The Sultonov Regression Model (SRM) - claiming to be a mathematical model of the market. - page 22

 
faa1947:

Emissions must be thrown out.

Yeah and news and trends and everything else, let's keep the sine wave, eh?
 
HideYourRichess:

can you regress a line like that, too?

A rare but beautiful example of how it is.

Please provide a timeline (preferably) of the bar and let's see what happens.
 
yosuf:
Yusuf, can a random series be predicted that way too?
 
Mischek2:

Yeah and news and trends and everything else, let's leave the sine wave, eh ?


The market has a systematic non-stationarity. That is the problem. The problem with news is the other kind. The other kind of emission generates breakpoints for which there is generally no cure.

As of today, the model is being investigated for the occurrence of an outlier. Ideally, the model should ignore an outlier if, after an outlier, the quotient has returned to its previous movement. If there is a new movement after an outlier, then rebuild (model adaptation). Outliers are a separate independent step in model development, which is not usually done in the TA framework.

 
faa1947:

Emissions must be thrown out.

Remove the outliers? I suspect people really did walk out of those "outliers" in their socks ;)


By the way, here's a sequel, 4 shocks of different sizes and 4 "fading processes", i.e. triangles


 
DmitriyN:
Yusuf, can a random series be predicted that way too?
"Randomness is a manifestation of necessity" (Marx). But seriously, in every random process we can always find a hidden pattern, although we don't notice it. A clear example is the one considered from the 90 sequences 0 and 1 in this thread. What pattern shifted the MO from 0.5 to 0.8787 with equal numbers of 0 and 1 hits? Most likely, the pattern RMS found in the grouping system of zeros and ones, if one can put it that way, but it found a clear pattern of MO shifts immediately, and ordinary observation does not allow to detect or understand it.
 
So we throw away the good stuff and choke on the rest before we digest it?
 
faa1947:

The market has a systematic non-stationarity. That is the problem. The problem with the news is something else. They give rise to breakpoints for which there is generally no cure. As of today, the model is being investigated for the appearance of outliers. Ideally it should ignore if after an outlier the quoter has returned to its previous movement. If a new movement, then rebuild (model adaptation). But this is a separate independent stage of model development, which is not usually done in the TA framework.

And how can the model and the Expert Advisor based on it ignore spikes and be profitable? That is, we analyse prices without spikes as a stationary series and make a forecast, but in reality we get spikes in the opposite direction.
 
faa1947:


The market has a systematic non-stationarity. That is the problem. The problem with the news is a different one. Another kind of emission generates breakpoints, for which there is generally no cure.

As of today, the model is being investigated for the emergence of outliers. Ideally the model should ignore an outlier if after the outlier the quotient has returned to its previous movement. If there is a new movement after an ejection, then rebuild (model adaptation). Outliers are a separate independent step in model development, which is usually not done in the TA framework.

We still need to try to predict these outbursts, because they cannot form immediately in an empty place, probably there are precursors in the BP that we do not notice, as in the case of tangent, there is also an even sequence of 9 digits did not signal anything, and radar clearly felt wrong, which occurred.

By the way, we should analyse the reaction of the RMS to arithmetic and geometric progressions.

 
faa1947:


The market has a systematic non-stationarity. That is the problem. The problem with news is the other kind. The other kind of emission generates breakpoints for which there is generally no cure.

As of today, the model is being investigated for the occurrence of an outlier. Ideally, the model should ignore an outlier if, after an outlier, the quotient has returned to its previous movement. If there is a new movement after an outlier, then rebuild (model adaptation). Ejections are a separate independent stage of model development, which is not usually done in the TA framework.


It's not even funny.

You will be blown away by definitions alone.

The "news" has no problem.

your view is an adaptation of your understanding and vision of the market