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Python for analyze big financial data.pdf
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Python for analyze big financial data.pdf
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PYTHONFINANCE
Python for Finance
ISBN: 978-1-491-94528-5
US $44.99 CAN $47.99
“
Python's readable
syntax, easy integration
with C/C++, and the
wide variety of numerical
computing tools make
it a natural choice for
financial analytics.
It's rapidly becoming
the de-facto replacement
for a patchwork of
languages and tools
at leading financial
institutions.
”
—Kirat Singh
cofounder, President and CTO
Washington Square Technologies
Twitter: @oreillymedia
facebook.com/oreilly
The financial industry has adopted Python at a tremendous rate, with
some of the largest investment banks and hedge funds using it to build
core trading and risk management systems. This hands-on guide helps
both developers and quantitative analysts get started with Python, and
guides you through the most important aspects of using Python for
quantitative finance.
Using practical examples throughout the book, author Yves Hilpisch also
shows you how to develop a full-fledged framework for Monte Carlo
simulation-based derivatives and risk analytics, based on a large, realistic
case study. Much of the book uses interactive IPython Notebooks, with
topics that include:
■ Fundamentals: Python data structures, NumPy array handling,
time series analysis with pandas, visualization with matplotlib,
high performance I/O operations with PyTables, date/time
information handling, and selected best practices
■ Financial topics: Mathematical techniques with NumPy, SciPy,
and SymPy, such as regression and optimization; stochastics
for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-
Risk calculations; statistics for normality tests, mean-variance
portfolio optimization, principal component analysis (PCA),
and Bayesian regression
■ Special topics: Performance Python for nancial algorithms,
such as vectorization and parallelization, integrating Python
with Excel, and building nancial applications based on Web
technologies
Yves Hilpisch is the founder and managing partner of The Python Quants, an
analytics software provider and financial engineering group. Yves also lectures on
mathematical finance and organizes meetups and conferences about Python for
Quant Finance in New York and London.
Yves Hilpisch
Python
for Finance
ANALYZE BIG FINANCIAL DATA
Python for Finance
Hilpisch
Yves Hilpisch
Python for Finance
Python for Finance
by Yves Hilpisch
Copyright © 2015 Yves Hilpisch. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/
institutional sales department: 800-998-9938 or corporate@oreilly.com.
Editors: Brian MacDonald and Meghan Blanchette
Production Editor: Matthew Hacker
Copyeditor: Charles Roumeliotis
Proofreader: Rachel Head
Indexer: Judith McConville
Cover Designer: Ellie Volckhausen
Interior Designer: David Futato
Illustrator: Rebecca Demarest
December 2014:
First Edition
Revision History for the First Edition:
2014-12-09: First release
See http://oreilly.com/catalog/errata.csp?isbn=9781491945285 for release details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Python for Finance, the cover image of a
Hispaniolan solenodon, and related trade dress are trademarks of O’Reilly Media, Inc.
Many of the designations used by manufacturers and sellers to distinguish their products are claimed as
trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a trademark
claim, the designations have been printed in caps or initial caps.
While the publisher and the author have used good faith efforts to ensure that the information and instruc‐
tions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors
or omissions, including without limitation responsibility for damages resulting from the use of or reliance
on this work. Use of the information and instructions contained in this work is at your own risk. If any code
samples or other technology this work contains or describes is subject to open source licenses or the intel‐
lectual property rights of others, it is your responsibility to ensure that your use thereof complies with such
licenses and/or rights.
This book is not intended as financial advice. Please consult a qualified professional if you require financial
advice.
ISBN: 978-1-491-94528-5
[LSI]
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Part I. Python and Finance
1.
Why Python for Finance?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
What Is Python? 3
Brief History of Python 5
The Python Ecosystem 6
Python User Spectrum 7
The Scientific Stack 8
Technology in Finance 9
Technology Spending 10
Technology as Enabler 10
Technology and Talent as Barriers to Entry 10
Ever-Increasing Speeds, Frequencies, Data Volumes 11
The Rise of Real-Time Analytics 12
Python for Finance 13
Finance and Python Syntax 14
Efficiency and Productivity Through Python 17
From Prototyping to Production 21
Conclusions 22
Further Reading 23
2.
Infrastructure and Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Python Deployment 26
Anaconda 26
Python Quant Platform 32
Tools 34
Python 34
iii
IPython 35
Spyder 45
Conclusions 47
Further Reading 48
3. Introductory Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Implied Volatilities 50
Monte Carlo Simulation 59
Pure Python 61
Vectorization with NumPy 63
Full Vectorization with Log Euler Scheme 65
Graphical Analysis 67
Technical Analysis 68
Conclusions 74
Further Reading 75
Part II. Financial Analytics and Development
4.
Data Types and Structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Basic Data Types 80
Integers 80
Floats 81
Strings 84
Basic Data Structures 86
Tuples 87
Lists 88
Excursion: Control Structures 89
Excursion: Functional Programming 91
Dicts 92
Sets 94
NumPy Data Structures 95
Arrays with Python Lists 96
Regular NumPy Arrays 97
Structured Arrays 101
Vectorization of Code 102
Basic Vectorization 102
Memory Layout 105
Conclusions 106
Further Reading 107
iv | Table of Contents
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