Название: Learning Modern C++ for Finance: Foundations for Quantitative Programming (Third Early Release)Автор: Daniel Hanson
Издательство: O’Reilly Media, Inc.
Год: 2022-11-29
Страниц: 146
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.2 MB
A lot of financial modeling has gravitated toward Python, R, and VBA, but many developers hit a wall with these languages when it comes to performance. This practical book demonstrates why C++ is still one of the dominant production-quality languages for financial applications and systems. Many programmers believe that C++ is too difficult to learn. Author Daniel Hanson demonstrates that this is no longer the case.
Financial programmers coming from Python or another interpreted language will discover how to leverage C++ abstractions that enable safer and quicker implementation of financial models. You'll also explore how popular open source libraries provide additional weapons for attacking mathematical problems. C++ programmers unfamiliar with financial applications will also benefit from this handy guide.
Before launching into programming in C++, it will be useful to present a brief overview of the language the C++ Standard Library, and the ways in which C++ continues to have a major presence in quantitative finance. You may have already felt intimidated by opinions and rumors claiming that C++ is extraordinarily difficult to learn and fraught with minefields. So, in this chapter, we will try to allay these fears by first debunking some of the common myths about C++, and then presenting straightforward examples to help you get up and running.
Learn C++ basics: syntax, inheritance, polymorphism, composition, STL containers, and algorithms
Dive into newer features and abstractions including functional programming using lambdas, task-based concurrency, and smart pointers
Employ common but nontrivial financial models in modern C++
Explore external open source math libraries, particularly Eigen and Boost
Implement basic numerical routines in modern C++
Understand best practices for writing clean and efficient code
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