No, the parameters don't need to be rescaled in the call to super(). The
intent of passing parameters to the super() is to calculate a key which
uniquely identifies an indicator and its parameters. This allows the
indicator manager to reuse indicators during optimization, instead of
creating new instances all the time. Here is the relevant code which
calculates the key:
protected Indicator(int... parameters) {
name = getClass().getSimpleName();
if (parameters.length == 0) {
throw new RuntimeException("No parameters passed from the
constructor of indicator " + name);
}
StringBuilder sb = new StringBuilder();
sb.append(name);
for (int parameter : parameters) {
sb.append("/").append(parameter);
}
key = sb.toString();
}
On Sunday, December 19, 2010 12:32:11 AM UTC-5, Alexana wrote:
>
>
> Eugene, in the TensionKalman.java your are re-scaling the fastNoise and
> slowNoise:
>
> *kalmanFastPrice = getInstance(fastNoise / 100.0);
> kalmanSlowPrice = getInstance(slowNoise / 100.0);
> *
> yet, in super( ), it is not re-scaled:
>
> *super(fastNoise, slowNoise);*
>
> Should the parameters be re-scaled here as well?
> ------------------------------
> *From:* Eugene Kononov <[email protected]>
> *To:* [email protected]
> *Sent:* Tue, November 30, 2010 6:41:08 AM
> *Subject:* Re: [JBookTrader] Re: Status of Kalman filter?
>
>
> In the constructor in TensionKalman, it has
>> super(fastNoise, fastNoise);
>> should it not be:
>> super(fastNoise, slowNoise); ?
>>
>>
> Yes, that was a typo.
>
> >addParam(FAST_ERROR, 0, 20, 1, 0);*
> *
> >has 0 as the last parameter. I understand the significance
> >of the first four parameters for a search algorithm.
> > I am not sure what the last
> >parameter, value, signifies within the optimization algorithm context.
>
> The first three values, "min", "max", and "step" are used by the
> optimizers. The last value is the actual parameter value used when the
> strategy is running in the back testing, forward testing, and trading modes.
>
>
> >The other area where the problem may lie is in the optimization step.
> > I would expect the measurement_noise values to be somewhere between
> > 0 and 1. For fast_error it may be 0.01 and for slow_error perhaps ten
> times
> >that, 0.1.
>
> I didn't realize the error should scale between 0 and 1. I've made
> modifications in the attached version to reflect it.
>
>
>
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