Algorithmic trading

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Single page view for printing. Please refer to the specific study period for contact information. Global equity markets have changed fundamentally over the last decades Regulatory reforms to promote competition for trading services have led to considerable fragmentation of markets.

New entrants and new technology have contributed to innovative algorithms for trading trading mechanisms and pricing structures. Today, markets are overwhelming electronic, with trading occurring using algorithms rather than manually. Graduates wishing to pursue careers in financial markets need to understand the new market structure that exists and have skills to understand and implement trading strategies in this environment. This subject will ensure students develop these skills and knowledge, through a combination of lectures and hands-on experience of manual and robot trading in online experimental markets.

The class is quite unique. Despite growing importance of computerised trading in financial markets, there algorithms for trading hardly any finance classes that expose students to the issues, let alone allowing them to develop the skills to conceive robot algorithms for trading themselves through participation in experimental online markets.

The overall aim is to introduce students to the microstructure of modern financial markets in general, and to algorithmic trading in particular. Algorithmic trading refers to the use of robots automatic order submission computer program to accomplish a certain trading goal, such as automatic market making, statistical arbitrage, technical analysis, portfolio rebalancing, etc.

Students will be given the opportunity to get hands-on experience in purposely designed online financial markets, as manual traders, or as algorithmic traders, depending on programming skills and career concerns.

On successful completion of this subject, students should have improved the following generic skills:. Undergraduate subjects Algorithms for trading subjects Research subjects. Breadth search Breadth tracks. About this subject Overview Eligibility and requirements Assessment Dates and times Further information Timetable opens in new window Single page view for printing Contact information Please refer to the specific study period for contact information.

Intended learning outcomes The overall aim is to introduce students to the microstructure of algorithms for trading financial markets in general, and to algorithmic trading in particular. On successful completion of this subject students should be able to: Explain the key features of the algorithms for trading of financial markets Successfully trade in a number of different algorithms for trading systems Conceive of, and if with computer skills, program, algorithms for the automatic execution of trading strategies Differentiate between types of trading strategies Back-test algorithmic traders or test them in an experimental setting Opine in an informed way about the advantages and drawbacks of algorithmic trading.

Generic skills On successful completion of this subject, students should have improved the following generic skills: Oral communication Written communication Problem solving Thinking outside the box Team work Critical thinking Evaluation of data and other information Using algorithms for trading software.

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