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|Course ID||Course Name||Instructor||Room Number||Time|
|TTC 202||Computational Finance and High Frequency Trading||Mr Neil Mckenzie||Confirmed on Booking|
Computational finance refers to research that applies advanced computation methods to finance with the aim of exploiting synergy. This typically involves the use of advanced computing techniques, such as computational intelligence, in the study of complex problems in economics and finance. This Diploma explores the synergy between advanced computation methods and finance & economics. Advanced computing methods have been applied to forecasting, bargaining, portfolio optimization and algorithmic trading. Machine learning plays an important role in many of these applications.
To obtain the Advanced Professional Diploma in Computational Finance, students are required to study four Modules. Each module is self-contained.
Module 1: Machine Learning in Computational Finance
Module 2: Constraint Satisfaction
Module 3: Algorithmic Trading
Module 4: High Frequency Trading
Machine learning is a branch of artificial intelligence. The aim is to use computer Programme to find patterns in data, to find trading strategies or to design rules for new markets. As data become abundant, and computers become more powerful, machine learning has become more and more important in practical trading applications. This certificate introduces techniques in machine learning with emphasis on its applications to finance.
Constraint satisfaction is about decision-making. It is about making a large number of decisions and satisfying complex constraints. Constraint techniques have been employed by many companies, including IBM, British Telecom and British Airways. This certificate introduces the techniques in constraint satisfaction with emphasis on its applications to finance.
Algorithmic trading is the only practical approach to high frequency trading. Human can never compete with computers in high frequency trading. Equipped with the same trading strategy, computer Programmes can trade in milliseconds. They can work day and night. They can monitor multiple markets and benefit from trading in all of them. This Certificate provides detailed knowledge of high frequency trading.
Data has become abundant. Instead of using daily closing prices, investors can now use tick by tick data. With such ‘big data’ being available, knowledge in how to analyse big data determines an investor’s competitiveness. This Certificate introduces advanced concepts such as Directional Changes and Market Calculus for analysing big financial data. Their relevance to trading and risk assessment will be explained in detail.
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