In DeepLearn.r you can find the following:
neural.train = function(model,XY)
XY <- as.matrix(XY)
X <- XY[,-ncol(XY)]
Y <- XY[,ncol(XY)]
Y <- ifelse(Y > 0,1,0)
Models[[model]] <<- sae.dnn.train(X,Y,
hidden = c(10,30,10),
activationfun = "tanh",
learningrate = 0.5,
momentum = 0.5,
learningrate_scale = 1.0,
output = "sigm",
sae_output = "linear",
numepochs = 100,
batchsize = 100,
hidden_dropout = 0,
visible_dropout = 0)
Start from here, Y <- ifelse(Y > 0,1,0) changes the target from trade returns to a 0/1 signal, you need to remove that...
Check sae.dnn.train, how can one do regression with it, test every step with a minimal example and data you know well...
for example - do sae.dnn.train regression on iris data
Find a metric, RMSE, MAE, Rsquared, read about them - use them - or write your own...
It is possible but it is a long way, i have done all that but i have connected the caret framework to zorro. Now i can do do both,
classification or regression with all caret models