Ultimate Algorithmic Trading System

is profit, x-axis is one parameter, and the z-axis is another parameter. The curve is
the different profit levels across the different values of the two parameters. If you
are looking to maximize profit, then the best parameter set, historically speaking,
would be at the peak (0.75, 0.35). If you are looking for robustness, then you don’t
want the peak; you want a parameter set located on a high plateau. Robustness is
the same as parameter stability. If a parameter is stable, then changing it up or down
will not have a huge impact on overall performance. If it does, then the parameter is
not considered stable. TradeStation includes, with its genetic optimization method,
a parameter stress test. This test includes two inputs, number of stress tests and
percentage of parameter change.
If three is chosen as the number of stress tests and 10 percent as the stress
increment, then the total number of tests will triple. For example, 7,250 iterations
were generated by a simple algorithm optimized using the genetic method and a
stress test of one. When the algorithm was reoptimized with a stress test number set
to three and the stress increment to 10 percent, the number of increments increased
to 21,750. What the genetic optimizer did was test each parameter set and then
increased/decreased each parameter by 10 percent and then retested, thus creating
three different sets of tests. A simple standard deviation algorithm example should
help illustrate what the computer is doing:
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GENETIC OPTIMIZATION, WALK FORWARD
Test 1: Normal
Moving Average Length = 50
Up Band = 2
Dn Band = 3
Test 2: Parameters are stressed upwardly by 10%
Moving Average Length = 50 + (10% of 50) = 55
Up Band = 2 + (10% of 2) = 2.2
Dn Band = 3 + (10% of 3) = 3.3
Test 3: Parameters are stressed downwardly by 10%
Moving Average Length = 50 – (10% of 50) = 45
Up Band = 2 + (10% of 2) = 1.8
Dn Band = 3 + (10% of 3) = 2.7
Stress testing eliminates parameter sets that fall on top of peaks, therefore
theoretically revealing a more robust set. If a certain parameter is profitable, and its
neighbors are nearly as profitable, then the parameter is deemed robust.
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