ARIMA.r

install.packages("quantmod")
install.packages("lattice")
install.packages("timeSeries")
install.packages("rugarch")

library(quantmod)
library(lattice)
library(timeSeries)
library(rugarch)

# Added
library(xts)

getSymbols("^GSPC", from="1950-01-01")
spReturns = diff(log(Cl(GSPC)))
spReturns[as.character(head(index(Cl(GSPC)),1))] = 0

windowLength = 500
foreLength = length(spReturns) - windowLength
forecasts <- vector(mode="character", length=foreLength)

ini = 0
for (d in ini:foreLength) {
  # Obtain the S&P500 rolling window for this day
  spReturnsOffset = spReturns[(1+d):(windowLength+d)]
  
  # Fit the ARIMA model
  final.aic <- Inf
  final.order <- c(0,0,0)
  for (p in 0:5) for (q in 0:5) {
    if ( p == 0 && q == 0) {
      next
    }
    
    arimaFit = tryCatch( arima(spReturnsOffset, order=c(p, 0, q)),
                         error=function( err ) {
                           message(err)
                           return(FALSE)
                         },
                         warning=function( err ) {
                           # message(err)
                           return(FALSE)
                         } )
    
    if( !is.logical( arimaFit ) ) {
      current.aic <- AIC(arimaFit)
      if (current.aic < final.aic) {
        final.aic <- current.aic
        final.order <- c(p, 0, q)
        # final.arima <- arima(spReturnsOffset, order=final.order)
        final.arima <- arimaFit
      }
    } else {
      next
    }
  }
  
  # test for the case we have not achieved a solution  
  if (final.order[1]==0 && final.order[3]==0) {
    final.order[1] = 1
    final.order[3] = 1
  }
  
  
  # Specify and fit the GARCH model
  spec = ugarchspec(
    variance.model=list(garchOrder=c(1,1)),
    mean.model=list(armaOrder=c(final.order[1], final.order[3]), include.mean=T),
    distribution.model="sged"
  )
  fit = tryCatch(
    ugarchfit(
      spec, spReturnsOffset, solver = 'hybrid'
    ), error=function(e) e, warning=function(w) w
  )
  
  # If the GARCH model does not converge, set the direction to "long" else
  # choose the correct forecast direction based on the returns prediction
  # Output the results to the screen and the forecasts vector
  if(is(fit, "warning")) {
    forecasts[d+1] = paste(index(spReturnsOffset[windowLength]), 1, sep=",")
    print(paste(index(spReturnsOffset[windowLength]), 1, sep=","))
  } else {
    fore = ugarchforecast(fit, n.ahead=1)
    ind = fore@forecast$seriesFor
    forecasts[d+1] = paste(colnames(ind), ifelse(ind[1] < 0, -1, 1), sep=",")
    print(paste(colnames(ind), ifelse(ind[1] < 0, -1, 1), sep=",")) 
  }
}
write.csv(forecasts, file="/forecasts_test.csv", row.names=FALSE)