Machine learning in trading: theory, models, practice and algo-trading - page 196
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Take your time. Regardless of the clustering method. Do one pass. Trim a few observations. Do it again. Compare the results.
results. Work out a method for further action. Otherwise you'll make "grails", and then you'll wonder why it does not work))))
I was thinking of doing a total overshoot, if initially there were 500, in the end it may turn out that a good model will take into account only 5
and that's all I'll leave...
If you search for patterns like mytarmailS, by the sliding window on bars - each pattern will carry information about what interval the values on each bar can be in. The more patterns - the smaller interval will be allocated to each bar.....................
This is just my style of data input, good or bad, I dunno, I think it's the same as everyone else's...
But it has nothing to do with the approach I explained...
This approach allows you to clustering and extract the few percent of useful data that conventional MOEs can't.
You can take your predictors as you feed them into your network, cluster them (each one) and run them through the algorithm I described
Roughly speaking, for a particular window with new data to fall into some previously found pattern, it must fall into such veritcal constraints inherent in each pattern.
Well it depends on how much you want to teach the model, if you teach it weakly and don't do many clusters then figures in the model's head will be smoothed out and that defect you say theoretically should disappear...
Here's how the model sees a price series of 50 values that are packed into 49 clusters
code...
plot(price,t="l")
# делаем скользящее окно (50 знач) через матрицу хенкеля
hankel <- function(data, r=50) {
do.call(cbind,
lapply(0:(r-1),function(i) { data[(i+1):(length(data)-(r-1-i))]}))}
price <- hankel(price)
# скалирую, центрирую, в общем нормализирую каждую строчку в матрице
price <- t(apply(price,1,function(x){scale(x,center = T,scale = T)}))
# запускаем пакет с кластеризацыей, в дан. случ с кохоненом
# хорошый мануал
#https://cran.r-project.org/web/packages/SOMbrero/vignettes/doc-numericSOM.html
install.packages("SOMbrero")
library(SOMbrero)
# тренируем модель, с матрицей 7 х7 те 49 кластеров
model <- trainSOM(price, dimension=c(7,7), verbose=T)
plot(model,what="prototypes",type="lines",print.title=T)
Well, it depends on how much you want to train the model, if you train weakly, don't make many clusters, then the figures in the model's head will be quite smoothed and the drawback you say theoretically should disappear...
Here's how the model sees a price series of 50 values that are packed into 49 clusters
code...
plot(price,t="l")
# делаем скользящее окно (50 знач) через матрицу хенкеля
hankel <- function(data, r=50) {
do.call(cbind,
lapply(0:(r-1),function(i) { data[(i+1):(length(data)-(r-1-i))]}))}
price <- hankel(price)
# скалирую, центрирую, в общем нормализирую каждую строчку в матрице
price <- t(apply(price,1,function(x){scale(x,center = T,scale = T)}))
# запускаем пакет с кластеризацыей, в дан. случ с кохоненом
# хорошый мануал
#https://cran.r-project.org/web/packages/SOMbrero/vignettes/doc-numericSOM.html
install.packages("SOMbrero")
library(SOMbrero)
# тренируем модель, с матрицей 7 х7 те 49 кластеров
model <- trainSOM(price, dimension=c(7,7), verbose=T)
plot(model,what="prototypes",type="lines",print.title=T)
Here. I also did it long time ago. Kohonen is interesting. But remember, it is not correct to call it a clustering algorithm. It is a convolution algorithm. And clustering is then done on the resulting two-dimensional space...
Thank you ! I did not know.
But it is possible to use model exit as a cluster, isn't it?
[1] 7 7 7 7 7 7 7 7 6 6 6 5 5 4 4 3 3 2 2 1 1 1 1 1 8 15 15 22 22 29 36
[32] 43 43 43 43 43 43 43 44 44 45 45 45 46 46 46 46 47 47 47 47 48 48 48 49 42 35 35 28 28 21 21
[63] 21 21 21 21 21 21 21 21 21 14 14 14 7 7 7 7 7 7 7 6 5 4 3 3 2 1 1 1 1 1 1
[94] 8 8 15 22 22 29 36
Thank you ! I did not know.
But it is possible to use the exit from the model as a cluster, isn't it?
[1] 7 7 7 7 7 7 7 7 6 6 6 5 5 4 4 3 3 2 2 1 1 1 1 1 8 15 15 22 22 29 36
[32] 43 43 43 43 43 43 43 44 44 45 45 45 46 46 46 46 47 47 47 47 48 48 48 49 42 35 35 28 28 21 21
[63] 21 21 21 21 21 21 21 21 21 14 14 14 7 7 7 7 7 7 7 6 5 4 3 3 2 1 1 1 1 1 1
[94] 8 8 15 22 22 29 36
Yes, of course you can... It's essentially an aggregation of similar vectors in these cells.
It just may be this way (I did it myself at work and I know what I'm saying). I have n million entries. The vector of inputs is a couple of hundred long. I want to cluster it, but I don't know in advance how many clusters there are. Not every algorithm will be able to process such an array of data on an ordinary computer. Therefore, I first minimize the input space and make a grid of, for example, 50*50. It turns out 2500 typical representatives of the population... On such an array, my computer pulls agnes (hierarchical clustering). It builds a matrix of proximity of all to all...
And it turns out that I cluster these 2500 into, say, 10 clusters and the aggregation metric is good.
Yes, of course you can... There is essentially an aggregation of similar vectors in these cells.
It just may be this way (I did it myself at work and I know what I'm saying). I have n million entries. The vector of inputs is a couple of hundred long. I want to cluster it, but I don't know in advance how many clusters there are. Not every algorithm will be able to process such an array of data on an ordinary computer. Therefore, I first minimize the input space and make a grid of, for example, 50*50. It turns out 2500 typical representatives of the population... On such an array, my computer pulls agnes (hierarchical clustering). It builds a matrix of proximity of all to all...
And it turns out that I cluster these 2500 into, say, 10 clusters and the aggregation metric is good.
in the manualhttps://cran.r-project.org/web/packages/SOMbrero/vignettes/doc-numericSOM.html
in the section
Building super classes from the resulting SOM
exactly what you're talking about
For those who want to know what the market really is.
watch from old videos to new ones....
https://www.youtube.com/channel/UCsdK6s9i_I0NEMvis6wXhnQ/featured
it seems to be not difficult, but to program ? it's not possible for me yet...
I suggest to discuss how you can encode such approaches for MO or just encode
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