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- Author: Cris Doloc
This book aims to introduce readers to the field of computational finance and provide an in-depth exploration of the application of computational intelligence in data-driven trading. The book is divided into two parts: the first half systematically introduces the two modern topics of data-driven paradigm and computational intelligence to build a solid theoretical foundation for readers. The second half elaborates on the specific application of computational intelligence in quantitative trading practice through a series of case studies, covering many aspects such as transaction execution optimization, price dynamic prediction, portfolio management, market making, derivatives valuation, risk management and compliance. The book discusses the challenges and opportunities of machine learning in the financial field, and introduces various mathematical techniques such as support vector machines, random forest classifiers, reinforcement learning, deep neural networks, long short-term memory networks, and convolutional neural networks, and their applications in financial problems. This book aims to provide engineering and scientific clarity on the use of "artificial intelligence" in the financial industry and convey confidence in the possibilities offered by the new era of data-intensive computing.
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