HiNot sure why i get error like this: Error in xts(forecasts,
dates[(window.length):length(returns)]) : order.by requires an appropriate
time-based object
I have researched for the answer that i need to convert returns into time
series. I did that but still it doesn't work?See attached for file.Thanks
library(quantmod)
library(timeSeries)
library(rugarch)
data = read.csv("EURJPY.m1440.csv", header=T)
names(data)
returns = diff(log(data$C))
returns[as.character(head(index(data)),1)]=0
window.length=1500
forecasts.length=length(returns)-window.length
forecasts <- vector(mode="numeric", length=forecasts.length)
directions <- vector(mode="numeric", length=forecasts.length)
p.val <- vector(mode="numeric", length=forecasts.length)
# loop through every trading day, estimate optimal model parameters from
rolling window
# and predict next day's return
for (i in 0:forecasts.length) {
roll.returns <- returns[(1+i):(window.length + i)] # create rolling window
final.aic <- Inf
final.order <- c(0,0,0)
# estimate optimal ARIMA model order
for (p in 0:5) for (q in 0:5) { # limit possible order to p,q <= 5
if (p == 0 && q == 0) next # p and q can't both be zero
arimaFit <- tryCatch( arima(roll.returns, order = c(p,0,q)),
error = function( err ) FALSE,
warning = function( err ) FALSE )
if (!is.logical( arimaFit)) {
current.aic <- AIC(arimaFit)
if (current.aic < final.aic) { # retain order if AIC is reduced
final.aic <- current.aic
final.order <- c(p,0,q)
final.arima <- arima(roll.returns, order = final.order)
}
}
else next
}
# specify and fit the GARCH model
spec <- ugarchspec(variance.model = list(model = "sGARCH",garchOrder=c(1,1)),
mean.model = list(
armaOrder = c(final.order[1], final.order[3]), arfima =
FALSE, include.mean = TRUE),
distribution.model = "sged")
fit = tryCatch(ugarchfit(spec, roll.returns, solver = 'hybrid'), error =
function(e) e, warning = function(w) w)
# calculate next day prediction from fitted mode
# model does not always converge - assign value of 0 to prediction and p.val
in this case
if (is(fit, "warning")) {
forecasts[i+1] <- 0
print(0)
p.val[i+1] <- 0
}
else {
next.day.fore = ugarchforecast(fit, n.ahead = 1)
x = next.day.fore@forecast$seriesFor
directions[i+1] <- ifelse(x[1] > 0, 1, -1) # directional prediction only
forecasts[i+1] <- x[1] # actual value of forecast
print(forecasts[i])
# analysis of residuals
resid <- as.numeric(residuals(fit, standardize = TRUE))
ljung.box <- Box.test(resid, lag = 20, type = "Ljung-Box", fitdf = 0)
p.val[i+1] <- ljung.box$p.value
}
}
dates <- data[,1]
forecasts.ts <- xts(forecasts, dates[(window.length):length(returns)])
# create lagged series of forecasts and sign of forecast
ag.forecasts <- Lag(forecasts.ts, 1)
ag.direction <- ifelse(ag.forecasts > 0, 1, ifelse(ag.forecasts < 0, -1, 0))
# Create the ARIMA/GARCH returns for the directional system
ag.direction.returns <- ag.direction * returns[(window.length):length(returns)]
ag.direction.returns[1] <- 0 # remove NA
# Create the backtests for ARIMA/GARCH and Buy & Hold
ag.curve <- log( cumprod( 1 + ag.direction.returns) )
buy.hold.ts <- xts(returns[(window.length):length(returns)],
dates[(window.length):length(returns)])
buy.hold.curve <- log(cumprod(1 + buy.hold.ts))
both.curves <- cbind(ag.curve, buy.hold.curve)
names(both.curves) <- c("Strategy returns", "Buy and hold returns")
# plot both curves together
myColors <- c( "darkorange", "blue")
plot(x = both.curves[,"Strategy returns"], xlab = "Time", ylab = "Cumulative
Return",
main = "Cumulative Returns", ylim = c(-0.25, 0.4), major.ticks= "quarters",
minor.ticks = FALSE, col = "darkorange")
lines(x = both.curves[,"Buy and hold returns"], col = "blue")
legend(x = 'bottomleft', legend = c("Strategy", "B&H"),
lty = 1, col = myColors)
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