Building Reliable Trading Systems: Tradable Strategies That Perform as They Backtest and Meet Your Risk-Reward Goals

290 Pages·2013·3.27 MB·English

by  Keith Fitschen(auth.)

BUILDING RELIABLE
TRADING SYSTEMS

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BUILDING
RELIABLE
TRADING SYSTEMS

Tradable Strategies That Perform as
They Backtest and Meet Your
Risk-Reward Goals

Keith Fitschen

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Cover Illustration: © Equinox Imagery/Alamy

Copyright © 2013 by Keith Fitschen. All rights reserved.

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Library of Congress Cataloging-in-Publication Data:

ISBN 978-1-118-52874-7 (Hardcover); ISBN 978-1-118-63561-2 (ebk);
ISBN 978-1-118-63581-0 (ebk); ISBN 978-1-118-67277-8 (ebk)

Printed in the United States of America
10 9 8 7 6 5 4 3 2 1

v

C O N T E N T S

Preface
vii

CHAPTER 1 What Is a “Tradeable Strategy?”
1

CHAPTER 2 Developing a Strategy So it Trades Like it
Back-Tests
7

CHAPTER 3 Find the Path of Least Resistance in the
Market You Want to Trade
31

CHAPTER 4 Trading System Elements: Entries
45

CHAPTER 5 Trading System Elements: Exits
65

CHAPTER 6 Trading System Elements: Filters
89

CHAPTER 7 Why You Should Include Money Management
Feedback in Your System Development
107

CHAPTER 8 Bar-Scoring: A New Trading Approach
119

CHAPTER 9 Avoid Being Swayed by the “Well-Chosen
Example”
133

CHAPTER 10 Trading Lore
153

CHAPTER 11 Introduction to Money Management
175

CHAPTER 12 Traditional Money Management Techniques
for Small Accounts: Commodities
191

vi

CONTENTS

CHAPTER 13 Traditional Money Management Techniques
for Small Accounts: Stock Strategy
215

CHAPTER 14 Traditional Money Management Techniques
for Large Accounts: Commodities
221

CHAPTER 15 Traditional Money Management Techniques
for Large Accounts: Stocks
233

CHAPTER 16 Trading the Stock and Commodity Strategies
Together
241

APPENDIX A Understanding the Formulas
245

APPENDIX B Understanding Futures
251

APPENDIX C Understanding Continuous Contracts
265

APPENDIX D More Curve-Fitting Examples
273

Index
277

vii

P R E FA C E

T
his book shows you how to build a tradeable strategy. A tradeable strat-
egy is one that fits your own risk/reward goals, and one that trades in
real‐time as well as it performs in the development back‐test. It is not easy
to develop a tradeable strategy because there are serious pitfalls. Most of us
are greedy and want to trade something that has a large return. We ratio-
nalize that we can accept a lot of risk in the form of draw‐down to achieve
the large return, but in reality few traders can trade through a 20 percent
draw‐down before abandoning the strategy. To help you form realistic risk/
reward goals, the first chapter shows the real‐time performance of the best
traders in the world over a recent five‐year period. Based on that chapter, I
urge you to write down the system characteristics that would be tradeable
for you. Address risk first in the form of max draw‐down, average annual
max draw‐down, and longest time between equity highs. So the first pitfall
is greed. It manifests itself in every strategy development step. You will be
inclined to accept a system rule that increases profitability, even though it
increases risk at a higher rate. But an even greater pitfall is the danger of
curve‐fitting.
Curve‐fitting occurs when you develop a strategy with too few trades in
the development sample. Curve‐fit systems impact the second characteristic
of a tradeable system: Curve‐fit systems don’t trade in real‐time as well as
they back‐test. It is unfortunate that the system development packages you
can buy are almost universally tied to the one‐chart paradigm; you place
many bars of data from a single tradeable on a chart and develop a strategy
to trade that instrument. Even if your developed strategy yields hundreds of

viii

PREFACE

trades, it probably isn’t enough to be anything other than heavily curve‐fit.
Chapter 2 covers curve‐fitting extensively and shows how you can generate
enough trades to minimize curve‐fitting. It uses examples and statistics, and
details an easy‐to‐perform process that you can use to determine the degree
of curve‐fitting in your development work. Most strategies fail or under‐
perform because they are overly curve‐fit. If you want to succeed, you need
to fully understand how to minimize curve‐fitting.
Most of the remainder of the book is spent developing two tradeable
systems: one a short‐term scalping system for stocks, and the other a
mid‐term trend‐following strategy for commodities. As these systems
are developed, entries, exits, and trading filters are detailed. By the end
of the development process, both are “tradeable” as is, but how to tai-
lor them to a range of risk/reward profiles is covered in, five chapters
on money management. I make a differentiation between small-account
traders and large-account traders. Small-account traders are always a
handful of adverse trades away from a margin call. They cannot take
advantage of sizing techniques that optimize equity growth because the
risk of more than a one‐lot might be 10 or even 20 percent of their ac-
count size. Using small‐account trading techniques, money management
rules for both the stock and commodity systems are detailed in two
chapters. Similarly, the systems are adapted for the large‐account trader
in two chapters. Lastly, both systems are combined. Table P.1 illustrates
the range of tradeable solutions available.

