Designing Stock Market Trading Systems: With and Without Soft Computing

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9781906659585: Designing Stock Market Trading Systems: With and Without Soft Computing
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In Designing Stock Market Trading Systems Bruce Vanstone and Tobias Hahn guide you through their tried and tested methodology for building rule-based stock market trading systems using both fundamental and technical data. This book shows the steps required to design and test a trading system until a trading edge is found, how to use artificial neural networks and soft computing to discover an edge and exploit it fully. Learn how to build trading systems with greater insight and dependability than ever before Most trading systems today fail to incorporate data from existing research into their operation. This is where Vanstone and Hahn's methodology is unique. Designed to integrate the best of past research on the workings of financial markets into the building of new trading systems, this synthesis helps produce stock market trading systems with unrivalled depth and accuracy. This book therefore includes a detailed review of key academic research, showing how to test existing research, how to take advantage of it by developing it into a rule-based trading system, and how to improve it with artificial intelligence techniques. The ideas and methods described in this book have been tried and tested in the heat of the market. They have been used by hedge funds to build their trading systems. Now you can use them too.

Über den Autor:

Dr. Bruce Vanstone is an Assistant Professor at Bond University in Australia. He completed his PhD in Computational Finance in 2006. He is a regular presenter and publisher of academic work on stock market trading systems at an international level. He teaches stock market trading courses at university, and is a consultant for a boutique hedge fund in Australia. More information on Bruce's research and methods can be found at Tobias Hahn is currently studying towards a PhD at Bond University in Australia. His research focuses on market microstructure and, in particular, the application of machine learning techniques to the pricing of derivative products.

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