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Hello Fred,
I am actually trying to better anderstand how work your polyfit
version, maybe to try to make other sort of fitting (exponential
fitting, inverse fonction fitting...).
For the Gaussian elimination in VBScript, it is to solve the linear
equations via matrix resolution with gauss pivot.
But I don't anderstand the parameters you send in the gaussian
elimination fonction.
It seems you scale the time axis with a ramp from -1 to 1. After tou
make ^2,^3,etc, of this scale axis.
My first question : why take the sum of the x^n (i.e LastValue(Cum
(.)) ) ? Some sum are null because of the symetry of the ramp [-1;1].
And for the price (Y) data, you multiply them by the ramp [-1;1] and
make them power of this ramp. Why ? And same thing, why make a
LastValue(Cum(.))?
For now, it seems you fit the polynom by anchor it to his most
recent and older value on the range. The value in the middle are
less weighted. And you fit him by using average value of price (so
the cum funtion). But maybe i am totaly wrong.
Do you try with the least square method or maximum correlation to
fit the polynome ? It is too much computer time consuming ?
Hope you will light my head who is burning for now héhé : )
Thanks,
Mich.
--- In amibroker@xxxxxxxxxxxxxxx, Fred Tonetti <ftonetti@xxx> wrote:
>
> Andy,
>
>
>
> Can you describe in English what your AFL does ? ...
>
>
>
> I've been playing with a Trig Fit a la Claud Cleeton the steps for
which I
> would describe as follows ...
>
>
>
> 1. Optional - Normalize the input i.e. Data = log10((H + L) / 2)
>
> 2. Calc an arbitrary length ( Parameterized but 11 at the moment )
centered
> moving average ( CMA ) of the data
>
> 3. Calc a 1st order least squares fit ( LSF ) of the CMA over the
period
> desired ( from / to range marker )
>
> 4. Subtract the LSF points from the data points resulting in
detrended data.
>
> 5. Take an n-bar sampling of the detrended data. This array
with "holes" or
> "gaps" in it needs either to be compressed or have the "gaps"
filled ... I
> elected ( for the moment ) to calc a cubic spline to fill the gaps
(
> interpolation ) ...
>
> 6. Calc a LSF of the detrended data resulting in the coeffs for
the Trig
> equation Y = A Cos wX + B * Sin wX
>
> 7. Calc the correlation of the resulting sin wave to the original
detrended
> data.
>
>
>
> Repeat steps 5 & 6 varying n from 1 to ? looking for n where the
correlation
> is the highest. This should yield the equation or data points
that most
> closely correlate to the detrended data.
>
>
>
> 8. Subtract the points in the sin wave from the detrended data
resulting in
> a modified detrended data.
>
>
>
> Repeat steps 5 - 8 looking for the next most significant cycle.
This can be
> done repeatedly until overall correlation stops getting better and
usually
> results in 2 - 6 cycles ...
>
>
>
> See attached .
>
>
>
> The white line in the upper graph is detrended price .
>
> The alternating green / red line is the trig fit, in sample up to
the
> vertical line and out of sample projection afterwards .
>
> The lines in the bottom section are the individual cycles found in
the data.
>
>
>
> Sometimes the projections are almost clairvoyant . run time
however is
> anything but quick .
>
>
>
>
>
>
>
>
>
>
>
>
> _____
>
> I am using the free version of SPAMfighter for private users.
> It has removed 8649 spam emails to date.
> Paying users do not have this message in their emails.
> Try SPAMfighter <http://www.spamfighter.com/go.asp?t=249> for
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