To develop an understanding of financial markets at the level of individual trades. To provide an overview of the range of different computer-based trading applications and techniques. To introduce the key issues with using historical high-frequency financial data for developing computer-based trading strategies. To provide an overview of statistical and computational methods for the design of trading strategies and their risk management. To develop a practical understanding of the design, implementation, evaluation, and deployment of trading strategies.

(LO1) Have an understanding of market microstructure and its impact on trading.

(LO2) Understand the spectrum of computer-based trading applications and techniques, from profit-seeking trading strategies to execution algorithms.

(LO3) Be able to design trading strategies and evaluate critically their historical performance and robustness.

(LO4) Understand the common pitfalls in developing trading strategies with historical data.

(LO5) Understand the benchmarks used to evaluate execution algorithms.

(LO6) Understand methods for measuring risk and diversification at the portfolio level.

(S1) Self-management (Readiness to accept responsibility (i.e. leadership), flexibility, resilience, self-starting, appropriate assertiveness, time management, readiness to improve own performance based on feedback/reflective learning.)

(S2) Application of numeracy (Manipulation of numbers, general mathematical awareness and its application in practical contexts (e.g. measuring, weighing, estimating and applying formulae).)

(S3) Problem solving (Analysing facts and situations and applying creative thinking to develop appropriate solutions.)

(S4) Application of information technology (Basic IT skills, including familiarity with word processing, spreadsheets, file management and use of internet search engines.)

(S5) Computer Science principles

(S6) Computer Science practice

Introduction and overview of the module (1 Lecture).
An overview of financial markets and instruments (2 Lectures).
Using R for financial modelling (2 Lectures and 2 Practicals).
Market microstructure, the limit order book, and dark pools of liquidity (2 Lectures and 1 Tutorial).
Profit seeking versus execution algorithms (1 Lecture).
Designing and testing trading strategies (4 Lectures and 1 Practical).
Common pitfalls when using historical data for developing trading strategies (2 Lectures and 1 Practical).
Statistical tests for evaluating trading strategies (3 Lectures and 1 Tutorial).
Money management techniques (2 Lectures and 1 Practical).
Price benchmarks for execution algorithms (2 Lectures and 1 Tutorial).
A selection of advanced topics: e.g. Smart order routing; Statistical arbitrage (5 Lectures and 1 Practical/Tutorial).
A guide to trading strategy project work (4 Lectures and 1 Practical).

Teaching Method 1 - Lecture
Description:
Teaching Method 2 - Laboratory Work
Description:

Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions.

(a) Hybrid delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Work
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

(b) Fully online delivery and assessment
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teaching Method 2 - Laboratory Work
Description: On-line synchronous/asynchronous sessions

(c) Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Work
Description: On-campus synchronous sessions