Quantitative Finance with Python: A Practical Guide to Investment Management, Trading, and Financial Engineering (Chapman and Hall/CRC Financial Mathematics Series)
by: Chris Kelliher (Author)
Publisher: Chapman and Hall/CRC
Edition: 1st
Publication Date: 2022/5/20
Language: English
Print Length: 659 pages
ISBN-10: 1032014431
ISBN-13: 9781032014432
Book Description
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. FeaturesUseful 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
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. FeaturesUseful 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.
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