Volumes, volatility and Hearst index - page 35
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And there are topics that manage to go on forever - say, the one about the catch :)
Come on, it's 34 pages and I've got a lot of anecdotes and funny stories :o)
Sergei, you... don't... Don't scare people more. He's already scared himself.
Why do you think I'm not starting? I'm scared myself :o)
joo:
There won't be any basic text(more detailed). It will go beyond the scope of this thread. Also, something interesting was about to be narrated by Farnsworth.
It's a pity it won't be. As for going beyond the scope of this thread - do you think its initiator will mind? :)
By the way, the first stages of your and Farnsworth's work are formally similar, I mean this:
-Preparing BP in order to get a distribution without fat tails.
-The steady-state view on the relative scales of the paternoster.(... temporarily removed the test result. I need to check it thoroughly, there seems to be an error)
to Mathemat
жили-жили, не тужили, а тут выясняется, что это, оказывается, предел...
so it is the real limit (don't even bother imagining it, just recall the formal definition of fractal dimension)
and it is not even clear how it is computable, since it is connected with an asymptote.
This I do not understand.
to joo
There will be no main text (more detailed).
That's for nothing. One topic, maybe change your mind?
I get the impression that you don't read the posts. Is it not enough for you that the theoretical and calculated results coincide? Or do you think that such a coincidence could happen by accident?
No, a mere coincidence of theoretical and computational indicators is not enough. You also need to match the content.
Yurixx: Never mind. As for the rest of our disagreements, the situation is the same - we speak different languages. Leaving aside trivialities and coming back to self-similarity we have: you believe that self-similarity is quite defined by a number, and this number should be constant, while the only argument for self-similarity is the similarity of graphs on different TFs. And this all seems to me to be an unwarranted simplification. How can we come to an agreement ? Can you give us your definition of self-similarity?
No problem, different languages mean different languages.
One observation. Really, besides "the only argument for self-similarity - similarity of charts in different timeframes" - I haven't seen anything else. Regarding "figures" - I'll fix my position once again, and that's it. The "figure" of fractal dimension was not invented by me. It does not necessarily have to be strictly the same for different "levels". But it is desirable that its deviations should be controllable and have no regularities.
And don't ask me for a definition of self-similarity - it's me who's unhappy about the lack of such a clear definition, and it's me who was asking where the boundary is between "similar and the same".
That's all for now.
joo:
The deadlock lies in the discreteness of TA signals, that famous and ubiquitous "buy, sell, smoke bamboo", which results in lagging and other inevitable "gyres" of TA. Then came the study of optimization algorithms in general and GA in particular as a tool for learning networks, and as a potential possibility of creating adaptive self-organizing systems with AI (limited by trading purposes of course). This is where the contours of CCI begin to emerge. A theory that is devoid of contradictions of the Dow Theory and TA. The theory, based on which it becomes possible to build an automatic trading system with "analog" signals to buy/sell, i.e. at any given moment in time, building almost "live" adaptive automatic trading systems absolutely without any settings related to trading functions of the TS itself
Why does the deadlock in signals apply to all TA and not to specific lagged algorithms? Maybe you shouldn't generalise different things so much?
It's a pity it won't be. As for going beyond the scope of this thread - do you think its initiator will mind? :)
By the way, the first stages of your and Farnsworth's work are formally similar, I mean this:
-Preparing BP to get a distribution without fat tails.
-Delaying to a stationary form on the relative scales of the paternoster.Indeed. Reread Farnsworth's post. At first glance it may seem that we are talking about the same thing, just in different words/terms, perhaps it is the same thing, although I am not sure. I am referring to approaches in general.
Farnsworth:
to joo
There won't be a basic text (more detailed).
That's for nothing. One topic, maybe change your mind?
Well, as a matter of fact, I have already laid out the basic postulates. Porquoi not pas, that is, we can continue.
I have no special education in mathematics, all my research I do on the basis of intuition/insight, so I will be only glad if the TPP will take a complete, clear and, moreover, mathematically sound form. Mathematically based seems to be a more realistic possibility compared to the amorphous Dow Theory.
Why does signal deadweight apply to all TA and not specific lagged algorithms?
All indicators based on TA principles will be lagging, not just specific ones.
Maybe we shouldn't generalise different things so much?
I am not generalising but stating common knowledge facts.
OK, I'll post a table later comparing the main theses of TA and TPP. It will be clear what the fundamental differences between the theories are.
All indicators based on TA principles will be lagging, not just specific ones.
Is it bad or abnormal? Outperforming algorithms predicting the future - maybe that's just what sounds weird?