Market phenomena - page 26
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Inertia does not work :)
Can you elaborate on the thought process?
For example: Work is the product of force by displacement. Inertia is not a force, so it cannot work. Therefore, inertia does not work :)
Gop-stop.))
And they legalised it! Good for the devils!)))
Can you elaborate on the thought process?
For example: Work is the product of force by displacement. Inertia is not a force, so it cannot work. Consequently, inertia does not work :)
The topicstarter's proof is a sure thing:
If it was working, then why don't I see you on the Forbes list? :))
Can you elaborate on the thought process?
For example: Work is the product of force by displacement. Inertia is not a force, so it cannot work. Consequently, inertia does not work :)
A stoic proof of the topicstarter:
If it worked, then why don't I see you on the Forbes list? :))
Inertia of thought. If it worked, I'd be drinking beer from two fridges and doing nothing :)
I wish I'd lived that way!
I'll put an order on a break of today's low. Maybe it will inertia to go another 100 pips.
I'll be damned!
I'll put an order on a break of today's low. Maybe it'll just go another 100 pips on inertia.
I wish I'd lived like this!
I'll put an order for a breakdown of today's low. Maybe it will inertia to go another 100 pips.
I wish I'd predicted the price like that! Paukas predicted another 60 pips! - That's the Phenomenon!
:)
I should have predicted the price like that! Paukas predicted another 60 pips! - There he is, the Phenomenon!
:)
I wanted to put in a few words.
I find the result very curious and unexpected. (If I understand correctly that the red line shows the cumulative BP of the summed differences not exceeding +- lambda?) Even very unexpected. The second thing that surprised me was the difference with the price data - very obvious. Although, I would ask what kind of distribution did you specify for the synthetic random numbers?
Yes, cumulative BP (for this example) . Again (used my post from another thread and modified it slightly):
Market model
After much searching, adopted this "control systems with random structure" thing as a working version of the market model. In my opinion (though not mathematics) - this model adequately describes the quoting process with all its subtleties.
Its essence is very simple. There is a finite number of structures that describe the transformation of input into output. Each such structure implies some model according to which the transformation takes place. The observed process is formed by a transition (switch) between the structures. All this is shown in the picture below:
Each model has a set of parameters, which can also change at each switching. So, I assumed that there are two main processes, each process has its own hierarchy, each element sitting at a node in the hierarchy has its own structure.
Process interactions
These two processes compete with each other according to the transition matrix (presumably), i.e. there is an "external" (conventionally of course) to the market some system that switches the generation of quotes between these processes. Later, I will show in more details, with reference to
Adaptation to practice.
Everything is great - but it's impossible to exactly identify such a system. Therefore, I introduce "combined model": A=W(1)MODEL1(parameters)+ W(2)MODEL2(parameters)+....+ W(n)MODELn(parameters). Where W(n) are some weights of participation of these models in prediction. It may be possible to explicitly partition the processes due to the invented transformation. But that's for later.
What am I working with?
I don't work directly with quotes - it's an extremely complicated process. I introduce all sorts of tricky transformations, but what I have said also applies to them. The complexity is not going anywhere - it is inherited. It is impossible to simplify the process. And if you do simplify it, you can lose the process itself. (i.e. even a little more complicated than I described, but I have shown the phenomenon and some more interesting observations)
Analysis of time series evolution
Basic stage. At this stage, I identify all possible structures by some criteria. I estimate statistics of transitions between these structures. I determine a transition frequency matrix for the structures. In the future, I am thinking to use the so-called impulse neural networks (or wave networks). It is a very promising direction.
Algorithm
(1) by making some assumptions about behavior, a probabilistic estimation of the future state of the system at the given moment on the planning horizon is performed. The neural network crawls through the resulting probability assessment matrix p=f(time,cotir) of the initial state and in turn makes an assumption about the presence of an entry/exit point. It can very accurately tell whether or not there will be an entry/exit on the planning horizon. All that remains is to find it.
(2) A command is given to build an accurate forecast. It is performed:
- identification of the "on" current structure
- assessment of the choice of the most probable future structures
- Identification of parameters of future models
(3) A simulation is run
(4) Then the neural network estimates coefficients of the combined model.
The second thing I wanted to say is about hypotheses of the author of this thread - Markovian transitions. I think some non-randomness can be found (if we stick to model with separation of increments inside lambda and outside lambda), because there is some autocorrelation of increments (taken modulo). And if to think about originally proposed model with troughs on the whole range of values, I don't know, we should try it.
Not randomness has already been found, proof of this is the extensive research by Alexey (Mathemat). I confirm them, everything is correct. But if the Markovianness is not respected, then everything will be much more complicated, we will have to reinvent it :o(