- 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.
- 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).
Students will be pointed to research papers on the relevant topics that are appropriate for level 2.
At the end of the module students will be expected to:
- Have an understanding of market microstructure and its impact on trading.
- Understand the spectrum of computer-based trading applications and techniques, from profit-seeking trading strategies to execution algorithms.
- Be able to design trading strategies and evaluate critically their historical performance and robustness.
- Understand the common pitfalls in developing trading strategies with historical data.
- Understand the benchmarks used to evaluate execution algorithms.
- Understand methods for measuring risk and diversification at the portfolio level.
Formal Lectures and Seminars: Students will be expected to attend three hours of formal lectures and seminars in a typical week. Formal lectures will be used to introduce students to the concepts and methods covered by the module.
Practicals: Students will be expected to attend one hour of computer lab practicals and tutorials in a typical week. Computer lab practicals are intended to allow students to undertake practical exercises with the possibility of immediate feedback.
Private study: In a typical week students will be expected to devote 6 hours of unsupervised time to private study; private study will provide time for reflection and consideration of lecture material, reading of background material (as advised by the module co-ordinator), and completion of the assessment tasks.
Assessment: Two continuous assessment tasks will be used to test to what extent students are able to apply the methods taught in this module to trading problems. A written examination at the end of the module will assess the academic achievement of students.