# A Closer Update To David Varadi’s Percentile Channels Strategy

So thanks to seeing Michael Kapler’s implementation of David Varadi’s percentile channels strategy, I was able to get a better understanding of what was going on. It turns out that rather than looking at the channel value only at the ends of months, that the strategy actually keeps track of the channel’s value intra-month. So if in the middle of the month, you had a sell signal and at the end of the month, the price moved up to intra-channel values, you would still be on a sell signal rather than the previous month’s end-of-month signal. It’s not much different than my previous implementation when all is said and done (slightly higher Sharpe, slightly lower returns and drawdowns). In any case, the concept remains the same.

For this implementation, I’m going to use the runquantile function from the caTools package, which contains a function called runquantile that works like a generalized runMedian/runMin/runMax from TTR, once you’re able to give it the proper arguments (on default, its results are questionable).

Here’s the code:

```require(quantmod)
require(caTools)
require(PerformanceAnalytics)
require(TTR)
getSymbols(c("LQD", "DBC", "VTI", "ICF", "SHY"), from="1990-01-01")

prices <- prices[!is.na(prices[,2]),]
returns <- Return.calculate(prices)
cashPrices <- prices[, 5]
assetPrices <- prices[, -5]

require(caTools)
pctChannelPosition <- function(prices,
dayLookback = 60,
lowerPct = .25, upperPct = .75) {

upperChannels <- runquantile(prices, k=dayLookback, probs=upperPct, endrule="trim")

lowerChannels <- runquantile(prices, k=dayLookback, probs=lowerPct, endrule="trim")

positions <- xts(matrix(nrow=nrow(prices), ncol=ncol(prices), NA), order.by=index(prices))
positions[prices > upperQ & lag(prices) < upperQ] <- 1 #cross up
positions[prices < lowerQ & lag(prices) > lowerQ] <- -1 #cross down
positions <- na.locf(positions)
positions[is.na(positions)] <- 0

colnames(positions) <- colnames(prices)
return(positions)
}

#find our positions, add them up
d60 <- pctChannelPosition(assetPrices)
d120 <- pctChannelPosition(assetPrices, dayLookback = 120)
d180 <- pctChannelPosition(assetPrices, dayLookback = 180)
d252 <- pctChannelPosition(assetPrices, dayLookback = 252)
compositePosition <- (d60 + d120 + d180 + d252)/4

compositeMonths <- compositePosition[endpoints(compositePosition, on="months"),]

returns <- Return.calculate(prices)
monthlySD20 <- xts(sapply(returns[,-5], runSD, n=20), order.by=index(prices))[index(compositeMonths),]
weight <- compositeMonths*1/monthlySD20
weight <- abs(weight)/rowSums(abs(weight))
weight[compositeMonths < 0 | is.na(weight)] <- 0
weight\$CASH <- 1-rowSums(weight)

#not actually equal weight--more like composite weight, going with
#Michael Kapler's terminology here
ewWeight <- abs(compositeMonths)/rowSums(abs(compositeMonths))
ewWeight[compositeMonths < 0 | is.na(ewWeight)] <- 0
ewWeight\$CASH <- 1-rowSums(ewWeight)

rpRets <- Return.portfolio(R = returns, weights = weight)
ewRets <- Return.portfolio(R = returns, weights = ewWeight)
```

Essentially, with runquantile, you need to give it the “trim” argument, and then manually append the leading NAs, and then manually turn it into an xts object, which is annoying. One would think that the author of this package would take care of these quality-of-life issues, but no. In any case, there are two strategies at play here–one being the percentile channel risk parity strategy, and the other what Michael Kapler calls “channel equal weight”, which actually *isn’t* an equal weight strategy, since the composite parameter values may take the values (-1, -.5, 0, .5, and 1–with a possibility for .75 or .25 early on when some of the lookback channels still say 0 instead of only 1 or -1), but simply, the weights without taking into account volatility at all, but I’m sticking with Michael Kapler’s terminology to be consistent. That said, I don’t personally use Michael Kapler’s SIT package due to the vast differences in syntax between it and the usual R code I’m used to. However, your mileage may vary.

In any case, here’s the updated performance:

```both <- cbind(rpRets, ewRets)
colnames(both) <- c("RiskParity", "Equal Weight")
charts.PerformanceSummary(both)
rbind(table.AnnualizedReturns(both), maxDrawdown(both))
apply.yearly(both, Return.cumulative)
```

Which gives us the following output:

```> rbind(table.AnnualizedReturns(both), maxDrawdown(both))
RiskParity Equal Weight
Annualized Return         0.09380000    0.1021000
Annualized Std Dev        0.06320000    0.0851000
Annualized Sharpe (Rf=0%) 1.48430000    1.1989000
Worst Drawdown            0.06894391    0.1150246

> apply.yearly(both, Return.cumulative)
RiskParity Equal Weight
2006-12-29 0.08352255   0.07678321
2007-12-31 0.05412147   0.06475540
2008-12-31 0.10663085   0.12212063
2009-12-31 0.11920721   0.19093131
2010-12-31 0.13756460   0.14594317
2011-12-30 0.11744706   0.08707801
2012-12-31 0.07730896   0.06085295
2013-12-31 0.06733187   0.08174173
2014-12-31 0.06435030   0.07357458
2015-02-17 0.01428705   0.01568372
```

In short, the more naive weighting scheme delivers slightly higher returns but pays dearly for those marginal returns with downside risk.

Here are the equity curves:

So, there you have it. The results David Varadi obtained are legitimate. But nevertheless, I hope this demonstrates how easy it is for the small details to make material differences.