You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Well, he was wrong to go after Kalman. If I understand correctly, it is talking about Kalman filter with constant coefficients known as alpha-betta-gamma filter (these are different modifications of Kalman filter).
You need a Neutron.
here we have compared Kalman filter (correct) and Butterworth filter.'Random Flow Theory and FOREX'.
There's an algorithm for calculating them on matcad. If anyone ventures to make a Butterworth filter in MQL I can help (explain what's there and how it's calculated in Matkadec), and I don't think JMA will be better (you can compare).
Kalman is inherently iterative MNC so there's only one way around it, if the models embedded in the filter don't match the process under study. (They just don't know how to prepare it :-))
Understand the word adaptation implies an answer to the question of what you have to adapt to. In radiolocation there are such concepts as signal (useful component) and noise (what disturbs us). Having understood this question it is possible to make adaptive filters, until you answer this question it is not clear what you need to adapt to.
2 grash
Here is what the author himself writes about JMA ) - http://www.jurikres.com/catalog/ms_ama.htm#top
Since all this is for sale, we get only disassembled code, as I understand it, and I really want to understand what is the trick )
Thanks for the link. I think this ferret is using a rather tricky adaptive filtering algorithm, most likely with prediction elements based just on autocorrelation. I think so. :o)
to Privalto Prival
It doesn't matter what I researched, but I 'caught' the ACF for company. This is just an observation, not confirmed by anything, roughly speaking, looked at the results with an eye and "caught it". Not proven by anything, not statistically confirmed, probably complete nonsense, but worth checking on occasion. The point is to make some assumptions about the development of the series by the type of ACF. So far I have roughly classified 2 variants (ACF is taken from the black series, gray series - process development). I am giving it without special comments, it is kind of obvious:
Variant A
Variant B
PS:
Prival - I have written, that in DSP - I am self-taught and my limited and technical illiteracy is obviously not enough to understand, that Nyquist frequency rules the world ...
I don't know what you can see, I can see from ACF that for variant A I can predict for 200 samples (I don't know if you have X minutes or what else). Option B is 50, then the nature of the process changes, but you need to look at the dynamics, because the ACF changes over time. And the first thing this function shows is the correlation time (time during which the process can be predicted) + the second is the type of process itself, almost always an oscillatory circuit (in radio engineering terms); we can further classify it by types of oscillatory circuits but in my studies (at this stage) it is not so important. At first it is necessary to deal with one kind of vibrating element, it will be easier to deal with others by analogy.
I tried to classify "by species and type" by simple observations:
In general - if it's not the main thing, then it doesn't matter... good luck
This JMA is very impressive, very impressive. I somehow didn't pay too much attention to it before, as I have a preconceived notion of muwings. But now it looks like I'll have to reconsider it.
As for that JMA that's in Code Base ('JMA'), it clearly doesn't look like the original one. Yes, it's smooth, but it clearly lags more.Parabellum's drawing there is much more convincing.
And there again appears the problem I am struggling with: I want to transform the chart of initial quotes so that it could eliminate disasters and then apply Jurik's indictors (or their clones) to the transformed chart... Somehow it seems to me that even if the distribution turns into something similar to Gaussian, nevertheless the price process will not be Wiener-like - because its Hurst index will be more than 0.5 (because of dependence of neighbouring samples).
P.S. Prival, again to you: http://www.jurikres.com/faq/faq_ama.htm#betterthan . Especially look at the third figure from the bottom: JMA, unlike other filters, has practically no Gibbs effect (spike after gap). And there are effective techniques to remove this effect (when I was a student I came across a book by Hemming "Digital Filters", I need to find it).
It's a very impressive JMA, very impressive. I somehow didn't pay too much attention to it before, as I have a preconceived notion of muwings. But now it looks like I'll have to reconsider it.
As for that JMA that's in Code Base ('JMA'), it clearly doesn't look like the original one. Yes, it's smooth, but it clearly lags more. Parabellum's drawing there is much more convincing.
Prival, thanks for the book. And here's another surprise for you, confirming your view of price as a target:
Taken from the same place, emphasis added.
Second. JMA doesn't redraw, so there's no FFT to speak of. Nevertheless they have removed the Gibbs effect...
Third. The Jurik Research team assumes something similar to the Cauchy distribution as a distribution model. What it is, you know: none of the moments of this distribution exists, not even m.o. Feel the ambush the enemy has set for us? Although, on the other hand, it's possible that their goal was simply to build an inductor to effectively smooth out even random wanderings with Cauchy-distributed increments.
2 Rosh: well, at least you solved the mystery of one Jurik inductor. Respect.