(This article was first published on

[Update: I have updated this so the number of days used for standard deviation can be passed as a parameter, you can find the code at Trading Mean Reversion with Augen Spikes ]**Shifting sands**, and kindly contributed to R-bloggers)Jeff Augen has written many excellent books on options trading, including The Volatility Edge in Options Trading in which he presents a novel way of looking at a securities price movement as a function of its recent standard deviation.

I believe it's a very useful way at looking at price moves, so implemented the following which I believe matches as it was described in the book.

slideapply <- function(x, n, FUN=sd) {

v <- c(rep(NA, length(x)))

for (i in n:length(x) ) {

v[i] <- FUN(x[(i-n+1):i])

}

return(v)

}

augenSpike <- function(x, n=20) {

prchg <- c(NA, diff(x))

lgchg <- c(NA, diff(log(x)))

stdevlgchg <- slideapply(lgchg, n, sd)

stdpr <- x * stdevlgchg

#shuffle things up one

stdpr <- c(NA, stdpr[-length(stdpr)])

spike <- prchg / stdpr

return(spike)

}

An example of how to use it with quantmod:

getSymbols('SPY')

sp <- SPY['2010/2011']

asp <- augenSpike(as.vector(Cl(sp)))

sp$spike <- asp

barplot(sp['2011']$spike, main="Augen Price Spike SPY 2011", xlab="Time Daily", ylab="Price Spike in Std Dev")

Which gives the following chart

If you want to verify it has been implemented correctly (and I won't hold it against you), I used the following which is based on the example data he gave in the book. You will need the slideapply function from above which will apply a function to a subset of a vector along a sliding window.

aub <- data.frame(c(47.58, 47.78, 48.09, 47.52, 48.47, 48.38, 49.30, 49.61, 50.03, 51.65, 51.65, 51.57, 50.60, 50.45, 50.83, 51.08, 51.26, 50.89, 50.51, 51.42, 52.09, 55.83, 55.79, 56.20))

colnames(aub) <- c('Close')

aub$PriceChg <- c(NA, diff(aub$Close))

aub$LnChg <- ROC(aub$Close)

aub$StDevLgChg<-slideapply(aub$LnChg, 20, sd)

aub$StdDevPr <- aub$Close * aub$StDevLgChg

pr <- aub$StdDevPr

pr <- c(NA, pr[-length(pr)])

aub$Spike <- aub$PriceChg / pr

aub

Which for me at least gives the same data as printed. Let me know if you find it useful or find any errors.

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