Quantitative Finance With Case Studies In Python
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Quantitative Finance with Case Studies in Python
Quantitative Finance with Case Studies in 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. This new edition includes brand new material on data science and AI concepts, including large language models, as well as updated content to reflect the transition from Libor to SOFR to bring the text right up to date. It also includes expanded material on inflation, mortgage-backed securities and counterparty risk. In addition, there is an increased emphasis on trade ideas, as well as examples throughout based on recent market dynamics, including the post-Covid inflation shock. Overall, the new edition is designed to be even more of a practical tool than the first edition, and more firmly rooted in real-world data, applications, and examples. 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 concepts of Machine Learning and Artificial Intelligence - Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https: //github.com/lingyixu/Quant-Finance-With-Python-Code.[CK1]
Quantitative Finance with Python
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.
Python for Finance
Author: Yves J. Hilpisch
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-12-05
The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.