Machine learning in trading: theory, models, practice and algo-trading - page 3428
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the marketplace ))))))
It's crooked like mine.
It's crooked, like mine.
Good.
That's a cool colab thing.
good
Got it.
From ticks in m1
weierstrass_mandelbrot <- function(t, D, gamma, phi, N) { sapply(t, \(x) { sum(sapply(-N:N, function(n) { ((1 - exp(1 i * gamma^n * x)) * exp(1 i * phi * n)) / (gamma^((2 - D) * n)) })) }) } t <- seq(0.1, 0.4, length.out = 10000) D <- 1.5 gamma <- 1.5 phi <- 6.1 # МЕНЯЙ ОТ 0 ДО 7 N <- 50 y <- Re(weierstrass_mandelbrot(t, D, gamma, phi, N)) par(mfrow=c(1,2), mar=c(2,2,2,2)) plot(t, y, type = "l", main = "Weierstrass-Mandelbrot Function", xlab = "t", ylab = "W(t)") y |> xts(Sys.time()+1:length(y)) |> to.minutes() |> chart_Series()Got it.
Okay. More
to get different curves
here also
t = np.linspace(0.1, 0.3, 100000) you get the number of cycles, that is, several fractals in a row of the same, with increasing period.
I set 0.3 or 0.4 - 3 or 4 repetitions. You can do more.
norms. More
to get different curves
here also
t = np.linspace(0.1, 0.3, 100000) you get the number of cycles, that is, several fractals in a row of the same, with increasing period.
I set 0.3 or 0.4 - 3 or 4 repetitions. You can do more.
Yeah, it's cool, but I don't know how this whole thing can help.
Yeah, it's cool, but I don't know how this whole thing can help.
I haven't done it yet.
1. generate charts, mark up, add to the main dataset.
2. train it, test it on the new data.
I haven't done it yet.
1. generate charts, mark them up, add them to the main dataset.
2. watch
you can go the other way round, you can teach the algorithm to process the input data itself - expand/shrink/narrowen/increase/compress, etc..
It will be more efficient than charging a 1000 GB dataset to see all the variants.
you can go the other way round, you can teach the algorithm to process the input data itself - expand/shrink/narrowen/eliminate/compress etc...
This will be more efficient than charging a 1000 GB dataset to see all the variants.
No. Again, there are patterns in this data. That's why you have to look for them. There's no point in narrowing and stretching the randoms.
No. Again, there are patterns in these data. That's why we need them. There is no point in narrowing and stretching the random.
You don't get it.
Okay, do it.