Analyzing Bitcoin in R - Part 8 - Calculating and plotting statistics, rolling windows

# rolling stuff
sdannual <- rollapply(dailyret, width = 250, FUN = sd.annualized)
sharpeannual <- rollapply(dailyret, width = 250, FUN = SharpeRatio.annualized)

meandaily <- rollapply(dailyret, width = 250, FUN = mean)
sddaily <- rollapply(dailyret, width = 250, FUN = sd)

cormeansd <- rollapply(data.frame(meandaily, sddaily), 250, function(x) cor(x[,1], x[,2]), by.column = FALSE)

plotdata <- merge(meandaily, sddaily, as.matrix(c(rep(NA, 249), cormeansd)))
colnames(plotdata) <- c("Mean", "Std.Dev.", "Correlation")

# plot with horizontal long run mean
png('btc_plot_daily_rolling1.png')
par(mfrow = c(3,1))
par(mar = c(3, 2, 3, 2))
plot(plotdata$Mean)
abline(h = mean(meandaily, na.rm = TRUE), col = 2, lty = 3)
plot(plotdata$Std.Dev.)
abline(h = mean(sddaily, na.rm = TRUE), col = 2, lty = 3)
plot(plotdata$Correlation)
abline(h = mean(cormeansd, na.rm = TRUE), col = 2, lty = 3)
dev.off()

png('btc_plot_daily_rolling2.png')
par(mfrow = c(3,1))
par(mar = c(4, 4, 4, 2))
plot(x = as.vector(plotdata$Mean), y = as.vector(plotdata$Std.Dev.), pch=20)
plot(x = as.vector(plotdata$Mean), y = as.vector(plotdata$Correlation), pch=20)
plot(x = as.vector(plotdata$Std.Dev.), y = as.vector(plotdata$Correlation), pch=20)
dev.off()

T <- nrow(dailyret)
png('btc_plot_daily_lagscatter.png')
plot(x = as.vector(dailyret[1:(T-1)]), y = as.vector(dailyret[2:T]), pch = 20)
dev.off()

png('btc_plot_daily_acf.png')
par(mfrow = c(2,1))
acf(dailyret, na.action = na.pass)
pacf(dailyret, na.action = na.pass)
dev.off()