library(dplyr)
library(tidyr)
getSymbols("^GSPC", from="1950-01-01")
spReturns = diff(log(Cl(GSPC)))
spReturns[as.character(head(index(Cl(GSPC)),1))] = 0
Returns <- spReturns["2016/"]
windowLength = 500
foreLength = length(Returns) - windowLength
forecasts <- vector(mode="character", length=foreLength)
forenum <- numeric(foreLength)
foreDate <- character(foreLength)
quantLoop <- function(){
for (d in 0:foreLength) {
# Obtain rolling window for this period
ReturnsOffset = Returns[(1+d):(windowLength+d)]
Order = forecast::auto.arima(ReturnsOffset) %>%
as.character() %>%
stringr::str_extract_all("(?<=\\().*(?=\\))") %>%
stringr::str_split_fixed(",", 3) %>%
as.numeric()
if(Order[2] !=0) {
for(i in 1:Order[2]) {
ReturnsOffset <- diff(ReturnsOffset)
ReturnsOffset[,1] <- 0
}
}
spec = rugarch::ugarchspec(
variance.model=list(garchOrder=c(1,1)),
mean.model=list(armaOrder=c(Order[1], Order[3]), include.mean=T),
distribution.model="sged"
)
fit = tryCatch(
rugarch::ugarchfit(
spec, ReturnsOffset, solver = 'hybrid'
), error=function(e) e, warning=function(w) w
)
if(is(fit, "warning")) {
forecasts[d+1] = paste(index(ReturnsOffset[windowLength]), 1, sep=",")
forenum[d+1] <- 1
foreDate[d+1] <- as.character(index(ReturnsOffset[windowLength]))
# print(paste(zoo::index(ReturnsOffset[windowLength]), 1, sep=","))
} else {
fore = rugarch::ugarchforecast(fit, n.ahead=1)
ind = fore@forecast$seriesFor
forecasts[d+1] = paste(colnames(ind), ifelse(ind[1] < 0, -1, 1), sep=",")
forenum[d+1] <- ifelse(ind[1] < 0, -1, 1)
foreDate[d+1] <- colnames(ind)
# print(paste(colnames(ind), ifelse(ind[1] < 0, -1, 1), sep=","))
}
}
write.csv(forecasts, file="./output/fore_Loop.csv", row.names=FALSE)
}
system.time(
quantLoop()
)
foreCsv <- readr::read_csv("./output/fore_Loop.csv")
fore_Loop <- foreCsv %>%
tidyr::separate(x, c("Date", "pos"), ",") %>%
dplyr::transmute(Date = lubridate::as_date(Date),
pos = as.numeric(pos))
for_Loopxts <- xts::xts(fore_Loop$pos[1:(nrow(fore_Loop)-1)],
order.by = fore_Loop$Date[1:(nrow(fore_Loop)-1)])
# Create the ARIMA+GARCH returns
Intersect_Loop = merge(for_Loopxts, Returns, all = FALSE)
ArimaGarchReturns_Loop = Intersect_Loop[,1] * Intersect_Loop[,2]
# Create the backtests for ARIMA+GARCH and Buy & Hold
ArimaGarchCurve_Loop = log( cumprod( 1 + ArimaGarchReturns_Loop ) )
BuyHoldCurve_Loop = log( cumprod( 1 + Intersect_Loop[,2] ) )
CombinedCurve_Loop = merge( ArimaGarchCurve_Loop, BuyHoldCurve_Loop, all=F )
# Plot the equity curves
lattice::xyplot(
CombinedCurve_Loop,
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")
)
)
)
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