| CODE | ARI5123 | ||||||||||||
| TITLE | Intelligent Algorithmic Trading | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | This study-unit investigates methods implemented in quantitative trading strategies with emphasis on automated trading and the application of AI and machine learning techniques to enhance the trade-decision making mechanism. Algorithmic trading is concerned with designing expert systems for such an unpredictable and unstable entity. In this study-unit, students will be introduced to various techniques that are currently used by various financial institutions in order to trade financial markets. Study-unit Aims: The study-unit provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. It will: - Introduce students to the principles of financial markets modelling and give an overview of financial trading; - Expose students to the process of designing machine learning models for algorithmic trading purposes; - Develop modeling skills necessary for solving real-life problems in automated trading; - Provide financial evaluation of the developed trading strategies, and give the knowledge and understanding of the mechanisms driving today`s markets and financial institutions. Learning Outcomes: 1. Knowledge & Understanding: The study-unit provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. Students will: - Understand how the markets work; - Analyse financial modeling and its pitfalls; - Define model based strategies; - Use portfolio optimization strategies and order execution strategies; - Apply of data mining and machine learning based trading strategies (such as Bayesian method Recurrent Neural Networks, Deep Learning models and Evolutionary algorithms); - Understand the Business Environment; - Use quantitative Trading Strategies on Algorithmic Trading Platforms; - Design Intelligent Trading Systems; - Perform Advanced Optimizations and Search Methods. 2. Skills: In this study-unit, students study trading strategies from the popular academic literature and learn the fundamental mathematics and AI aspects of this emerging field. By working on the class projects, they will gain hands-on experience. After satisfactorily completing this unit, the students will have the necessary quantitative, computing, and programming skills in quantitative trading. They are therefore well prepared for a front office role in hedge funds or banks. Main Text/s and any supplementary readings: - Chan, Ernie. Algorithmic trading: winning strategies and their rationale. John Wiley & Sons, 2013. - Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, 2017. - Yves Hilpisch, Python for Finance: Analyze Big Financial Data, O'Reilly Media, 2014. - Prodromos E. Tsinaslanidis, Technical Analysis for Algorithmic Pattern Recognition, Springer, 2017. - Kissell, Robert. The science of algorithmic trading and portfolio management. Academic Press, 2013. - Pruitt, George. The Ultimate Algorithmic Trading System Toolbox+ Website: Using Today's Technology To Help You Become A Better Trader. John Wiley & Sons, 2016. |
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| STUDY-UNIT TYPE | Lecture & Independent Online Learning | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Martha Axiak Vincent Vella (Co-ord.) |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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