The market is a controlled dynamic system. - page 111
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Yusuf, textbook.
Since my student days I know stability, AFC, Nyquist-Mikhailov, pi=3.14, transfer function and many other notions, I just need to refresh my memory. Professor Lapshenkov G. I. read to us TAU. http://www. mitht.ru/pages/66?id=47, at a stroke and in one breath he read lectures without cribs, which was unusual. People prepared for his lectures even at recess, opening their notebooks, because he began at once, without any introductory words. Back then, in the late sixties, one wondered why we needed all this; it turns out we did. I am convinced that the Soviet teaching programme was powerful.I don't know what happened to him.
That's good, of course... but it's kind of somewhere in the distance... Isn't it?
I used to study English, and I even remember some words and phrases... but it does not mean that I know English ;)
That's good, of course... but it's kind of a long way off... Isn't it?
I used to study English, and I even remember some words and phrases... but that doesn't mean that I know English ;)
Oleg, don't forget about the readers of the thread.
And they have long suffered because of the deviations from the canonical concepts.
Hence the lack of understanding and rejection by many.
The title of the branch because of the inaccurate wording even associate professors (experts in transients) do not understand, and what to say about the others ...
For a long time, since the start of the branch, I have been searching in the direction indicated in the title of the branch --- through misunderstanding, rejection and, at times, open hostility. At that time, I wasn't sure yet, but I had a hunch (based on some knowledge) that it was the right path. And the further I went, the more I became convinced of that. And as you know, the more we know, the better we know what we do not know. And ignorance had to be eliminated. And that again -- searching and testing and searching and testing again... It's a long process, and it goes on and on, and the results of the process take quite definite shape. During this time I have several times had the thought of correcting the first post by giving definitions, but was stopped by the absence of an opportunity to edit - apparently, this desire was not strong enough. But now I intend to rectify the situation, and as I've said before, I will put together a more or less expanded description to put in the first post of the thread, But there needs to be verified information, so this will take some time. Hopefully the current experiment won't prevent me from doing so.
.
For specialists the idea must be clear and understandable. Perhaps not at first or second glance. But in working order I will give the necessary explanations.
.
The system is non-linear, but at the first step, for convenience we shall represent the system as linear
dX(t) = A(t)*X(t) + B(t)*U(t)
Here, as usual, X is the state vector and U is the control vector. (all in the presence of noise)
X is a known process, say Close
U - unknown control action
Matrices A and B are to be defined.
The task: to determine U
This is the inverse problem of dynamics -- with its complexities, singularities, uncorrections, and other pleasures ;)
What does it give us in the end?
By solving the problem, i.e. defining the control U, we get a motion controller.
.
For example, for gold by Close values I have the following picture:
Thin line --- control U, motion set point Close.
.
The full picture is much more complicated:
.
The decision subsystem contributes to the overall complexity of the system
.
The work is ongoing. The research horizons have been pushed back even further. Conscious ignorance needs to be eliminated ;)
Figuratively it looks something like this:
.
For specialists, the idea should be clear and understandable. Perhaps not at first or second glance... But in working order I will give the necessary explanations.
.
The system is non-linear, but at the first step, for convenience we will present the system as linear
dX(t) = A(t)*X(t) + B(t)*U(t)
Here, as usual, X is the state vector and U is the control vector.
X is a known process, say Close
U - unknown control action
Matrices A and B are to be defined.
The task: to determine U
This is the inverse problem of dynamics - with its complexities, singularities, uncorrections and other pleasures ;)
What does it give us in the end?
By solving the problem, i.e. defining the control U, we get a motion controller.
.
For example, for gold by Close values I have the following picture:
Thin line --- control U, motion set point Close.
.
The full picture is much more complicated:
.
The decision subsystem contributes to the overall complexity of the system
.
The work is ongoing. The research horizons have been pushed back even further. Conscious ignorance needs to be addressed ;)
Suppose, by some miracle, we find out the nature of the function U. The problem is that modulo it may not be as large as you can imagine the volume and power of the market. That's what we have to think about now. Is it necessary to deal with a knowingly unsolvable problem? I suggest initially, deliberately, narrowing the scope of the problem to the capabilities of the real TC. But you know best. Perhaps I am misunderstanding something.
No. You're not.
The nature of the U function is informational. It is a direction setter for the development of the process. The volume and power of the market is a different story.
Nevertheless, you can apply this scheme to study the volume and power of the market, as well as any other process.
TAU studies controlled systems. There is an input signal, there is a control action and there is a transformed output signal. The task is to determine the control action at the desired outgoing signal.
You have an input and output signal - price and no transformation as a result of the control.
Or you have input signal - price and output signal - transformed price. It is a piece of cake to find a function that would give a transformed price of a given value based on the incoming price.
There is no controlled object here.
It's essentially a trivial forecasting problem - with constant coefficients or variables, linear or non-linear, etc.
It's like Yusuf would say his regression equations are TAU. He too takes the price and performs mathematical transformations in order to reduce prediction error.
Only he calls the "motion setter" the unknown regression coefficient.
TAU studies controlled systems. There is an input signal, there is a control action and there is a transformed output signal. The task is to determine the control action at the desired outgoing signal.
You have an input and output signal - price and no transformation as a result of the control.
Or you have input signal - price and output signal - transformed price. It is a piece of cake to find a function that would give a transformed price of a given value based on the incoming price.
There is no controlled object here.
It's essentially a trivial forecasting problem - with constant coefficients or variables, linear or non-linear, etc.
It's like Yusuf would say his regression equations are TAU. He too takes the price and performs mathematical transformations in order to reduce the prediction error.
Don't make such categorical statements about a subject with which you are, to put it mildly, very unfamiliar.