#include <r.h>
void main(int mode, int model, int numSignals, void* Data)
{
if(!wait(0)) return 0;
// open an R script with the same name as the stratefy script
if(mode == NEURAL_INIT) {
if(!Rstart(strf("%s.r",Script),2)) return 0;
Rx("neural.init()");
return 1;
}
// export batch training samples and call the R training function
if(mode == NEURAL_TRAIN) {
string name = strf("Data\\signals%i.csv",Core);
file_write(name,Data,0);
Rx(strf("XY <- read.csv('%s%s',header = F)",slash(ZorroFolder),slash(name)));
Rset("AlgoVar",AlgoVar,8);
if(!Rx(strf("neural.train(%i,XY)",model+1),2))
return 0;
return 1;
}
// predict the target with the R predict function
if(mode == NEURAL_PREDICT) {
Rset("AlgoVar",AlgoVar,8);
Rset("X",(double*)Data,numSignals);
Rx(strf("Y <- neural.predict(%i,X)",model+1));
return Rd("Y[1]");
}
// save all trained models
if(mode == NEURAL_SAVE) {
print(TO_ANY,"\nStore %s",strrchr(Data,'\\')+1);
return Rx(strf("neural.save('%s')",slash(Data)),2);
}
// load all trained models
if(mode == NEURAL_LOAD) {
printf("\nLoad %s",strrchr(Data,'\\')+1);
return Rx(strf("load('%s')",slash(Data)),2);
}
return 1;
}

library('deepnet', quietly = T)
library('caret', quietly = T)

# called by Zorro for training
neural.train = function(model,XY)
{
XY <- as.matrix(XY)
X <- XY[,-ncol(XY)] # predictors
Y <- XY[,ncol(XY)] # target
Y <- ifelse(Y > 0,1,0) # convert -1..1 to 0..1
Models[[model]] <<- sae.dnn.train(X,Y,
hidden = c(20,20,20),
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)
}

# called by Zorro for prediction
neural.predict = function(model,X)
{
if(is.vector(X)) X <- t(X) # transpose horizontal vector
return(nn.predict(Models[[model]],X))
}

# called by Zorro for saving the models
neural.save = function(name)
{
save(Models,file=name) # save trained models
}

# called by Zorro for initialization
neural.init = function()
{
set.seed(365)
Models <<- vector("list")
}

# quick OOS test for experimenting with the settings
Test = function()
{
neural.init()
XY <<- read.csv('C:/Project/Zorro/Data/signals0.csv',header = F)
splits <- nrow(XY)*0.8
XY.tr <<- head(XY,splits) # training set
XY.ts <<- tail(XY,-splits) # test set
neural.train(1,XY.tr)
X <<- XY.ts[,-ncol(XY.ts)]
Y <<- XY.ts[,ncol(XY.ts)]
Y.ob <<- ifelse(Y > 0,1,0)
Y <<- neural.predict(1,X)
Y.pr <<- ifelse(Y > 0.5,1,0)
confusionMatrix(Y.pr,Y.ob) # display prediction accuracy
}

You need to use an advise() function within the run() function. For example,


Where "Objective" is the target value you are trying to predict (in this case, positive or negative next-bar return designated as +1 and -1 respectively) and "Signal_1, Signal_2, ... etc" are the variables (otherwise known as features, predictors, independent variables etc) that you are using to forecast "Objective".

In terms of the way the script flows, the advise function calls the NEURAL function (the first function in your post). The NEURAL function then calls the R script (the second function in your post).

Does that help?

#include <r.h>

function run()
{
BarPeriod = 60;
WFOPeriod = 20*24; // 4 weeks
StartDate = 2010;
LookBack = 100;
set(RULES);
Cores = 4; // train parallel with 4 R instances

// generate 10 signals for prediction...
var Sig[10];
Sig[0] = ...
...

Detrend = TRADES;
ExitTime = 5; // 4 hours prediction horizon
var PredictLong = adviseLong(NEURAL+BALANCED,0, // predict next trade outcome
Sig[0],Sig[1],Sig[2],Sig[3],Sig[4],Sig[5],Sig[6],Sig[7],Sig[8],Sig[9]);
if(Train) enterLong(); // enter always a trade in training mode
var PredictShort = adviseShort();
if(Train) enterShort();

if(!Train) {
if(PredictLong > 0.6 && PredictShort < 0.4)
enterLong;
else if(PredictLong < 0.4 && PredictShort > 0.6)
enterShort;
}
}