Algorithmic trading has been revolutionised through exciting developments in big data, cloud storage and AI, making it accessible and feasible for individual investors and small institutions. As a result, algorithmic trading is more relevant than ever before. You'll learn all the theory of algorithmic trading and how to transform this theory into practical skills through hands-on work on Python, programming financial models and trading strategies. This bridges the gap between classical and contemporary theories and gives you the experience you need to build the skills, knowledge and confidence to launch an exciting career in trading.
• Total contact hours: 35
• Private study hours: 115
• Total study hours: 150
Main assessment methods:
Individual report - 1,500 words (30%)
Individual research project – 3,000 words (70%)
Reassessment methods:
Individual research project (100%)
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
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
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