Whether there is a process whose analysis of one part does not allow predicting the next part. - page 6
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Unfortunately, any prediction can only rely on a deterministic component. On rows that do not have this component, any prediction, and consequently, earning becomes impossible.
This is exactly what I mean. Pseudo-random series are deterministic by definition.
The question (request?) is to learn how to quack this determinism with "external" ("not knowing" the generator's original algorithm) methods.
Moreover, it is possible to check the profitability on OutOfSample - continuation of the series of the same generator.
While quack with proof of profitability on OutOfSample, a continuation of the same generator series.
Unfortunately, any forecast can only rely on a deterministic component. On rows that do not have this component, any prediction, and hence earnings, becomes impossible.
Unfortunately, any forecast can only rely on a deterministic component
How the team views such considerations.
1. Prediction is possible if there is a deterministic component.
2. The deterministic component is differentiable not only on the left but also on the right at the last bar.
3. Differibility to the right (until the next bar arrives!) is provided by the type of the smoothing function. I saw somewhere that cubic splines at the junction remain differentiable.
No! Why should I?
That's exactly my point. Pseudorandom series are deterministic by definition.
The question (request?) is to learn to shake out this determinacy by "external" ("not knowing" the generator's initial algorithm) methods.
Moreover, to shake out with profitability confirmation on OutOfSample - continuation of the row of the same generator.
It is a question of all questions. Give me such a method and I will reverse the market. I am convinced that in order to work effectively with determinism one must first of all identify it. Any TS is essentially a method of deduction of this very component. But it is difficult to look for what I do not know what. Therefore, the overwhelming number of TCs are too inefficient. At best they only process a small fraction of conditioning, at worst, and they tend to take noise as input.
An effective method of dealing with chaotic deterministic series is invaluable. In principle it is possible to formulate properties which it should have and in the process of its construction be guided by this. As an example of the complexity and non-triviality of the problem I give the following graph:
Apart from the known positive mathematical expectation, there is no other stationary component in this random walk. That is, in fact, it is pure SB. There is no deterministic component here and our method must point to this: "What have you slipped me!? This is a SB! I refuse to work with such a series!"
No! Why should I?
Give me an example where the prediction does not rely on the determinism of the process.
The wrong coin. The process is non-deterministic. That is, a random row with a bevel.
Better yet, poker.
I think what was meant was trend trading. But it is not the only one.
I distinguish between two types of determinism, conventionally called trend and anti-trend. On both, the appropriate TS are used.