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For
what it is worth, I offer my concept of a robust system.
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I
develop systems using data from the U.S. stock market and I select
parameters for those systems based on the same data.
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My
"robustness" test is achieved by running the same systems, same parameters on
data from the following stock markets:
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<FONT face=Arial
color=#0000ff size=2>Canada
<FONT face=Arial
color=#0000ff size=2>U.K.
<FONT face=Arial
color=#0000ff size=2>Japan
<FONT face=Arial
color=#0000ff size=2>Australia
<FONT face=Arial
color=#0000ff size=2>Germany
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size=2>
In
order to be considered as "robust", the system must have a CAR or MAR at least
90% as good as that achieved by backtesting (in-sample) the U.S. data with a
drawdown not exceeding 110% of the drawdown experienced when backtesting the
U.S. data. Frequently, the results are actually better when applying
a system to one of these other markets.
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In all
cases, I would run over at least 10 years of data including extinct
stocks.
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Of
course, if I'm using an index such as the RUT for timing the U.S. market, I
usually have to substitute an index from the corresponding market.
Some markets simply don't have adequate (IMO) indexes, so I have to create my
own.
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That's
my approach and it works for me.
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