Ultimate Algorithmic Trading System

The results from the two tests are quite similar; results only start deviating after
the top six parameter sets. This example is not perfect for demonstrating the power
of GA, as the brute force approach only took 13 minutes, but the implications of its
power is more than evident. In many cases, a brute force optimization scheme will
be too time intensive, and the only alternative is a GA.
TradeStation makes using the Genetic form of optimization very simple. All
it requires is selecting it over the Exhaustive mode. This simple switch makes
implementation simple, but you do have the ability to override the Genetic
optimizer’s default settings. If you select Genetic as the optimization method
and then click Advanced Settings, a dialog box will open that will allow you to
override the default settings. Before changing these, you want to make sure you
fully understand what you are doing. Here is explanation of each of the Genetic
Optimization Settings.
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GENETIC OPTIMIZATION, WALK FORWARD
Generations
This setting indicates the total number of generations each test will iterate to
come up with a solution chromosome. In our very first example, the number
of generations concluded after a single chromosome fulfilled the requirements to
solve the simple equation. The computer program and process could continue until
multiple solutions were uncovered. Trading algorithms don’t have a black-or-white
solution so you can specify to only go so far in the process. This number should
increase with larger search spaces. This setting should be ‘‘Suggested’’ by clicking
the Suggest button.
Mutation Rate
Remember how a gene in our example chromosomes could be randomly changed?
This rate defaults to 0.1 or 10 percent, and this is probably the best value for this
setting. Higher mutation rates increase the probability of searching more areas in
the search space and may prevent the population from converging on an optimal
solution. Searching a larger space takes more time and a mutation could randomly
change the gene of a good solution after selection and reproduction and turn it into
a nonoptimal solution, thus preventing convergence.
Population Size
Referring to our initial example of a simple GA, we used six different chromosomes
to solve our problem. So its population size was six. Population size grows with the
size of the search space. A small population on a large search space could lead to less
robust solutions. Initial population sizing is very important and is very difficult to
gauge. Again, let the Suggest button help here.
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