodologies.

The goal is to give a broad overview of strategies in common use, so students can be equipped with methods for implementing these and exploring their known and provable properties.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. Analyse statistically trading strategies
  2. Research, design, and develop new strategies

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MSc Computational Finance
  • MSc Financial Risk Management

Prerequisites:

There are no formal pre-requisites.

Content

Introduction to trading

  • Trading industry
  • Data sources

Trading strategies

  • Trading strategies
  • Exotic strategies
  • Order book dynamics
  • Portfolio theory

Statistical analysis of strategies

  • Evaluating strategies
  • Sharpe Ratio and other metrics
  • Multiple hypothesis testing and model validation

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures, tutorials, seminars, written and programming exercises, and project work.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Written examination (2hrs 30mins)

60

 

2

In class test 1

20

 

3

In class test 2

20

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.

Resources

Recommended Books:

  • Cartea, Álvaro, Sebastian Jaimungal, and José Penalva. Algorithmic and high-frequency trading. Cambridge University Press, 2015.
  • Chan, Ernie. Algorithmic trading: winning strategies and their rationale. Vol. 625. John Wiley & Sons, 2013.
  • Lecture Slides, Handouts
  • Jupyter Notebooks