Learning Modern C++ for Finance

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, thanks to modern features added to the C++ Standard beginning in 2011.

Financial programmers will discover how to leverage C++ abstractions that enable safe 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 also benefit from this handy guide.

• Learn C++ basics from a modern perspective: 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
• Implement basic numerical routines in modern C++
• Understand best practices for writing clean and efficient code

The modern features that have been added to the C++ Standard beginning with C++11 in 2011 have been truly remarkable and transformative. Consider the following additions through 2017, which can be of immediate benefit to financial C++ developers, and in most cases are very easy to incorporate into code:

• Move semantics, allowing transfer of object ownership without the performance overhead of copying (C++11)
• Smart pointers, which dramatically reduce problems associated with raw pointers (C++11 and C++14)
• Parallel standard algorithms that require only an additional parameter yet can speed up code by magnitudes (C++17)
• Random number generation from common distributions (C++11)
• Task-based concurrency, which can run tasks in parallel by simply replacing a couple of lines of code (C++11)

Updated versions of the C++ Standard have been released every three years since C++11, leading to the subsequent standard C++20 and the most recent C++23. In particular, these more recent features are also now available:

• A new date class, critical for fixed income applications (C++20)
• Concepts, making template-based generic programs simpler to debug and more expressive (C++20)
• Range adaptors, enabling the composition of algorithms in a modern functional form, while cutting out “the middleman” of inefficient object copying (C++20 and C++23)
• Modules—​say goodbye to #include and hello to safer code (C++20)

All the modern features mentioned here, plus coverage of popular open source C++ libraries such as Eigen (linear algebra) and the Boost libraries, are provided within this book, with a focus on relevant financial applications.

Table of Contents

Chapter 1. An Overview of C++

Chapter 2. Writing User-Defined Classes with

Modern C++ Features

Chapter 3. Inheritance, Polymorphism, and Smart Pointers

Chapter 4. The Standard Template Library Part I: Containers and Iterators

Chapter 5. The Standard Template Library Part II: Algorithms and Ranges

Chapter 6. Random Number Generation and Concurrency

Chapter 7. Dates and Fixed Income Securities

Chapter 8. Linear Algebra

Chapter 9. The Boost Libraries

Chapter 10. Modules and Concepts

Appendix A. Virtual Default Destructor

Appendix B. Object Slicing

Appendix C. Implementation of Move

Special Member Functions

Appendix D. Resolving Conflicts in the Initialization of a vector

Appendix E. valarray and Matrix Operations

About the Author

Daniel Hanson spent over 20 years in quantitative development in finance, primarily with C++ implementation of option pricing and portfolio risk models, trading systems, and library development. He now holds a full-time lecturer position in the Department of Applied Mathematics at the University of Washington, teaching quantitative development courses in the Computational Finance & Risk Management (CFRM) undergraduate and graduate programs. Among the classes he teaches is graduate-level sequence in C++ for quantitative finance, ranging from an introductory level through advanced. He also mentors Google Summer of Code student projects involving mathematical model implementations in C++ and R.