Process Engineering and Best Practices for Systematic Trading and Investment

280 Pages·2008·1.46 MB·English

Quality Money Management
Process Engineering and Best Practices for
Systematic Trading and Investment
The Financial Market Technology Series
Series Editor
Benjamin Van Vliet
The Financial Market Technology Series is a partnership between Elsevier, Inc. and the
Institute for Market Technology (i4mt) to publish cutting-edge books covering topics concerning
the integration of technology with financial markets, including:
● automated trading,
● building trading and investment systems,
● operational issues in back office processing,
● clearing and settlement, and
● compliance and governance issues as they relate to technology.
The goal of the series is to promote increased understanding and competency with tech-
nology in the finance industry through publishing high-quality books on the latest areas of
research and practice for professionals working in the financial markets.
Series Editor: Ben Van Vliet is a Lecturer at the Illinois Institute of Technology (IIT),
where he also serves as the Associate Director of the M.S. Financial Markets program. At
IIT he teaches courses in quantitative finance, C (cid:2) (cid:2) and .NET programming, and auto-
mated trading system design and development. He is vice chairman of the Institute for
Market Technology, where he chairs the advisory board for the Certified Trading System
Developer (CTSD) program. He also serves as series editor of the Financial Markets
Technology series for Elsevier/Academic Press and consults extensively in the financial
markets industry.
Mr. Van Vliet is also the author of “Modeling Financial Markets” with Robert Hendry
(2003, McGraw Hill) and “Building Automated Trading Systems” (2007, Academic Press).
Additionally, he has published several articles in the areas of finance and technology, and
presented his research at several academic and professional conferences.
We welcome proposals for books for the series. Please go to www.books.elsevier.com/
finance where you will find a link to send us your proposal.
Quality Money
Management
Process Engineering and Best Practices for
Systematic Trading and Investment

Andrew Kumiega
Benjamin Van Vliet
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Library of Congress Cataloging-in-Publication Data
Kumiega, Andrew.
Quality money management : best practices and process engineering for systematic
trading and investment / Andrew Kumiega, Benjamin Van Vliet.
p. cm.
Includes index.
ISBN-10: 0-12-372549-6 (hardback : acid-free paper)
ISBN-10: (invalid) 0-12-372549-3 (hardback : acid-free paper)
ISBN-13: 978-0-12-372549-3 (hardback : acid-free paper)
1. Electronic trading of securities. 2. Finance—Mathematical models.
3. Investments—Mathematical models. 4. Financial engineering. I. Vliet, Benjamin
Van. II. Title.
HG4515.95.K86 2008
332.6—dc22
2007052636
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
ISBN: 978-0-12-372549-3
For information on all Academic Press publications
visit our Web site at www.books.elsevier.com

