Another Failed Volatility Histeresis: Ehlers’s Own Idea

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 settingstradeSize <- 100000initEq <- 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)#parametersFC = 1SC = 300n = 20triggerLag = 1nBands = 10bandFrac=10entryThreshPctB=1exitThreshPctB=.5period=10pctATR=.06#indicatorsadd.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")#signalsadd.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")#rulesadd.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 strategyt1 <- Sys.time()out <- applyStrategy(strategy=strategy.st,portfolios=portfolio.st)t2 <- Sys.time()print(t2-t1)#set up analyticsupdatePortf(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      1Q1       2Med      6Mean     9Q3      14Max     65WMin     1WQ1      3WMed    13WMean   13WQ3     19WMax    65LMin     1LQ1      2LMed     4LMean    6LQ3      8LMax    57mean(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.