1. Load Data.R

#Load the dataset, adjust, and convert to monthly returns
set.seed(42)
library(quantmod)
getSymbols('^GSPC', from='1990-01-01')
GSPC <- adjustOHLC(GSPC, symbol.name='^GSPC')
GSPC <- to.monthly(GSPC, indexAt='lastof')
Target <- ClCl(GSPC)

2. Covar.R

#Calculate some technical indicators
periods <- c(3, 6, 9, 12)
Lags <- data.frame(lapply(c(1:2, periods), function(x) Lag(Target, x)))
EMAs <- data.frame(lapply(periods, function(x) {
  out <- EMA(Target, x)
  names(out) <- paste('EMA', x, sep='.')
  return(out)
}))
RSIs <- data.frame(lapply(periods, function(x) {
  out <- RSI(Cl(GSPC), x)
  names(out) <- paste('RSI', x, sep='.')
  return(out)
}))
DVIs <- data.frame(lapply(periods, function(x) {
  out <- DVI(Cl(GSPC), x)
  out <- out$dvi
  names(out) <- paste('DVI', x, sep='.')
  return(out)
}))
dat <- data.frame(Next(Target), Lags, EMAs, RSIs, DVIs)
dat <- na.omit(dat)

3. summary.R

#Create a summary function to calculate trade costs and cumulative profit in the test set
mySummary <- function (data, lev = NULL, model = NULL) {
  positions <- sign(data[, "pred"])
  trades <- abs(c(1,diff(positions)))
  profits <- positions*data[, "obs"] + trades*0.01
  profit <- prod(1+profits)
  names(profit) <- 'profit'
  return(profit)
}

4. Model.R

#Build the model!
library(caret)
model <- train(dat[,-1], dat[,1], method='rpart', 
               metric='profit', maximize=TRUE,
               trControl=trainControl(
                 method='timeslice',
                 initialWindow=12, fixedWindow=TRUE, 
                 horizon=12, summaryFunction=mySummary,
                 verboseIter=TRUE))
model