# Plotting overbought / oversold regions in R

**Thinking inside the box**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Classifying markets as overbought or oversold is a popular heuristic. It starts from computing a rolling smoothed estimate of the prices, usually via a (exponential or standard) moving average over a suitable number of days (where Bespoke uses 50 days, see here). This is typically coupled with a (simple) rolling standard deviation. Overbought and oversold regions are then constructed by taking the smoothed mean plus/minus one and two standard deviations.

Doing this is in R is pretty easy thanks to the combination of R’s rich base functions and its add-on packages from CRAN. Below is a simply function I wrote a couple of months ago—and I figured I might as well release. It relies on the powerful packages quantmod and TTR by my pals Josh Ulrich and Jeff Ryan, respectively.

## plotOBOS -- displaying overbough/oversold as eg in Bespoke's plots ## ## Copyright (C) 2010 - 2011 Dirk Eddelbuettel ## ## This is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 2 of the License, or ## (at your option) any later version. suppressMessages(library(quantmod)) # for getSymbols(), brings in xts too suppressMessages(library(TTR)) # for various moving averages plotOBOS <- function(symbol, n=50, type=c("sma", "ema", "zlema"), years=1, blue=TRUE) { today <- Sys.Date() X <- getSymbols(symbol, src="yahoo", from=format(today-365*years-2*n), auto.assign=FALSE) x <- X[,6] # use Adjusted type <- match.arg(type) xd <- switch(type, # compute xd as the central location via selected MA smoother sma = SMA(x,n), ema = EMA(x,n), zlema = ZLEMA(x,n)) xv <- runSD(x, n) # compute xv as the rolling volatility strt <- paste(format(today-365*years), "::", sep="") x <- x[strt] # subset plotting range using xts' nice functionality xd <- xd[strt] xv <- xv[strt] xyd <- xy.coords(.index(xd),xd[,1]) # xy coordinates for direct plot commands xyv <- xy.coords(.index(xv),xv[,1]) n <- length(xyd$x) xx <- xyd$x[c(1,1:n,n:1)] # for polygon(): from first point to last and back if (blue) { blues5 <- c("#EFF3FF", "#BDD7E7", "#6BAED6", "#3182BD", "#08519C") # cf brewer.pal(5, "Blues") fairlylight <- rgb(189/255, 215/255, 231/255, alpha=0.625) # aka blues5[2] verylight <- rgb(239/255, 243/255, 255/255, alpha=0.625) # aka blues5[1] dark <- rgb(8/255, 81/255, 156/255, alpha=0.625) # aka blues5[5] } else { fairlylight <- rgb(204/255, 204/255, 204/255, alpha=0.5) # grays with alpha-blending at 50% verylight <- rgb(242/255, 242/255, 242/255, alpha=0.5) dark <- 'black' } plot(x, ylim=range(range(xd+2*xv, xd-2*xv, na.rm=TRUE)), main=symbol, col=fairlylight) # basic xts plot polygon(x=xx, y=c(xyd$y[1]+xyv$y[1], xyd$y+2*xyv$y, rev(xyd$y+xyv$y)), border=NA, col=fairlylight) # upper polygon(x=xx, y=c(xyd$y[1]-1*xyv$y[1], xyd$y+1*xyv$y, rev(xyd$y-1*xyv$y)), border=NA, col=verylight)# center polygon(x=xx, y=c(xyd$y[1]-xyv$y[1], xyd$y-2*xyv$y, rev(xyd$y-xyv$y)), border=NA, col=fairlylight) # lower lines(xd, lwd=2, col=fairlylight) # central smooted location lines(x, lwd=3, col=dark) # actual price, thicker invisible(NULL) }

After downloading data and computing the rolling smoothed mean and standard deviation, it really is just a matter of plotting (appropriate) filled polygons. Here I used colors from the neat RColorBrewer package with some alpha blending. Colors can be turned off via an option to the function; ranges, data length and type of smoother can also be picked.

To call this in R, simply source the file and the call, say, `plotOBOS("^GSPC", years=2)`

which creates a two-year plot of the SP500 as shown here:

This shows the market did indeed bounce off the *oversold* lows nicely
on a few occassions in 2009 and 2010 --- but also continued to slide after
hitting the condition. Nothing is foolproof, and certainly nothing as simple
as this is, so *buyer beware*. But it may prove useful in conjunction
with other tools.

The code for the script is here and of course available under GPL 2 or later. I'd be happy to help incorporate it into some other finance package. Lastly, if you read this post this far, also consider our R / Finance conference coming at the end of April.

**leave a comment**for the author, please follow the link and comment on their blog:

**Thinking inside the box**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.