Way of the turtle

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The Optimization Paradox
The optimization paradox states that parameter optimization results
in a system that is more likely to perform well in the future but less
likely to perform as well as the simulation indicates. Thus, opti-
mization improves the likely performance of the system while
decreasing the predictive accuracy of the historical simulation met-
rics. I believe that an incomplete understanding of this paradox
and its causes has led many traders to shy away from optimizing sys-
tems out of a fear of overoptimizing or curve fitting a system. How-
ever, I contend that proper optimization is always desirable. 
Using parameter values that result from proper optimization
should increase the likelihood of getting good results in actual trad-
ing in the future. A specific example will help explain this. Con-
sider the Bollinger Breakout system, which has two parameters.
Figure 11-1 shows a graph of the values for the MAR ratio as the
entry threshold parameter, which defines the width of the volatil-
ity channel in standard deviation, varies from 1 standard deviation
to 4 standard deviations. 
Note how the results for a channel with a width of 2.4 standard
deviations are the peak for this simulation. Any value for the entry
threshold that is less than 2.4 or greater than 2.4 results in a test
that shows a lower MAR ratio.
Now, returning to our premise that optimization is beneficial,
suppose we had not considered optimizing the channel width and
instead had decided arbitrarily to use a channel width of 3.0 since
we recalled from high-school statistics that 99-plus percent of val-
ues for normal distributions fall within 3 standard deviations of the
average. If the future is fairly close to the past, we would have been
leaving a lot of money on the table and would have subjected our