The Sultonov Regression Model (SRM) - claiming to be a mathematical model of the market. - page 25
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We can successfully trade at a normally distributed price because we know that the probability of finding the price near the median is higher than far from it. In other words, we trade in the direction of the median. You can call it price prediction, but you don't need any market model, regression or neural network for successful trading.
We can successfully trade at a normally distributed price because we know that the probability of finding the price near the median is higher than far from it. In other words, we trade in the direction of the median. You can call it predicting the price, but you don't need any market model, regression or neural network for successful trading.
You contradict yourself, the median is the result of regression in these cases!
The median is calculated as follows
m = SUM( x[i] )/N
I don't see any regression here.
Man, what a mass insanity...
What's the problem? The conversation was about a normally distributed price, not random rambling, which are two different things.
The median is calculated this way
m = SUM( x[i] )/N
I don't see any regression here.
To see regression here, just transform for recursive recalculation.
(and it's not the median, by the way ;)
The median is calculated as follows
m = SUM( x[i] )/N
I don't see any regression here.
If you don't see it, it doesn't mean that the same result can be obtained by regression analysis of available observational data. By the way, RMS also satisfactorily describes the law of normal distribution itself with an error of 3.85%:
If you do not see it, it does not mean that the same result can be obtained by regression analysis of the available observational data. By the way, RMS also satisfactorily describes the law of the normal distribution itself with an error of 3.85%:
Just because you can fit your regression model into anything doesn't mean you have to do it.
All the basic assumptions of correlation and regression theory are based on the assumption that the data under study is normally distributed. Do your inputs (price) have a normal distribution?
Just because you can fit your regression model into anything doesn't mean you have to.
Well, don't :)