Here’s a quick R implementation of David Varadi’s alternative to the RSI(2).  Michael Stokes over at the MarketSci blog has three great posts exploring this indicator:

Here’s the R code:

``````DV <- function(HLC, n=2, bounded=FALSE) {
# "HLC" is an _xts_ object with "High", "Low", and "Close"
# columns, in that order.

# This is David Varadi's alternative to the RSI(2).  Calculations
# taken from the marketsci blog -- http://marketsci.wordpress.com
# Author of this implementation: Joshua Ulrich

# Calculate each day's high/low mean
hlMean <- rowMeans( HLC[,-3] )

# Calculate the running Mean of the Close divided by the
# high/low mean, then subtract 1.
res <- runMean( HLC[,3] / hlMean, n ) - 1

# If we want the bounded DV...
if(bounded) {
# Set the range to calculated the bounded DV
rng <- 252:NROW(res)

# Grab the index of the unbounded results, so we can convert
# the bounded results back to an xts object.
indx <- index(res)

# A simple percent rank function hack
pctRank <- function(x,i) match(x[i], sort(coredata(x[(i-251):i])))

# Apply the percent rank function to the coredata of our results
res <- sapply(rng, function(i) pctRank(res, i) / 252)

# Convert the bounded results to xts
res <- xts(c(rep(NA,251),res), indx)
}

# Return results
return(res)
}
``````

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