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Printed and bound in the United States of America
08 09 10 10 9 8 7 6 5 4 3 2 1
Contents
Preface vii
1 Introduction 1
2 Key Concepts and Definitions of Terms 15
3 Overview of the Trading/Investment System
Development Methodology 29
4 Managing Design and Development 45
5 Types of Trading Systems 61
6 STAGE 0: The Money Document 67
STAGE I Design and Document Trading/Investment Strategy 79
7 STAGE 1: Overview 81
8 Describe Trading/Investment Idea 87
9 Research Quantitative Methods 97
10 Prototype in Modeling Software 105
11 Check Performance 115
12 Gate 1 123
S TAGE II Backtest 129
13 STAGE 2: Overview 131
14 Gather Historical Data 139
15 Develop Cleaning Algorithms 149
16 Perform In-Sample/Out-of-Sample Tests 157
v
vi Contents
17 Check Performance and Shadow Trade 165
18 Gate 2 169
S TAGE III Implement 175
1 9 STAGE 3: Overview 177
20 Plan and Document Technology Specifications 183
21 Design System Architecture 193
22 Build and Document the System 205
23 Check Performance and Probationary Trade 213
24 Gate 3 221
S TAGE IV Manage Portfolio and Risk 227
25 STAGE 4: Overview 229
26 Plan Performance and Risk Processes 237
27 Define Performance Controls 245
28 Perform SPC Analysis 251
29 Determine Causes of Variation 263
30 Kaizen: Continuous Improvement 271
Endnotes 279
Index 287
Preface
This book began several years ago as an attempt to provide a detailed road map for students
to follow from the theoretical quantitative finance taught in graduate school to building a
trading/investment system to manage real money in the real world. From our years of con-
sulting and experience in building funds and through academic research, discussions with
working students, and feedback from many colleagues, our perspective grew from writing
a classical money management book with detailed equations for calculating trading signals
and risk calculations to process engineering, statistical process control, Six Sigma, and soft-
ware design. The original shift back to industrial engineering was slow in the beginning, but
we kept going back to manufacturing examples to explain how to build trading/investment
systems. Every class, job, and consulting assignment led us away from the financial litera-
ture toward process engineering. Eventually, we both realized that the true missing theory
in finance was not another equation, but a concise methodology to implement theoretical
concepts.
At colleges and universities around the world and in most industry publications, the
mathematics of markets and trading is most often taught from a theoretical perspective
using clean sample data, in spite of the practical nature of the discipline as well as the
fact that most students are pursuing the knowledge for purely professional reasons. While
most academics prefer to teach students highly mathematical theory, working students
want to learn how to implement those theories in the real world and turn them into prof-
itable ideas and careers. While academics prefer to find and explain degrees of market
efficiency, working students hope one day to find and exploit inefficiencies. Students who
lack experience in the real-world financial markets study mathematics diligently only to
fail in interviews that ask for real-world knowledge. A gap exists between students of the
markets and the educational service providers.
Furthermore, students learn by doing. An ancient Chinese proverb states, “ I hear and I
forget, I see and I remember, I do and I understand.” No class or homework assignment is
sufficient for one to become a good trading/investment system architect. Due to time con-
straints, students will not build their own components but rather buy off-the-shelf ones.
The value is in learning how to test components and glue the pieces together to build a
complete system. Does the school attempt to organize its curriculum around a process
vii
viii Preface
for solving real-world problems, similar to engineering disciplines, or does it more or
less offer a smattering of courses in mathematics related to finance? Just as with product
teams, for students to implement financial theory in the real world, they need a step-by-
step process to follow. Other engineering disciplines have confronted this same problem
and chosen to design courses around building products using real-world machines and
real companies.
In this book, we present an overview of the body of knowledge of finance, linking
topics to create a linear progression of steps toward solutions to a business problem—how
to build trading and investment systems. We have not written in-depth chapters on areas
that are covered in detail in dozens of other books and hundreds of papers.
We had a difficult time drawing the line between adding in-depth quantitative finance
information and focusing solely on higher-level processes. Like graduate students doing
research, we often wandered off the path to smell the surroundings, only to force our-
selves back on the road to present a process map. For the interested reader who intends to
sniff the mathematical and technological flowers close up, we recommend the readings in
the end notes or searching the Internet for papers and books on topics of interest.
As one can imagine, additional credit for completion of this project must be given
to many friends, family, and colleagues, in particular to the many individuals who read
drafts of the chapters and provided invaluable feedback, including Bruce Rawlings,
Debbie Cernauskas, Fabian Valencia, Mulianto The, Dr. Zia Hassan, Assad Fehmy, Jason
Malkin, Batavia, Larissa J. Miller, Dr. Joe Wojkowski, Matt Lech, and Josip Roleta; and
all of our colleagues at the Illinois Institute of Technology’ s Stuart School of Business:
Russell Wojcik, Dr. Michael Gorham, Keith Black, Dr. Michael Ong, Dr. John Bilson,
Dr. Michael Kelly, and Jodi Houlihan. Also we would like to thank the many students at
IIT who have also provided valuable feedback. Certainly without their help and the help
of many others this book would never have been completed.
Andy Kumiega also thanks Megan, Kayla, Carrie, and Therese Kumiega for their
long-term support. He also thanks Dr. Miller, Dr. Rice, and Dr. Cesarone for their guid-
ance and support in graduate school. Ben Van Vliet thanks his wife Julia for putting up
with late nights and long weekends of research and writing.
W e hope you learn from our research the topic of process engineering for trading and
investment systems and are inspired to delve deeper into the topic. Please provide us with
any feedback you may have.
CHAPTER ◆ 1
Introduction
In the financial markets, the competition gets tougher every day. Today, the difference
between 25th and 75th percentile performance of money managers in some sectors of
the industry is measured in basis points. The only trading and money management firms
that survive now are the ones that continually discover cutting-edge position selection
strategies and technologies to build better quality trading and investment systems. Where
once traders and money managers based decisions on greed and hope, now rational self-
interest controls discovery processes that promote fact-based decisions and implement
proven research.
Entrepreneurs refer to the discovery process as knowledge-based innovation. In
finance it has begotten a new discipline: systematic trading and investment, the reasoned
study of financial markets using the scientific method to explain and replicate market phe-
nomena and the use of computer automation to make profitable trading/investment deci-
sions. Today, entrepreneurial activity in financial markets revolves around the practice of
systematic innovation1 to build new (and shut down old) trading and investment systems.
A top-rated hedge fund company stopped accepting new money in 2004 and started liquidating posi-
tions. In 2005 management shut down the fund completely. Their diligent and continuous research made
it clear that their models were no longer working. There was a fundamental shift in their ability to arbi-
trage convertible bonds and they told their investors to take their money back. Every investor is waiting
anxiously for them to open their next fund. (Compare that to Long Term Capital Management.) Ideally,
the hedge fund manager should have been building new strategies ahead of time so that they could have
kept the customers ’ money by switching funds.
T he key determinant of sustainable competitive advantage is the ability to continu-
ally discover, build, and operate better trading/investment systems. Which is to say, in
financial markets the new business model is quality: higher returns, lower risk, and lower
cost, all at a faster time-to-market. This model is complicated, though, by the tendency of
many skillful fund managers, traders, and financial engineers to fall short when it comes
to process engineering. 2 Other industries have faced this same problem.
In manufacturing plants, small margins demand reductions in waste and costs of
reworking defective parts. High quality is necessary to gain and maintain market share.
1