Out of sporting interest, I engaged in adaptive quote filtering

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..., refreshing my memory a little bit about what we went through on DSP. And I got this (let me tell you right away, there are no redraws):



The question to those who have worked closely on this: is this (graph) good or not?

 
Digital price processing is a big topic. I haven't done much about it. I haven't found any luck. Can you elaborate on the curve?
 
I don't see anything in particular. Maybe I missed something, then specify what you wanted to get. For example a mash for 6 at average prices would give almost the same result (didn't check) but it seems to me. :)
 
alsu писал(а) >> Question to those who have worked closely on this: Is this (graph) good or not?

It is not clear where the inputs should be.

 

Let me explain briefly (I'm sorry, I'll leave out the details)

We have a recursive filter defined by difference equation, similar to EMA, with parameter alpha. Since there is only one parameter, we will adapt it. Idea: let it check if the market behavior corresponds to a certain a priori set model at any given moment of time. A certain value, which we will call "adequacy ratio" (AR) for the sake of certainty, is introduced and calculated. Let it vary from 0 to 1, where 1 corresponds to complete "correspondence" and 0 to complete "nonconformity". Further reasoning is as follows: if KA is close to one, then the market may not be monitored too "closely", as it develops predictably within the framework of our model. If, on the contrary, the KA is close to 0, we believe that the predictability of market behavior has declined and the price should be tracked "more carefully". We conclude that the parameter alpha should be a monotonic function of CA. Let's select the dependence (e.g. linear) and go ahead.


As can be seen from the figure, it is quite good to track the trends (though always differently) and nail the unnecessary fluctuations in the flat. I would like to hear someone's opinion, based on personal experience, as to how successful this is.

 
SProgrammer >>:
Не вижу ничего особенного. Может я что-то не заметил, тогда уточните что вы хотели получить. Например машка на 6 по средним ценам даст почти такой же результат (не проверял) но мне кажется. :)

Yes, you should have done it right away.

Here: the blue one is the yamashka(6) in the middle


 
LeoV >>:

Не понятно где должны быть входы.

I don't understand it yet either, but if you're familiar with engineering, you might know the feeling - somewhere around the heel, there's a distinct feeling that there's something here that you can't get hold of... That's why I'm bringing up my topic, I don't usually do that

 

Then show a linearly weighted one,


myself

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Nothing is redrawn :)

 
SProgrammer >>:
Тогда покажите линейно взвешенную,

here's the one that seems closest graphically: linearly weighted(2):


you see - the blue one jumps in the flat areas and the red one behaves much more calmly. Both are in trend and both are about the case

 
alsu >>:

вот из того, что вроде как ближе всего графически: линейно взвешенная(2):


видите - синяя на флетовых участках прыгает, а красная ведет себя гораздо более спокойно. При этом в тренде и там, и там около дела

I have shown above that linearly weighted 3 behaves even better than yours, well the lag is less.

Adaptive filters are done with a spectrum, first the spectrum is detected, then three to five frequencies are selected from the highest power and filtered.

But I have to say at once there is no special result.

 
alsu писал(а) >>

You see - the blue one jumps on the flat sections, while the red one is much calmer.

alsu, so do you need it to behave more calmly?

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