scripts/quantstart.R

In 3schwartz/forecast:

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

# Obtain the S&P500 returns and truncate the NA value
getSymbols("^GSPC", from="1950-01-01")
spReturns = diff(log(Cl(GSPC)))
spReturns[as.character(head(index(Cl(GSPC)),1))] = 0

# Create the forecasts vector to store the predictions
windowLength = 500
foreLength = length(spReturns) - windowLength
forecasts <- vector(mode="character", length=foreLength)
forenum <- numeric(foreLength)
foreDate <- character(foreLength)

for (d in 0: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 ) FALSE,
                         warning=function( err ) 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)
      }
    } else {
      next
    }
  }

  # 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=",")
    forenum[d+1] <- 1
    foreDate[d+1] <- as.character(index(spReturnsOffset[windowLength]))

    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=",")
    forenum[d+1] <- ifelse(ind[1] < 0, -1, 1)
    foreDate[d+1] <- colnames(ind)

    print(paste(colnames(ind), ifelse(ind[1] < 0, -1, 1), sep=","))
  }
}

# Output the CSV file to "forecasts.csv"
write.csv(forecasts, file="forecasts.csv", row.names=FALSE)

forexts <- xts::xts(forenum[1:(length(forenum)-1)],
                    order.by = lubridate::as_date(foreDate[1:(length(foreDate)-1)]))

# Create the ARIMA+GARCH returns
spIntersect = merge(forexts, spReturns, all = FALSE)
spArimaGarchReturns = spIntersect[,1] * spIntersect[,2]

# Create the backtests for ARIMA+GARCH and Buy & Hold
spArimaGarchCurve = log( cumprod( 1 + spArimaGarchReturns ) )
spBuyHoldCurve = log( cumprod( 1 + spIntersect[,2] ) )
spCombinedCurve = merge( spArimaGarchCurve, spBuyHoldCurve, all=F )

# Plot the equity curves
xyplot(
  spCombinedCurve,
  superpose=T,
  col=c("darkred", "darkblue"),
  lwd=2,
  key=list(
    text=list(
      c("ARIMA+GARCH", "Buy & Hold")
    ),
    lines=list(
      lwd=2, col=c("darkred", "darkblue")
    )
  )
)