Volumes, volatility and Hearst index - page 28
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Gentlemen, I don't understand where the belief in self-similarity comes from? What is it based on?
What's the problem? You have doubts? And on what do your doubts rest?
The self-similarity here should probably be considered as a similarity of notorious patterns on charts of different sampling rate (roughly speaking, some trajectory can be found on a monthly timeframe, but with a different step of increments in price - on minute, 5 minute, etc.).
But if the second ones (minutes, etc.) are made into older ones (including monthly ones), then the illusions are very convincing...
;)
What is the problem? Do you have doubts? And what are your doubts based on?
Yes, there are doubts.
At least because you have to trade on the "minutes" differently than on the "days". They are completely different things. Plus, if we consider Pastukhov's statistics, we can see that volatility changes with increasing H. Even if it is not strongly noticeable, but we can see the trends. Coming back to Hyo, in various researches on the Internet we can also notice that log-log plots do not form strictly straight line, as they should at self-similarity. This is also not in favour of the fractality theory. If you look at it from a fundamental point of view, global processes and "high frequency" processes occurring in the market are different, different groups of capitals are involved in them. Therefore, the only argument for self-similarity and similarity of charts on different timeframes seems to be ineffective. So there you go, in short.
That said, I'm not personally talking about the uselessness of Hyo, not at all. What I am saying is that the theory is not valid, or rather valid to a limited extent, and it is not Hyo's fault.
perhaps on an illusion, or maybe something will turn up ...
Trading by the minute and beyond, to me, requires highlighting a really 'substantial' movement, and any other estimates will be offset by a comparable (or even larger) spread in scale (from a non-tolerant distribution of the average spread)...
;)
Cantor's dust, as a principle, can be applied to any scalable section.
Like hammering in Galton's nails with varying precision - 2 digits, 3, 4, and now 5...
Imho.
;)
Yes, there are doubts.
At least because you have to trade on "minutes" differently than on "days". They are completely different things. If we consider the statistics according to Pastuhov, we can see that volatility changes when H increases. Coming back to Hyo, in various researches on the Internet we can also notice that log-log plots do not form strictly straight line, as they should at self-similarity. This is also not in favour of the fractality theory. If you look at it from a fundamental point of view, global processes and "high frequency" processes occurring in the market are different, different groups of capitals are involved in them. Therefore, the only argument for self-similarity and similarity of charts on different timeframes seems to be ineffective. That's it, in a nutshell.
I'd like to second that. In my previous posts in another thread I tried to prove that quotes of one timeframe by resonance frequencies are one thing and quotes of another timeframe are quite another.
If you remember fractals, they are derived algorithmically from one another. Cotiers are derived one from the other, but these transformations were not intended to be self-similar. If we take a senior timeframe and isolate a figure from it, will we be able to find the same figure on the lower timeframes. Not necessarily, and most likely not at all on this particular timeframe. On different timeframes we will probably succeed.
TS working on different timeframes find such similar shapes, but where? Somewhere. The same figures are in the quotient of different timeframes, but they are not related. There are areas between these figures found by the TS that have nothing to do with self-similarity. Can such "self-similarity" serve as an explanation for the fractality of quotes? By the way, I have not seen any TS using the ideas of Maldenbrot et al.
Proceeding at least from the fact that you have to trade on "minutes" differently than on "days". Totally different things. If we consider the statistics according to Pastuhov, we can see that volatility changes when H increases. Coming back to Hyo, in various researches on the Internet we can also notice that log-log plots do not form strictly straight line, as they should at self-similarity. This is also not in favour of the fractality theory. If you look at it from a fundamental point of view, global processes and "high frequency" processes occurring in the market are different, different groups of capitals are involved in them. Therefore, the only argument for self-similarity and similarity of charts on different timeframes seems to be ineffective. That's it in a nutshell.
That said, I'm not personally talking about the uselessness of Hyo, not at all. I'm saying that the theory is not valid, or rather valid within a limited range, and it's not Hyo's fault.
Colleagues, be a little more careful, this has already been written about/written about in this thread (why tread on the spot) - and it's exactly the same. Let me remind you that the quoting process is not self-similar, i.e. practically not at all, and those local areas where it literally happens to show up are on a very narrow scale. practical usefulness = 0. And no TA, much less nonsense in the form of VA worked and will not work.
But if you go deeper into FA, after picking with all sorts of correlation integrals, information dimensions, entropies, singularities, etc. (that's me, as you noticed - "crushing" intellect :o)))) + some optimism, then one can come to one very important conclusion. Quoting is an extremely complex process, but not random (!!!!). The process is not noisy, it is as we see it - but very complex(!!!)
But complex enough that it doesn't make sense to work with a quote directly - there is no such mathematical apparatus. So, we have to simplify it, introduce some transformations and work with them (which I would like to do). It's kind of obvious, but not very obvious - how to transform. And it is unlikely that filtering as such will work here.
It seems to me that the term "paternoster" should be seen in a broader sense. I will try to give my definition of a paternoster:
A PATTERN is divided into a "Causal PATTERN" followed by an "Investigative PATTERN". BP segments may include different number of elementary (indivisible) time segments (bars/types), while forming the same Patterns. The shape of the same Paternals can vary widely. The closest analogy is geometric figures - polygons. So, no matter how the sides of a triangle are changed, it will remain a triangle, excluding degenerate cases.
Different TFs form their own characteristic Patterns. It is not self-similarity or fractality. Patterns form all the time and are present in every indivisible segment of BP.
Somewhat summarily, but I have no other definition, but the principles I adhere to. In my opinion, the Paterns, as I've defined them, cannot be investigated by correlation and other statistical methods, and in general it is impossible to draw formulas of characteristic Paterns analytically, because they appear and disappear continuously, flowing into each other, at that, as I said, in each TF their paterns are different and do not depend on each other. Different combinations of PATTERNs in different TFs give different but moment-specific Investigative PATTERNs. It is like a kaleidoscope or snowflake pattern, although the patterns are infinitely many, but exclude the appearance of "impossible" patterns. That is, there is some set other than the set of Patterns.
It follows from all this that it is necessary to analyse Paterns simultaneously on different TFs. It is not the same as the Three Screens Method, which only gives discrete signals. The Method of Flowing Patterns (well, there's finally a name for my method) gives continuous (with the smallest possible discretization that is possible on the BP under study) signals in time.
May be, leading specialists of this branch may find my considerations useful, may be they will direct me in some useful direction. I watch with interest the development of social thought in this branch, but, in my opinion, Hurst and similar methods of estimation are a dead end, but this is my IMHO.
Somewhat similar thoughts:
The same figures are available in different timeframe quotes, but they are not related things. There are areas between these figures found by the TS that have nothing to do with self-similarity. Can such "self-similarity" serve as an explanation for the fractality of quotes? By the way, I have not seen any TS using the ideas of Maldenbrot et al.