Algorithmic Trading - BUSN9194

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Canterbury
Spring Term 7 15 (7.5) Nikolaos Voukelatos checkmark-circle

Overview

This module will provide students with a core understanding of algorithmic trading, and specifically how to develop and implement quantitative trading strategies. The module will cover the following indicative topics
• High-frequency trading and tick data
• Backtesting and automated execution
• Mean reversion strategies
• Momentum strategies
• Arbitrage strategies
• Risk management
• Performance evaluation

Details

Contact hours

• Total contact hours: 35
• Private study hours: 115
• Total study hours: 150

Method of assessment

Main assessment methods:
Individual report - 1,500 words (30%)
Individual research project – 3,000 words (70%)

Reassessment methods:
Individual research project (100%)

Indicative reading

E. Chan, "Algorithmic Trading: Winning Strategies and their Rationale", 2013, Wiley, ISBN: 9781118746912
I. Aldridge, “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems”, 2009, Wiley, ISBN: 9780470579770
P. Kaufman, “A Guide to Creating a Successful Algorithmic Trading Strategy”, 2016, Wiley, ISBN: 9781119224754

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Demonstrate knowledge and understanding of the advanced concepts and theory within the field of finance and financial technology, and their application to a company's financial decisions
- Apply the research methodologies required to test and evaluate complex finance models
- Demonstrate an in-depth knowledge and understanding of theoretical and practical aspects of algorithmic trading in financial markets
- Demonstrate knowledge and understanding of up-to-date empirical literature in the fields of algorithmic trading and investing
- Apply complex quantitative and statistical methods on financial data

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Interpret complex financial data and perform quantitative analysis
- Interpret and systematically evaluate the results obtained from quantitative analysis
- Demonstrate and apply in-depth problem-solving skills
- Analyse complex issues relevant to companies' financial decisions
- Conduct systematic research in the area of finance and financial technology

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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