TABLE P.1
Tradeable Solutions with Combined Trading of the Stock and
Commodity Strategies

Average Annual
Return (percent)
Average Annual Max
Draw‐Down (percent)
Max Draw‐Down
(percent)

23.4
5.6
8.7

25.9
6.1
9.6

28.6
6.7
10.4

31.2
7.2
11.3

33.9
7.8
12.1

36.6
8.3
13.0

There is material in the book that I’ve never seen elsewhere. Most nota-
bly, bar‐scoring is detailed in Chapter 8. It is an exciting new way to charac-
terize the profit potential of each bar with user‐defined criteria.

ix

PREFACE

Lastly, Chapter 9 and Chapter 10 are devoted to an analysis of some of the
trading claims commonly made in the literature, and to trading maxims that
may or may not have a basis.
The TradeStation Easy Language code and daily signals for the systems
developed in this book are available on a companion web site. See the
instructions at the back of the book for more information.

1
T
he purpose of this book is to show the reader how to develop a trade-
able strategy. But before the how-to part, you need to realistically un-
derstand what a tradeable strategy must encompass. I say realistically be-
cause some traders envision a tradeable strategy as one that never loses a
trade, never has a losing day, and at least doubles your money each year.
It’s nice to have lofty goals, but you’ll never find a strategy that meets those
criteria. To show the art of the possible, the first part of this chapter will
present documented performance from some of the best traders over the
last five years. Then we’ll look at metrics that best characterize a trading
strategy’s performance. Lastly, a set of questions will be presented to help
you define what would constitute a tradeable strategy according to your
risk‐taking tolerance.

■ Realistic Return/Risk Expectations

Table 1.1 shows Barclay’s top 20 Commodity Trading Advisors (CTAs) for
the five‐year period of July 1, 2005, through June 30, 2010. It was compiled
by Barclay’s from the 290 CTAs that submit performance information to
them.

C H A P T E R 1

What Is a
Tradeable
Strategy?

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2

WHAT IS A TRADEABLE STRATEGY?

Table 1.1 raises a number of interesting points:

■ Across all 20 funds, the average annual yearly performance of 27.98 percent
is only about 3.5 percent higher than the average of the max draw‐downs.

■ Out of the 20 funds, 17 had an entire one‐year period without any gain.

■ The best 12‐month period heavily skewed the average five‐year return per-
formance numbers. If you compound a starting amount by 27.98 percent
per year (the average annual return across the 20 entities) for five years,

TABLE 1.1
Top 20 CTA Performance for 7/1/2005 to 6/30/2010

Advisor

5‐Yr. Comp.
Annual
Return (%)

Largest
Draw‐
Down (%)

Percent
Winning
Months

Best 12
Month
Period (%)

Worst 12
Month
Period (%)

Vegasoul Capital
Management
44.54
6.88
80.0
112
1

Quantitative Invest. Mgmt.
37.01
29.49
68.33
126
–22

Pere Trading Group
36.44
60.72
61.66
570
–50

DiTomasso Group
33.73
26.13
68.33
76
–26

Commodity Future
Services
32.04
27.84
61.66
135
–22

Two Sigma
31.56
18.17
66.66
75
0

24FX Management Ltd.
29.41
19.28
80.0
55
2

Scully Capital Mgmt.
28.44
21.26
58.33
79
–6

Dighton
27.41
44.32
61.66
226
–44

Belvedere Advisors
27.35
14.55
66.66
86
7

Financial Comm. Inv.
26.99
34.63
76.66
112
–23

Red Oak Comm. Advisors
25.03
21.91
68.33
86
–7

Altis GFP Master Fund
24.45
22.73
56.66
70
–8

Heyden & Steindl
23.69
17.76
51.66
82
–8

Aisling Analytics
23.13
26.05
65.0
66
–20

Tactical Invest. Mgmt.
22.64
22.23
51.66
52
–16

Quicksilver Trading Inc.
22.04
24.58
63.33
73
–20

Blenheim Capital Mgmt.
21.53
25.63
63.33
84
–22

Paskewitz Asset Mgmt.
21.05
12.18
68.33
64
–7

MIGFX Inc.
21.02
12.86
63.33
67
–11

Average
27.98
24.46
65.08
125.3
–15.1

3

WHAT IS A TRADEABLE STRATEGY?

the ending equity is about 243 percent higher than the starting amount.
The average best 12‐month average return of 125.3 percent is over half
that amount.

■ In general, the funds with the higher fi ve‐year return took more risk, as re-
fl ected by the draw‐down numbers. This can be seen by plotting the average
return and max draw‐down points on a graph and fi tting a regression line to
the return and draw‐down points. Figure 1.1 shows the graph.

