Absolute courses - page 39
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There may be many solutions, but the limit transition satisfies one. We are looking for such E, D, Y which would have in relations correlated with known relations with coefficient = 1, and between themselves would be MAXIMUM close to unities (I mean correlation coefficients). Reached the maximum possible ceiling corr(E,D)+corr(E,Y)+corr(D,Y) -> 3 as close to 3 as possible. There is, of course, only one such solution. On real courses it has a limit, it cannot reach 3. On HSPC it can.
There you go. I meant that there are many solutions, but there should be a kind of limit point approximating the most optimal variant. In other words, you pick and pick, the spread decreases, but after a certain point, the optimum point comes, after which the spread is not reduced, and it begins to grow again. Is it so?
There may be many solutions, but the limit transition satisfies one. We are looking for such E, D, Y which would have in relations correlated with known relations with coefficient = 1, and between themselves would approach maximally close to unities (I mean correlation coefficients). Reached the maximum possible ceiling corr(E,D)+corr(E,Y)+corr(D,Y) -> 3 as close to 3 as possible. There is, of course, only one such solution. On real courses it has a limit, it cannot reach 3. On HSPC it can.
Well, why do you draw far-reaching conclusions from a single experiment on PRNG? Especially since you recently had the opposite theory - that all currencies are related (inflation etc) and therefore it is possible to find such E,D,Y. Do some normal research
Why do you draw far-reaching conclusions from a single PRNG experiment? Especially since you recently had the opposite theory - that all currencies are linked (inflation, etc.) and therefore it is possible to find such E,D,Y. Do some normal research
What do you mean the opposite theory? That's what it is. The HSPC experiment confirmed my ideas. A clear demonstration of the existence of differences in E, D, Y forms in the case of real data and the absence of differences in the case of HSPC. It is characteristic of the human brain to draw far-reaching conclusions from incomplete data. Neural network. Although of course I agree that in the evening and tomorrow or the day after the question should be discussed in more detail.
triangle in the sense of a plane triangle with vertices 1.33 0.99 0.010
0_o This is a kind of tricutnik on a straight line, not a triangle on the plane. Meanwhile, it is obvious that for three points N=3 on the plane (dimension of space d=2) with three links (l=3) the number of independent variables is s = d*N-l = 3, so attempts to represent "triangle" have sense, in fact it is even elementary :-) any ideas how?
P.S. Why - another question... :-)
What do you mean by an opposing theory? It is. The HSPC experiment confirmed my ideas. A clear demonstration of the existence of differences in E, D, Y forms in the case of real data and no differences in the case of HSPC. It is typical of the human brain to draw far-reaching conclusions from incomplete data. Neural network. Although I certainly agree that in the evening and tomorrow or the day after the question should be discussed in detail.
They won't. I guess my algorithm will crash and will not be able to represent three similar curves with CC->1. The very possibility to converge to a single shape is determined by the non-randomness of quotes. I'll give it a try. I will post it here today, tomorrow, the day after tomorrow. Actually, in my past incarnations here I myself have repeatedly suggested testing all sorts of algorithms on Gaussian white noise and on simple functions (sines, meanders, steps).
Now it turns out that "algorithm is not destroyed", and "random" on the contrary KK->1 unlike non-random)))
0_o This is a kind of tricutnik on a straight line, not a triangle on the plane. Meanwhile, it is obvious that for three points N=3 on the plane (dimension of space d=2) with three links (l=3) the number of independent variables is s = d*N-l = 3, so attempts to represent "triangle" have sense, in fact it is even elementary :-) any ideas how?
P.S. Why - another question... :-)
why explain, I mean your data triangle vertices are your currencies before (fuck I keep forgetting that word)))) normalisation. On the vertical mv we see all 3 vertices of it on one line, no volumetric view. That's what I was talking about. And what for to look at its dynamics in volumetric views is another question. In order to trace the connection between the pairs and the currency pair results. Triangles there and there.
Now it turns out that "the algorithm is not destroyed" and that the "random" ones have a CC->1 as opposed to the non-random ones)))
Well, yes. It turns out so. So? Are you accusing me of not guessing right away how it will behave?
Yeah. Turns out I did. So what? Are you accusing me of not guessing right away how he's gonna behave?
No, jumping to conclusions.)
Colleagues, I have made new progress. Which has somewhat clarified my point of view on the processes taking place. I will now show you everything in more detail.
With your permission on the new, current, today's data files. So, watch your hands: here are the EURUSD and EURJPY files.