**QuantStrat TradeR » R**, and kindly contributed to R-bloggers)

This week, I attempted to use Ehlers’s own idea from this presentation.

Essentially, the idea is that when an indicator is flat, line crossings can produce whipsaws, so add a fraction of the daily range to the lagged indicator, and see if the non-lagged indicator crosses the threshold. In this case, it’s an exponentially smoothed daily range that’s used to compute the bands. I ran this from 2012 through the present day at the time of this writing (July 14, 2014), as the original link goes through most of the 2000s. (Also, be sure you’re using my most up-to-date IKTrading package, as I updated the quandClean function to deal with some intraday messy data issues that had gone unnoticed before.)

The settings I used were John Ehlers’s original settings — that is, a 20 day analysis period, a 10 day exponential band smoothing (that is, the band is computed as .1*(high-low)+.9*band), entered upon the percent B (that is, the current FRAMA minus the low band over the difference of the bands), and the fraction is 1/10th of the daily range.

Here’s the indicator used:

FRAMAbands <- function(HLC, n=126, FC=1, SC=300, nBands=n/2, bandFrac=10, ...) { frama <- FRAMA(HLC, n=n, FC=FC, SC=SC, ...) band <- Hi(HLC) - Lo(HLC) band <- xts(filter(1/nBands*band, 1-1/nBands, method="recursive"), order.by=index(frama)) bandUp <- frama$trigger + band/bandFrac bandDn <- frama$trigger - band/bandFrac pctB <- (frama$FRAMA-bandDn)/(bandUp-bandDn) out <- cbind(frama, pctB) colnames(out) <- c("FRAMA", "trigger", "pctB") return(out) }

And here’s the strategy code:

source("futuresData.R") #trade sizing and initial equity settings tradeSize <- 100000 initEq <- tradeSize*length(symbols) strategy.st <- portfolio.st <- account.st <- "FRAMA_BANDS_I" rm.strat(portfolio.st) rm.strat(strategy.st) initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD') initAcct(account.st, portfolios=portfolio.st, initDate=initDate, currency='USD',initEq=initEq) initOrders(portfolio.st, initDate=initDate) strategy(strategy.st, store=TRUE) #parameters FC = 1 SC = 300 n = 20 triggerLag = 1 nBands = 10 bandFrac=10 entryThreshPctB=1 exitThreshPctB=.5 period=10 pctATR=.06 #indicators add.indicator(strategy.st, name="FRAMAbands", arguments=list(HLC=quote(HLC(mktdata)), FC=FC, SC=SC, n=n, triggerLag=triggerLag, nBands=nBands, bandFrac=bandFrac), label="Fbands") add.indicator(strategy.st, name="lagATR", arguments=list(HLC=quote(HLC(mktdata)), n=period), label="atrX") #signals add.signal(strategy.st, name="sigThreshold", arguments=list(column="pctB.Fbands", threshold=entryThreshPctB, relationship="gt", cross=TRUE), label="longEntry") add.signal(strategy.st, name="sigThreshold", arguments=list(column="pctB.Fbands", threshold=exitThreshPctB, relationship="lt", cross=TRUE), label="longExit") #rules add.rule(strategy.st, name="ruleSignal", arguments=list(sigcol="longEntry", sigval=TRUE, ordertype="market", orderside="long", replace=FALSE, prefer="Open", osFUN=osDollarATR, tradeSize=tradeSize, pctATR=pctATR, atrMod="X"), type="enter", path.dep=TRUE) add.rule(strategy.st, name="ruleSignal", arguments=list(sigcol="longExit", sigval=TRUE, orderqty="all", ordertype="market", orderside="long", replace=FALSE, prefer="Open"), type="exit", path.dep=TRUE) #apply strategy t1 <- Sys.time() out <- applyStrategy(strategy=strategy.st,portfolios=portfolio.st) t2 <- Sys.time() print(t2-t1) #set up analytics updatePortf(portfolio.st) dateRange <- time(getPortfolio(portfolio.st)$summary)[-1] updateAcct(portfolio.st,dateRange) updateEndEq(account.st)

Here are the results:

> (aggPF <- sum(tStats$Gross.Profits)/-sum(tStats$Gross.Losses)) [1] 0.956477 > (aggCorrect <- mean(tStats$Percent.Positive)) [1] 36.39737 > (numTrades <- sum(tStats$Num.Trades)) [1] 1778 > (meanAvgWLR <- mean(tStats$Avg.WinLoss.Ratio[tStats$Avg.WinLoss.Ratio < Inf], na.rm=TRUE)) [1] 1.678421 > print(t(durStats)) [,1] Min 1 Q1 2 Med 6 Mean 9 Q3 14 Max 65 WMin 1 WQ1 3 WMed 13 WMean 13 WQ3 19 WMax 65 LMin 1 LQ1 2 LMed 4 LMean 6 LQ3 8 LMax 57 mean(corMeans) [1] 0.08232023 > SharpeRatio.annualized(portfRets) [,1] Annualized Sharpe Ratio (Rf=0%) -0.2476826 > Return.annualized(portfRets) [,1] Annualized Return -0.03485231 > maxDrawdown(portfRets) [1] 0.2632001

In short, it’s a loser over the past three years. Here’s the equity curve:

Now while it may have worked in the past (or something similar to it, using Ehlers’s filter indicator), it doesn’t seem to do so going forward.

I’ll leave this here for now as a demonstration of how to do Ehlers bands.

Thanks for reading.

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