FIGURE 1.1 Return versus Risk for the Top 20 CTAs

0

10

20

30

40

50

60

70

1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20

Percent Return/Draw-Down

CTA

Return
Regress

DD Regress
Return
Max DD

These performance numbers probably introduce a reality shock into
those looking to develop their own tradeable strategy. Real trading involves
these tenets:

■ You are not going to make 100 percent each year. The number-one CTA
in the list averaged 44.54 percent for 5 years, but even then it had a
12‐month period that netted only 1 percent.

■ If you shoot for a relatively high return, you will have a relatively high
draw‐down at some point. If you shoot for a relatively lower return, you
should be able to see relatively less draw‐down.

■ If you are fortunate enough to have a period of exceptional returns,
remember that those gains may have to carry your performance for
years.

■ You will go extended periods of time without any gains.

4

WHAT IS A TRADEABLE STRATEGY?

■ Metrics to Use in Gauging Tradeable
System Performance

Based on the performance of some of the best money managers in the world,
there are a minimum of three areas that need to be addressed in defining a
suitable tradeable system:

1. Some measure of normal return and normal draw‐down. These are
the risk/reward benchmarks you’ll be experiencing most of the
time.
2. The worst‐case risk benchmark. This is the worst draw‐down you’ll
have to trade through over an extended period of time—maybe the
worst case in the last 5 or 10 years.
3. The longest flat time. This is the longest time you’ll go before hitting a
new equity high.

Looking at worst‐case risk first, we’ve seen that the best managers in the
world normally average just a little more than their five‐year biggest draw‐
down. A goal of a tradeable system is one that averages more per year than
its largest draw‐down. This book will show that, in general, return can be
increased or decreased through leverage. As you increase or decrease the re-
turn, your draw‐down will also go up or down. It makes sense then to define
the max draw‐down you are willing to accept and use leverage to bring the
system performance down to a point where the max draw‐down is less than
your acceptable threshold.
It’s my experience that it’s hard to design a system with minimal “flat
time” as a design metric. You do the best you can through the development
process and when you’ve got something that meets your risk‐reward goals,
you see what the other metrics look like. If flat time, or some other charac-
teristic, is way over what you’re willing to trade through, the best way to fix
the problem is to introduce another type of strategy that is lowly correlated
to the one with the issue.

■ Know Yourself

It’s easy to start the design process and say, “I want something that has a max
draw‐down of 20 percent, and for that risk I’ll accept an average return of
25 percent.” The problem starts when you’ve developed such a strategy and

5

WHAT IS A TRADEABLE STRATEGY?

start to trade. Suppose you go three months and are down 10 percent. I
don’t care who you are, you’ll start to have doubts:

■ Is the strategy curve‐fit in some way?

■ Has the market changed?

■ Volatility seems too high (too low). Is that the problem?

If you’ve set your max draw‐down too high, by the time you recover, or
get to 20 percent, you’ll be a wreck. This is where it becomes important to
know yourself. If you’re new to trading and never experienced a 20‐percent
draw‐down, shoot for less—probably much less. I’ve talked to thousands of
traders, and most say, “I can weather a 20 percent draw‐down if the return is
X percent,” but in reality few can go that far. Don’t look at draw‐down as a
function of return. You’ve got to get through the draw‐downs to realize the
return, so set your max draw‐down at a level you know you can trade through.

■ Conclusion

The best money managers in the world average less than 30 percent a year, and
experience a max draw‐down every five years that is only a few percent less than
their average return. Unfortunately, statistics on average annual max draw‐down
aren’t easy to find, but that is an important number because that’s what you need
to trade through every year to achieve your return. Our goals then for develop-
ing a tradeable system must include these two performance objectives:

1. Average annual return is greater than max draw‐down over at least a
five‐year period.
2. Average annual return needs to be a multiple of the average annual max
draw‐down.

The third required performance objective is defined by the trader: What
is the max draw‐down you will tolerate? Pick a realistic number.
This book will show you how to develop strategies that meet these
objectives. In fact, when we’re done, there will be fully defined stock
and commodity strategies that you can trade as-is if you wish. Before we
develop these systems, there’s really another part to tradeability and that
is, your strategy must trade in real time like it does on your developmen-
tal data. The next chapter will discuss how to develop your strategy so it
trades like it back‐tests.

7
D
eveloping a tradeable strategy involves two steps. The first is to define
the risk/reward balance the trader can live with, and the second is to
develop a strategy that meets those guidelines and trades in real time like it
back‐tests. Most of the rest of this book will address how to develop strate-
gies that meet your trading objectives. This chapter addresses something much
more important: the pitfalls a developer will run into in developing a strategy
that will trade like it backtests. There are many “how to develop” ways; this
book only shows mine, but there is only one way to make sure your developed
strategy trades in real time like it backtests. That one way involves minimizing
curve‐fitting in your development and making trading assumptions in your
development that work as realistically as possible. Far and away, the biggest
pitfall is curve‐fitting. This chapter uses statistics to help clarify the concept,
but don’t skip over it because of the math. These are important concepts, and
if you don’t understand them you will continually develop systems that fail in
real‐time trading and you will never understand why.

C H A P T E R 2

Developing a
Strategy So It
Trades Like It
Back‐Tests

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