0815_Python for Finance.pdf

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Python for Finance
Yves Hilpisch
Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo
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Preface
Not too long ago,
Python
as a programming language and platform technology was
considered exotic — if not completely irrelevant — in the financial industry. By contrast,
in 2014 there are many examples of large financial institutions — like Bank of America
Merrill Lynch with its Quartz project, or JP Morgan Chase with the Athena project — that
strategically use
Python
alongside other established technologies to build, enhance, and
maintain some of their core IT systems. There is also a multitude of larger and smaller
hedge funds that make heavy use of
Python
’s capabilities when it comes to efficient
financial application development and productive financial analytics efforts.
Similarly, many of today’s Master of Financial Engineering programs (or programs
awarding similar degrees) use
Python
as one of the core languages for teaching the
translation of quantitative finance theory into executable computer code. Educational
programs and trainings targeted to finance professionals are also increasingly
incorporating
Python
into their curricula. Some now teach it as the main implementation
language.
There are many reasons why
Python
has had such recent success and why it seems it will
continue to do so in the future. Among these reasons are its syntax, the ecosystem of
scientific and data analytics libraries available to developers using Python, its ease of
integration with almost any other technology, and its status as open source. (See Chapter 1
for a few more insights in this regard.)
For that reason, there is an abundance of good books available that teach
Python
from
different angles and with different focuses. This book is one of the first to introduce and
teach
Python
for finance — in particular, for quantitative finance and for financial
analytics. The approach is a practical one, in that implementation and illustration come
before theoretical details, and the big picture is generally more focused on than the most
arcane parameterization options of a certain class or function.
Most of this book has been written in the powerful, interactive, browser-based
IPython
Notebook
environment (explained in more detail in Chapter 2). This makes it possible to
provide the reader with executable, interactive versions of almost all examples used in this
book.
Those who want to immediately get started with a full-fledged, interactive financial
analytics environment for
Python
(and, for instance,
R
and
Julia
) should go to
http://oreilly.quant-platform.com and try out the Python Quant Platform (in combination
with the
IPython Notebook
files and code that come with this book). You should also
have a look at
DX
analytics, a
Python
-based financial analytics library. My other book,
Derivatives Analytics with Python (Wiley Finance), presents more details on the theory
and numerical methods for advanced derivatives analytics. It also provides a wealth of
readily usable
Python
code. Further material, and, in particular, slide decks and videos of
talks about
Python
for Quant Finance can be found on my private website.
If you want to get involved in
Python
for Quant Finance community events, there are
opportunities in the financial centers of the world. For example, I myself (co)organize
meetup groups with this focus in London (cf. http://www.meetup.com/Python-for-Quant-
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Finance-London/) and New York City (cf. http://www.meetup.com/Python-for-Quant-
Finance-NYC/). There are also For Python Quants conferences and workshops several
times a year (cf. http://forpythonquants.com and http://pythonquants.com).
I am really excited that
Python
has established itself as an important technology in the
financial industry. I am also sure that it will play an even more important role there in the
future, in fields like derivatives and risk analytics or high performance computing. My
hope is that this book will help professionals, researchers, and students alike make the
most of
Python
when facing the challenges of this fascinating field.
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Conventions Used in This Book
The following typographical conventions are used in this book:
Italic
Indicates new terms, URLs, and email addresses.
Constant width
Used for program listings, as well as within paragraphs to refer to software packages,
programming languages, file extensions, filenames, program elements such as
variable or function names, databases, data types, environment variables, statements,
and keywords.
Constant width italic
Shows text that should be replaced with user-supplied values or by values determined
by context.
TIP
This element signifies a tip or suggestion.
WARNING
This element indicates a warning or caution.
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