Quantitative Finance with Python

"""This ambitious book is a practical guide for aspirant quants, on both the buyside and the sellside. [. . .] There is a further 175-page code and exercises supplement to ‘provide a coding baseline’. [. . .] The subject matter is neatly partitioned into 21 Chapters, starting with an overview of the quant landscape and ending with basic machine learning in finance. The flow between chapters makes the book a pleasure to read. One can also easily access a topic of particular interest. [. . .] The author is both a lecturer and practitioner in the field. This is evident from the accessible style of writing, comprehensive examples and the way the topics are built up. The content is generally well balanced between theory and practice. There is a broad range of finance topics covered. From swaption and currency triangles to CDO mechanics to feature explainability in machine learning, few books in this space are as comprehensive. [. . .] Readers will find a good selection of case studies throughout the book. The author’s experience as a practitioner allows him to write with conviction. The commentary is accessible and free of jargon. These case studies, such as the 2018 natural gas options squeeze and the 2021 Reddit meme Gamestop squeeze, are useful cautionary tales for those new to the field. [. . .] Finance students in their final years' study and those starting careers as quants will find the book a useful resource. It might be considered as an equally comprehensive but more practical complement to Hull’s classic ‘Options, Futures, and Other Derivatives’."" - Mark Greenwood, Quantitative Finance"