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... "Maybe someone will come in handy and maybe someone will tell you how to do it better?"
As for wavelets - this is a scam. If we take any function, decompose it into a Fourier series and restore it, it falls under the definition of a wavelet with respect to the zero harmonic level, since the integral of the function histogram at this very level is 0. The wavelet operators only invent that their "inventions" supposedly contain more information than the Fourier transform. Fucking lobbyists are lying.
We should at least do a full optimization on all input parameters and analyze the resulting 6-dimensional surface.
As Zoshchenko said: I would be surprised if a lady put the half of her coat in a bucket with paint. And I would be surprised if a system that was designed for one thing was run on another and you could see the result.
My research has also shown that using time patterns is more effective. I just don't understand why we should input the changes of indicator values instead of the price. In the end there is pattern recognition by indicator values (which is often wrong), but not by price. I suppose that the use of the neural network is most effective when it is run through prices. If you believe in self-similarity of time series of quotes, you'd better use the smallest timeframe. Because the system will give more signals and there will be much more ineffective sets of weighting coefficients.
"Wavelets are a scam" is a bold statement when you consider the significant improvement in the compression ratio of some data when using them.
Why bother people with wavelets and other innovations when it is possible to use the same data in Fourier transform, cut off a part of harmonics with small amplitudes, reconstruct them relative to level 0 and obtain thereby what is called a wavelet?
A small digression: if quote changes are a completely random process, then it is not possible to create a profitable system (otherwise there is pseudo-randomness)