Quantitative finance with Python : a practical guide to investment management, trading, and financial engineering /
Kelliher, Chris,
Quantitative finance with Python : a practical guide to investment management, trading, and financial engineering / Chris Kelliher. - 1 Ed. - xxxvii, 659 pages. - Chapman & Hall/CRC Financial Mathematics series .
Includes bibliographical references and index.
"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 code available at www.routledge.com/ 9781032014432"--
9781032014432 9781032019147
2021056941
Investments--Management.
Trading bands (Securities)
Financial engineering.
Python (Computer program language)
HG4515.2. K445 2022
332.6
Quantitative finance with Python : a practical guide to investment management, trading, and financial engineering / Chris Kelliher. - 1 Ed. - xxxvii, 659 pages. - Chapman & Hall/CRC Financial Mathematics series .
Includes bibliographical references and index.
"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 code available at www.routledge.com/ 9781032014432"--
9781032014432 9781032019147
2021056941
Investments--Management.
Trading bands (Securities)
Financial engineering.
Python (Computer program language)
HG4515.2. K445 2022
332.6