In DeepLearn.r you can find the following:
Code
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 wink

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.