Highly recommended.

Learning Modern C++ for Finance by Daniel Hanson tackles the challenge to bridge the gap between classic C++ programming and the demands of contemporary finance, all while leveraging the latest C++ language features. In ten well-organized chapters, the author covers a broad range of topics that equip readers to write robust, efficient C++ code for finance.

The structure of the book takes the reader from fundamental C++ concepts all the way to cutting-edge techniques, always grounded in finance-related scenarios. The progression feels natural – by the time new C++ features or complex models are introduced, the reader has the necessary context from previous chapters. The result is a cohesive learning path that serves both as a modern C++ tutorial and a quantitative finance primer.

One of the book’s greatest strength is how the author introduces C++ tips, tricks and best practices into every chapter. Hanson is a seasoned C++ developer and it shows. Throughout the text, readers will find insightful guidance on topics like managing memory with RAII and smart pointers, type inference , range-based loops, the <=> operator and leveraging move semantics.

While it teaches C++ thoroughly, this book never loses sight of its fintech audience. Hanson uses real-world financial examples as the driving force for learning, which adds both relevance and excitement for readers in quantitative finance. This applied approach means readers are not only learning C++ in theory, but also gaining a mini-education in quantitative finance. A chapter on Monte Carlo option pricing doesn’t just stop at explaining std::async and random distributions; it walks through simulating stock price paths, then demonstrates how to parallelize those simulations across threads for a huge speedup – a very practical concern for quantitative developers.

Another standout feature of Learning Modern C++ for Finance is the author’s decision to embrace widely-used libraries and explain how they can be applied. In particular, an entire chapter is devoted to the Boost libraries. Hanson introduces Boost’s capabilities in areas like statistical distributions, random number generation, and data structures (for example, multi-dimensional arrays) and shows how they can solve problems that would be tedious to implement from scratch.

In summary, Learning Modern C++ for Finance succeeds in its dual mission as a C++ modernization guide and a finance programming handbook. The few prerequisites (a minimal familiarity with C++ and some math finance background) are a small price for the wealth of knowledge delivered. Daniel Hanson has managed to create a resource that is at once a tutorial, reference, and inspirational guide for writing high-quality code in the finance domain.

For C++ developers in fintech, or anyone looking to bring their C++ skills up to date with modern best practices, this book is an invaluable asset.

Website: https://www.oreilly.com/library/view/learning-modern-c/9781098100797/

Code site: https://github.com/QuantDevHacks/LearningModCppFinance