Computer-Based Trading in Financial Markets


  • 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.


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

Recommended Texts

Students will be pointed to research papers on the relevant topics that are appropriate for level 2.

Learning Outcomes

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.

Learning Strategy

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.