Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors.

Features

• Useful as both a teaching resource and as a practical tool for professional investors.
• Ideal textbook for first year graduate students in quantitative finance programs, such as those in master's programs in Mathematical Finance, Quant Finance or Financial Engineering.
• Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning.
• Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https://github.com/lingyixu/Quant-Finance-With-Python-Code.

About the Author

Chris Kelliher is a Senior Quantitative Researcher in the Global Asset Allocation group at Fidelity Investments. In addition, Mr. Kelliher is a Lecturer in the Masters in Mathematical Finance and Financial Technology program at Boston University's Questrom School of Business. In this role he teaches multiple graduate level courses including Computational Methods in Finance, Fixed Income & Programming for Quant Finance. Prior to joining Fidelity in 2019, Mr. Kelliher served as a portfolio manager for RDC Capital Partners. Before joining RDC, Mr. Kelliher served as a principal and quantitative portfolio manager at a leading quantitative investment management firm, FDO Partners. Prior to FDO, Mr. Kelliher was a senior quantitative portfolio analyst and trader at Convexity Capital Management and a senior quantitative researcher at Bracebridge Capital. He has been in the financial industry since 2004. Mr. Kelliher earned a BA in Economics from Gordon College, where he graduated Cum Laude with Departmental Honours, and an MS in Mathematical Finance from New York University's Courant Institute.

Reviews

"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

Book Information
ISBN 9781032014432
Author Chris Kelliher
Format Hardback
Page Count 659
Imprint Chapman & Hall/CRC
Publisher Taylor & Francis Ltd
Weight(grams) 1680g