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:

- Varadi’s RSI(2) Alternative: The DV(2)
- RSI(2) vs. DV(2)
- Last Couple of Notes on DV(2)

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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