Course Details in 2024/25 Session

Module Title	LM Algorithmic and High Frequency Trading
School	Mathematics
Department	Mathematics
Module Code	06 39816
Module Lead	TBC
Level	Masters Level
Credits	10
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-24 hours Guided independent study-76 hours Total: 100 hours
Exclusions
Description	This module provides students with a comprehensive understanding of the principles, strategies, and mathematical techniques involved in algorithmic trading and high-frequency trading in financial markets. The module focuses on the application of computational methods and quantitative analysis to facilitate efficient and automated trading strategies. The module introduces students to the fundamental concepts of financial markets, including market microstructure, order types, and the role of exchanges and liquidity providers. Students will gain an understanding of the advantages and challenges associated with algorithmic and high-frequency trading and their impact on market dynamics. Students will explore various algorithmic trading strategies, such as market making, statistical arbitrage, trend following, and execution algorithms. They will learn how to design, implement, and backtest these strategies using statistical programming languages such as Python or R. Emphasis will be placed on the development of trading systems that utilize real-time market data, optimize trade execution, and manage risk effectively. The module will delve into market data analysis and quantitative methods used in algorithmic trading. Students will learn how to analyze historical price and volume data, identify patterns, and develop statistical models for forecasting asset prices and market trends. They will also explore techniques such as machine learning and artificial intelligence for creating predictive models and enhancing trading strategies. The module will introduce students to the technological infrastructure required for algorithmic and high-frequency trading, including low-latency systems, connectivity, data feeds, and co-location. Students will gain practical experience in implementing trading algorithms, managing data streams, and handling real-time market data through hands-on programming exercises and simulations. Risk management and regulatory considerations in algorithmic trading will be discussed, including market impact, liquidity risk, and compliance with relevant regulations and best practices. Students will learn how to evaluate and manage these risks effectively, ensuring the sustainability and stability of their trading strategies. Throughout the module, case studies and real-world examples will be used to illustrate the application of algorithmic and high-frequency trading techniques in different asset classes, such as equities, futures, and foreign exchange. The ethical considerations and potential impact of algorithmic trading on market fairness and stability will also be examined.
Learning Outcomes	By the end of the module students should be able to: By the end of the module, students will have developed a solid understanding of algorithmic and high-frequency trading strategies, techniques, and their applications in financial markets. They will be equipped with the skills to design, implement, and evaluate automated trading systems, making them well-prepared for careers in quantitative finance, proprietary trading, risk management, or financial technology.
Assessment
Assessment Methods & Exceptions	Assessment: 80% examination, 20% coursework. Reassessment: Resit examination
Other
Reading List