Automation of Trading Machine for Traders: How to Develop Trading Models - Hardcover

Chan, Jacinta

 
9789811399442: Automation of Trading Machine for Traders: How to Develop Trading Models

Inhaltsangabe


This Palgrave Pivot innovatively combines new methods and approaches to building dynamic trading systems to forecast future price direction in today’s increasingly difficult and volatile financial markets. The primary purpose of this book is to provide a structured course for building robust algorithmic trading models that forecast future price direction.

Chan provides insider information and insights on trading strategies; her knowledge and experience has been gained over two decades as a trader in foreign exchange, stock and derivatives markets. She guides the reader to build, evaluate, and test the predictive ability and the profitability of abnormal returns of new hybrid forecasting models.

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Über die Autorin bzw. den Autor

Jacinta Chan, PhD in Financial Statistics, is with the Faculty of Business and Accountancy at the University of Malaya, Malaysia. She shares her knowledge and skills gained over two decades as a forex dealer, equity research analyst, product development officer of the futures exchange and senior Vice President of the sales and dealing derivatives desk of a financial institution with her students in universities and trading courses all over the world.


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This Palgrave Pivot innovatively combines new methods and approaches to building dynamic trading systems to forecast future price direction in today’s increasingly difficult and volatile financial markets. The primary purpose of this book is to provide a structured course for building robust algorithmic trading models that forecast future price direction.

Chan provides insider information and insights on trading strategies; her knowledge and experience has been gained over two decades as a trader in foreign exchange, stock and derivatives markets. She guides the reader to build, evaluate, and test the predictive ability and the profitability of abnormal returns of new hybrid forecasting models.

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