As your example - if I understand it correctly - tells me RSI(3) is of no help as it will not distinct between 'good' (potential profit > ??) and 'bad' (potential profit < ??) but RSI(16) does.
But if so there has been an optimization as after that we know 16 is better than 3 - or from where do you know that?
Now do you train the NN with RSI(3)? It will be deleted probably. Or are you trying RSI(3) (NN-input 1) and RSI(16) (NN-input 2) and if RSI(3) will be deleted (NN-input 1 is set to 0 e.g.) RSI(x) has been optimized to 16 - even in very simple way. Do we need an NN for this having the MT-optimizer?
Or am I missing something in your example?
ok - so what is sent to the NN?
RSI(..) with a fix value (how did get it) with a variable value - can one optimize the calculation or not?
This all influences the danger of over adapting - therefore sorry being so nasty.
Hi Vladimir, I am very impressed with your article.
I managed to get it installed and tried with the various steps in R. I have some doubts about spatialSign transformation hopefully you can help me to understand.
I tried to learn the effect of preProcess with spatialSign, so I tried the following codes:
I get the following results:
I was very surprised with this result, intuitively, I would expect 1 and 2 should not be the same in spatialSign. I know it first center and scale then apply spatialSign, is the result correct?
fantastic article indeed even in these days.
but I got a question why my Kzz equals -Inf？
sig.zz<-ifelse(tail(dt[ , ncol(dt)], 500) == 0, 1, -1)
bal.zz<-cumsum(tail(price[ , 'CO'], 500) * sig.zz)
Kzz<-mean(bal.zz / bal)
Using Rsi could be the wrong approach.
Maybe its a better one to let it trade directly, like as if it needs to learn to walk, or play chess or any other game.
sig<-ifelse(pr.sae>mean(pr.sae), -1, 1)
sig.zz<-ifelse(y.ts == 0, 1,-1 )
bal<-cumsum(tail(price[ ,'CO'], bar) * sig)
bal.zz<-cumsum(tail(price[ ,'CO'], bar) * sig.zz)
sir, above code, when calc bal you havent move the sign backword like you did in the ariticle DEEP NEURAL NETDEEP NEURAL NETWORK WITH STACKED RBM.
sth do I miss?