CS984 – Evolutionary Computation For Finance 2

TIMETABLE TEACHING MATERIAL
Credits 10
Level 5
Semester 2
Prerequisites CS983 – Evolutionary Computation for Finance 1
Availability Available only to MSc Quantitative Finance students
Contact Lectures: 5 | Labs: 15 | Homework / Private Study: 80
Assessment Individual assignments (50%), and a one-hour examination (50%)
Resit By examination (1-hour)
Lecturer Dr Marc Roper

General Aims

This class aims to build on the foundations CS983 – Evolutionary Computation for Finance 1 – to explore more advanced applications of evolutionary and natural computing, in particular algorithmic trading.

The course is very practical in its nature: much of the learning is achieved via an assessed mini-projects, and students are expected to develop a solutions to problems using neural networks and evolutionary computation techniques, evaluate these on real data sets, and compare them with other more traditional approaches. Consequently, a large amount of self-directed study and learning is expected.

Learning Outcomes

After completing this class participants will be able to:

  • Understand the benefits and opportunities for evolutionary computing in the context of financial applications;
  • Understand the principles of evolutionary computation: genetic programming and genetic algorithms in particular, and also neural networks;
  • Understand how the computational approaches covered in the class may be applied to financial problem solving and understand their limitations;
  • Develop and evaluate practical solutions to finance-based problems.

Syllabus

  • Advanced Neural Network configurations (e.g. RNNs and LSTMs);
  • Algorithmic Trading – This topic looks at the application of both Neural Networks potentially combined with GAs or GP to identify automatically at what point a trade should be executed, and evaluate the effectiveness of the strategy through backtesting.

Recommended Text/Reading

The class lecturer will recommend a mixture of specific and generic references for each part of the course. Where possible, free online material will be recommended.