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On Mon, 17 Apr 2000 07:26:45 +0530, you wrote:
>In a system testing it is said that more the events (statistics)
> the better would be the result.
This may not be correct, because market characteristics can definitely
change over time. If so, it does not make much sense to fit the system
to "old behavior". Moreover, it might make sense to emphasize the most
recent market behavior, e.g. by weighting over time.
>This also tells that a system
>having lesser number of events is doubtful?
This in general seems to be true, because more events lead to more
(statistically) reliable test samples.
>If you have data
>of a longer period than getting a large number of statistics is
> not a problem but if you have a data of lesser number of
>periods than what to do? Does it mean, that one cannot apply
>the system to a stock which has a less trading history
> (say 2-4 years).But a question arises how many events a system
> should produce to draw a reasonable conclusion?
You may test this e.g. by constructing (probability) distributions of
price changes for different time periods. If the distributions do not
change "too much" e.g. in terms of tail-iness, you may use the shorter
time period.
>If a system looks into one single pattern it may produce lesser
>events. So can we say that the pattern is doubtful because of
>lesser statistics? What is the solution?
Imo, in most situations it does not make much sense to use only one
single system, because in general any system works like a filter,
which is looking only for (a) special pattern(s). This way, all other
trading strategies (or profitable patterns) are not considered by this
system.
A solution might be to use _a bunch of systems_ directed to different
profitable patterns. This kind of compound system may smoothly
maintained and optimized by a genetic algorithm, which can handle
"generations" of systems.
mfg rudolf stricker
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