The Sultonov Regression Model (SRM) - claiming to be a mathematical model of the market. - page 41
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The second column is Yi? Him?
Build a linear regression line first, then applaud.
What's there to build, it's pretty clear how it should go. And your PMS, if it were more humanly constructed, would have to straighten out at 0.5.
Please:
Not 0.5, but still... 0.486691 at one end, 0.491087 at the other.
The average is 0.4889.
Not 0.5, but still... 0.486691 at one end, 0.491087 at the other
Yes, I must have overdone it with the zeros, if you shift the graph a bit, it turns out that in both cases MO=0.5:
Here https://forum.mql4.com/ru/19762/page30 was asked to describe a random sequence of 10 digits as a market model. This is what came out in the case of RMS and LR:
Good point also from here https://forum.mql4.com/ru/19762/page29
gpwr 09.06.2009 03:27
Sorry for the intrusion. I read almost the whole thread and couldn't understand what the Fourier argument is about. The subject of the branch is the description of market conditions affecting the future price movement. What does Fourier have to do with it? I agree that the price movement can be decomposed into sines and cosines: m+An*cos(wn*t)+Bn*sin(wn*t). So? The spectrum (An+j*Bn) will be our description of the market state? The idea is interesting. But in the discrete Fourier transform the number of sines and cosines equals the number of prices taken. What then is the advantage of using the output parameters of the DFT (An and Bn) to describe the market? The number of variables is not reduced. So we have to take the largest amplitudes sqrt(An^2+Bn^2). They with their frequencies become the market description? Am I going in the right direction? Using these parameters (An, Bn, wn) we will predict the future by extrapolating the corresponding sines and cosines into the future? Have done such a thing. There is a great misconception in this approach. The Fourier transform is nothing more than fitting a trigonometric series to the original price curve. It makes as much sense as fitting polynomials and other functions to the price curve. You can twist it and take Bessel's functions, sinc, Si and so on. All these adjustments will reach their goal of exact reproduction of the price. But who told us that there are trigonometric functions or polynomials or Bessel functions hidden in the price movement. They are only approximating functions. They can be fitted to anything. To extrapolate sines and cosines you must first prove that the motion of prices is described by ordinary differential equations as an oscillating circuit. I find it hard to see the benefits of the Fourier transform to describe the market. Although I won't mind if someone decides to change my mind. Who has other ideas?
I suggest you look at the view of the function obtained by differentiating (18) and which is the density of the RMS distribution function and given in the article as (7), which (the view) suggests is very similar to the behaviour of EUR/USD during its evolution:
I suggest you look at the view of the function obtained by differentiating (18) and which is the density of the RMS distribution function and given in the article as (7), which (the view) suggests is very similar to the behaviour of EUR/USD during its evolution: