Genetic optimization. Application in TradeStation environment.: Questions and Answer

Questions please.

Question:
Which parameter it is better to optimize and is it possible to optimize such parameter of the system as “leverage”?

Answer:
It is hard to answer unambiguously. What one SHOULD NOT DO: One should not choose NetProfit criterion since if we have such parameter as leverage then it is clear that the best specimen will be the one with the maximal lever because it will have the maximal profit.

Question:
Then what criterion is better to avoid overfitting and improve the stability of the system?

Answer:
First, it is necessary to choose another optimization criterion than that one which characterizes only the profitability of the system. In another words it is necessary to include in fitness indirect parameters, i.e. those parameters that characterize the quality of the system indirectly. It can be the number of deals or trading time for example. Second: it is possible to apply “FreshBlood” function, which causes perturbances and system that had maximal fitness and replaced other specimens from population could be not the best as a result of usage of this function. As a result we would keep in population other more viable specimens which could show better results on “out of sample” data for example. This problem as a whole is known as getting in the local extremum. In TSGO search goes rather uniformly on the whole plane. As a result we get population that is the section of the whole plane that we have. Though if there is a local peak it can be the first specimen in our population. In such case we might choose not the best specimen in future. On the whole the stability of the system can be conditioned by some limitations. One can set bigger transaction costs for example. If the system generates too many number of deals then such system will not survive. In reality such strict conditions may not occur but I think one will feel more comfortable with such system than with system that was tested under more soft conditions.

Question:
What number of iterations it is necessary to choose when using genetic optimization? Will it be better if we choose as big number as possible?

Answer:
Several hundreds of iterations are enough for proper result. In order to estimate the situation quickly one hundred is enough.

Question:
When it is necessary to stop the optimization?

Answer:
It depends on number of iterations we specified and what result was obtained. If the number of signals, coefficients and filers is big and we set strict limitations on population size but there are no specimens with good fitness then we should continue the optimization.

Question:
What is the reason of testing on “sample” data in your program for genetic optimization in TradeStation and then representing the results obtained on “out of sample data” at the same time in your example of signal?

Answer:
There is no specific reason. It is just technical possibility to make useful search on “sample” data and then without even pressing a key get the same result on “out of sample” data. Actually it is the variant of technical solution for those who need it.

Question:
How much time does it take to find a proper result in your example of system?

Answer:
The search of proper solution from several thousands of variants takes for several minutes.

Moscow, 2004..
Trade Smart Research
www.tsresearch.com

Genetic Optimizer for TradeStation
www.tsresearch.com/software/genetic_optimization/