000			01854nam a22002177a 4500
005			20240206200307.0
008			240206b |||||||| |||| 00| 0 eng d
020			_a9781032014432
082			_a332.6 _bKEL
100			_aKelliher, Chris _913984
245			_aQuantitative finance with python: _ba practical guide to investment management, trading, and financial engineering
260			_bCRC Press _aBoca Raton _c2022
300			_axxxvii, 659 p.
365			_aGBP _b99.99
520			_aQuantitative 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. (https://www.routledge.com/Quantitative-Finance-with-Python-A-Practical-Guide-to-Investment-Management/Kelliher/p/book/9781032014432)
650			_aInvestments--Management _915171
650			_aTrading bands (Securities) _915172
650			_aFinancial engineering _915173
650			_aPython (Computer program language) _913655
942			_cBK _2ddc
999			_c5716 _d5716