Thoughts on the random - page 19
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How is this signal line different from EMA or SMA?
The difference is that the EMA and SMA are too primitive to adequately describe the system behavior.
They are just a first and rough approximation. The market will quickly "calculate" this frozen structure of its (the market's) model and gobble up the deposit.
This explains the fact that the TS on machines requires frequent optimization, i.e. adjustment for the market.
We need (a worn-out truth) adaptation-autotuning within reasonable (acceptable from the risk point of view) time intervals.
There are two ways here - either periodically change the signal model, or change the parameters of the flappers (conventionally speaking).
Where to get the error (deviation from the norm), how to select it and how to use it for autotuning is a matter of algorithm.
Remembering all the DSP supporters on this forum and on other trader forums (and there are plenty of them) I have a slogan "DSP with a hickey!"
People do not want to understand that there is no signal in quotes as they understand it, and there is no noise as they understand it.
There is a deterministic component in kotir (let's not consider SB with demolition) which they confuse with signal. One could agree with them (it's not about terminology after all, it's about money) if the difference between the deterministic component ("signal") and the quotient were stationary (almost constant MO and almost constant variance).
For an automaton:
And about the generally accepted. The first line is ARCH, more fully thick tails, the mathematical model for this is FARIMA (fractional integrability, Hurst is synonymous). This is not only a sea of literature, but also widely available ready-made, free code (R) which takes into account a lot of nuances in the named.
I wish you success, you automaton. I have confidence that a good adaptive filter will produce a fairly stable system, and the obligatory stops can keep it from going down, especially if you know exactly how your system reacts to the delta function (see avalsa above).
faa, you are such an expert, you don't talk, you talk ;))))
But here's the trouble, you're trying to "talk" about a topic you have absolutely no understanding of.
Is it possible to make a good filter based on a neural network (it will be a non-linear filter, but not the point)? I mean, it doesn't contradict the basics that you have expert knowledge about? (I haven't dealt with filters at all, so the question is very general.)
Is it possible to make a good filter based on a neural network (it will be a non-linear filter, but not the point)? I mean, it doesn't contradict the basics that you have expert knowledge about? (I haven't dealt with filters at all, so the question is very general.)
Although I have some knowledge of NS, I am not a NS expert.I suppose that making a good filter based on a neural network isa feasible task.
Although I have some knowledge of NS, I am not a NS expert.I suppose that making a good filter based on a neural network is a feasible task.
Thanks!
Can I ask you another question? Are you trying to reduce the size of the "filter-quote" error when building a filter or is it much more complicated than that?
Thank you!
Can I ask you another question? Are you trying to reduce the size of the "filter-quote" error when building the filter or is it more complicated than that?
The objective of the system is to minimise tracking error. But it is possible to set the task differently.
There is an example of a problem statementhere. (there are several posts before and after on the thread)
The task of the system is to minimise tracking error. But it is possible to set the task differently as well.
There is an example of a problem statementhere. (there are several before and after posts on the thread).
Great! I am studying it with great interest. Thank you.
Great! I am studying it with great interest. Thank you.
The difference is that the EMA and SMA are too primitive to adequately describe the system behaviour.
They are just a first and rough approximation. The market will quickly "calculate" this frozen structure of its (the market) model and gobble up the deposit.
This explains the fact that the TS on machines requires frequent optimization, i.e. adjustment for the market.
We need (a worn-out truth) adaptation-autotuning within reasonable (acceptable from the risk point of view) time intervals.
There are two ways here - either periodically change the signal model, or change the parameters of the flappers (conventionally speaking).
Where to get the error (deviation from the norm), how to select it and how to use it for autotuning is a matter of algorithm.
I've never seen adaptive filter that would differ from EMA, SMA or other FIR filter and be better than them. If you know what you are talking about, then show such adaptive filter on pictures and explain its advantage.