Algorithmic Trading|High-Frequency Trading|Algotrading|Trading Strategies|Order book dynamics|Po

Aims:

The module aims to introduce algorithmic trading or risk premia strategies, their rationales, properties, design and use. These are presented as an...

Aims:

The module aims to introduce 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

Aims:

To introduce primary theoretical constructs in the field of intelligent systems and to examine significant methods illustrating those constructs,...

Aims:

To introduce primary theoretical constructs in the field of intelligent systems and to examine significant methods illustrating those constructs, together with examples of their application.Learning outcomes:

On successful completion of the module, a student will be able to:

Understand foundational theories in artificial intelligence including: the structural dimensions of intelligent agents in relation to the capabilities they achieve in different environments, and; the differences between symbolic and sub-symbolic agent architectures.
	Understand established and emerging methods for providing the primary capabilities of intelligent agents such as their decision-making, problem solving and learning.
	Understand the technical challenges involved in modelling and building intelligent systems for different application settings.
Content:

Systems and intelligence.
	Agents.
	Agent architectures.
	Problem solving.
	Knowledge representation.
	Decision-making.
	Learning.
	Sensing, acting and communicating.
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) have A level Maths (or equivalent); and (2) have English Language at the UCL “Standard” level.

Students wishing to take this IEP Minor must complete a short take-home test which probes programming knowledge amongst others.