Table Of ContentLearning Modern C++ for
Finance
Foundations for Quantitative Programming
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Daniel Hanson
Learning Modern C++ for Finance
by Daniel Hanson
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978-1-098-10080-3
Chapter 1. An Overview of C++
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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.
Most of the content here is likely familiar for most readers, but the discussion
here attempts to extend some of the basics with points about quantitative
programming and best practices that often are not included in introductory
books. We will also have our first look at C++20, namely mathematical
constants that have been added to the C++ Standard Library.
By the end of the chapter, you should be able to write, compile, and run
simple C++ programs, understand basic numerical types, and employ
mathematical functions in the Standard Library that are fundamental in just
about any quantitative discipline, including finance.
C++ and Quantitative Finance
C++ started its rapid growth in the financial sector around the mid-1990’s.
Many of us who were in the industry around this time had been raised on
FORTRAN, particularly for writing numerical routines and scientific
applications. While FORTRAN and its supporting libraries were very well-
developed in terms of mathematical and linear algebra support, it lacked
support for object-oriented programming.
Financial modeling in the abstract is naturally comprised of different
components that interact with each other. For example, to price even a simple
derivative contract based on foreign exchange and interest rates, one would
typically require the following:
The term structure of interest rates for each currency
A market rate feed of live foreign exchange rate quotes
Volatility curves or surfaces for movements in FX rates and interest
rates
A set of pricing methods, eg closed form, simulation, or other numerical
approximations
Each of these components can be represented by an object, and C++ provided
the means for creating these objects and managing their relationships to each
other.
Banks and other financial institutions also needed a way to calculate risk
measures at both a regional and global scale. This was a particular challenge
for companies with trading operations spread across the major financial
centers of New York, London, and Tokyo, as well as other capital markets.
At the start of each trading day, risk reporting was required for a firm’s
headquarters in, say, New York that took into account the portfolios
maintained both locally and around the world. This could be a
computationally intensive task, but the performance of C++ made it possible
and was yet another significant factor in its early adoption in the financial
industry.
Around the turn of the century, newer object-oriented languages, such as Java
and C#, made software development a relatively simpler and faster process,
while more efficient processors became less expensive. However, the same
features in these languages that enabled quicker deployment, such as built-in
managed memory and intermediate compilation, could also introduce
overhead in terms of run-time performance. Management decisions on which
language to adopt often came down to a trade-off between more rapid
development and run-time efficiency. Even if one of these language
alternatives was employed, computationally intensive pricing models and risk
calculations were -- and still are -- often delegated to existing C++ libraries
and called via an interface. It should also be noted that C++ also offers
certain compile-time optimizations that are not available in these other
programming languages.
C++ 11: The Modern Era is Born
In 2011, the Standard C++ Foundation released a substantial revision that
addressed long-needed modernization and in particular provided some very
welcome abstractions that are immediately useful to quantitative developers.
These include:
Random number generation from a variety of probability distributions
Lambda expressions that encapsulate mathematical functions that can
also be passed as arguments
Task-based concurrency that can parallelize computations without the
need for manual thread management
Smart pointers that prevent memory-related program crashes, without
affecting performance
These topics and more will be discussed in the chapters ahead. An excellent
reference that covers the history and evolution of C++ into the modern era is
also available from O’Reilly: C++ Today: The Beast is Back, by Jon Kalb
and Gasper Azman [1]. It should also be noted that with more attention to,
and promotion of best practices[1] and guidelines[2] by the ISO C++
committee, cross-platform development is now a much easier task than in
years past.
And following C++11, new releases with more and more modern features
addressing the demands of financial and data science industries are being
rolled out on a threeyear cadence, with the most recent release being C++20.
This book will primarily cover developments through C++20, particularly
those that should be of interest to financial quant developers. Proposals
currently in the works for future standards are also mentioned where relevant.
Proprietary and high-frequency trading firms have been at the forefront of
adopting the C++11 Standard and later, where the speed of acting on market
and trading book signals in statistical strategies can mean a profound
difference in profit and loss. Modern C++ is also in keen demand for
derivatives pricing models utilized by traders and risk managers at
investment banks and hedge funds. The recent random number generation
and concurrency features in the Standard Library, for example, provide built-
in support for efficient Monte Carlo simulation that is a key component in
both evaluating trading strategies and pricing complex exotic options. These
tasks used to require many more hours of distributional random number
generation code development and time-consuming integration of platform-
dependent threading libraries.
Open Source Mathematical Libraries
Another very welcome development over the past decade has been the
proliferation of robust open-source mathematical libraries written in standard
C++ that therefore do not require the time-consuming C-language interface
gymnastics of the past. Primary among these are the Boost libraries, the
Eigen and Armadillo matrix algebra libraries, and machine learning libraries
such as TensorFlow and PyTorch. We will cover Boost and Eigen in more
detail later in the book.
Debunking Myths About C++
There are a multitude of myths about C++. Here are several of the more
infamous beliefs, and explanations which debunk them.
Knowledge of C is necessary for learning C++: While the C++ Standard
retains most of the C language, it is entirely possible to learn C++
without knowledge of C, as we shall see. Clinging to C style can in fact
hinder learning the powerful abstractions and potential benefits of C++.
C++ is too difficult: There is no doubt that C++ is a rich language that
provides plenty of the proverbial rope with which one can hang oneself,
but by leveraging _modern_ features of the language while holding
legacy issues in abeyance at the outset, it is entirely possible to become
very productive as a quantitative developer in C++ very quickly.
Memory leaks are always a problem in C++: With smart pointers
available since C++11, this no longer needs to be an issue in most
financial model implementations, as we shall see.
Compiled vs Interpreted Code
As alluded to above, C++ is a compiled language, where commands typed
into a file by us mere mortals are translated into binary instructions, or
machine code, that a computer processor will understand. This is in contrast
to non-typed and interpreted quantitative languages such as Python, R, and
Matlab, where each line of code must be individually translated to machine
code at run-time, thus slowing down execution time for larger applications.
This is by no means a knock on these languages, as their power is evident in
their popularity for rapid implementations of models arising in quantitative
fields such as finance, data science, and biosciences, with their built-in
mathematical and statistical functions are often compiled in C, C++, or
FORTRAN. However, the financial world at least is replete with stories
where a model would require days to run in an interpreted language, where
run times could be reduced to a matter of minutes when reimplemented in
C++.
An effective approach is to use interpreted mathematical languages with C++
in a complementary fashion. For example, when computationally intensive
models code is written in a C++ library, and then called either interactively or
from an application in R, for example, C++ efficiently takes care of the
number crunching. The results can then be used inside powerful plotting and
other visualization tools in R that are not available in C++.
Another advantage is that the models code is written once and maintained in
a C++ library that can be deployed across many different departments,
divisions, and even international boundaries, and called via interfaces from
applications in written in different front-end languages, while ensuring
consistent numerical results throughout the organization. This can be
particularly advantageous for regulatory compliance purposes.
Popular open-source C++ packages are available for both R and Python,
namely Rcpp and pybind11, respectively. Matlab also provides options for
C++ interfaces.
The Components of C++
Standard C++ releases, at a high level, consist of two components: language
features, and the C++ Standard Library. A software library is essentially a set
of functions and classes that are not executable on their own but that are
called by an application or system. Library development -- both open source
and commercial -- now dominates modern C++ development compared to
standalone applications that were popular in previous decades, and we will
discuss some of those later that are useful for computational work. The most
important C++ library is the Standard Library that is shipped with modern
compilers.
