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Description
Aims:
The module aims to introduce听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:
- Analyse statistically trading strategies.
- Research, design, and develop new strategies.
Content:
- Introduction to trading.
- Trading industry.
- Data sources.
- Trading strategies.
- Order book dynamics.
- Portfolio theory.
- Statistical analysis of strategies.
- Evaluating strategies.
- Sharpe Ratio and other metrics.
- Multiple hypothesis testing and model validation.
Requisites:
To be eligible to select this module as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) be familiar with fundamental probability and statistics concepts; (3) be familiar with mathematical analysis; and (4) be familiar with a scientific programming language.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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