Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

This post will demonstrate a stop-loss rule inspired by Andrew Lo’s paper “when do stop-loss rules stop losses”? Furthermore, it will demonstrate how to deflate a Sharpe ratio to account for the total number of trials conducted, which is presented in a paper written by David H. Bailey and Marcos Lopez De Prado. Lastly, the strategy will be tested on the out-of-sample ETFs, rather than the mutual funds that have been used up until now (which actually cannot be traded more than once every two months, but have been used simply for the purpose of demonstration).

First, however, I’d like to fix some code from the last post and append some results.

A reader asked about displaying the max drawdown for each of the previous rule-testing variants based off of volatility control, and Brian Peterson also recommended displaying max leverage, which this post will provide.

Here’s the updated rule backtest code:

```ruleBacktest <- function(returns, nMonths, dailyReturns,
nSD=126, volTarget = .1) {
nMonthAverage <- apply(returns, 2, runSum, n = nMonths)
nMonthAverage <- na.omit(xts(nMonthAverage, order.by = index(returns)))
nMonthAvgRank <- t(apply(nMonthAverage, 1, rank))
nMonthAvgRank <- xts(nMonthAvgRank, order.by=index(nMonthAverage))
selection <- (nMonthAvgRank==5) * 1 #select highest average performance
dailyBacktest <- Return.portfolio(R = dailyReturns, weights = selection)
constantVol <- volTarget/(runSD(dailyBacktest, n = nSD) * sqrt(252))
monthlyLeverage <- na.omit(constantVol[endpoints(constantVol), on ="months"])
wts <- cbind(monthlyLeverage, 1-monthlyLeverage)
constantVolComponents <- cbind(dailyBacktest, 0)
out <- Return.portfolio(R = constantVolComponents, weights = wts)
out <- apply.monthly(out, Return.cumulative)
maxLeverage <- max(monthlyLeverage, na.rm = TRUE)
return(list(out, maxLeverage))
}

t1 <- Sys.time()
allPermutations <- list()
allDDs <- list()
leverages <- list()
for(i in seq(21, 252, by = 21)) {
monthVariants <- list()
ddVariants <- list()
leverageVariants <- list()
for(j in 1:12) {
trial <- ruleBacktest(returns = monthRets, nMonths = j, dailyReturns = sample, nSD = i)
sharpe <- table.AnnualizedReturns(trial[[1]])[3,]
dd <- maxDrawdown(trial[[1]])
monthVariants[[j]] <- sharpe
ddVariants[[j]] <- dd
leverageVariants[[j]] <- trial[[2]]
}
allPermutations[[i]] <- do.call(c, monthVariants)
allDDs[[i]] <- do.call(c, ddVariants)
leverages[[i]] <- do.call(c, leverageVariants)
}
allPermutations <- do.call(rbind, allPermutations)
allDDs <- do.call(rbind, allDDs)
leverages <- do.call(rbind, leverages)
t2 <- Sys.time()
print(t2-t1)
```

Drawdowns:

Leverage:

Here are the results presented as a hypothesis test–a linear regression of drawdowns and leverage against momentum formation period and volatility calculation period:

```ddLM <- lm(meltedDDs\$MaxDD~meltedDDs\$volFormation + meltedDDs\$momentumFormation)
summary(ddLM)

Call:
lm(formula = meltedDDs\$MaxDD ~ meltedDDs\$volFormation + meltedDDs\$momentumFormation)

Residuals:
Min 1Q Median 3Q Max
-0.08022 -0.03434 -0.00135 0.02911 0.20077

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.240146 0.010922 21.99 < 2e-16 ***
meltedDDs\$volFormation -0.000484 0.000053 -9.13 6.5e-16 ***
meltedDDs\$momentumFormation 0.001533 0.001112 1.38 0.17
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0461 on 141 degrees of freedom
Multiple R-squared: 0.377, Adjusted R-squared: 0.368
F-statistic: 42.6 on 2 and 141 DF, p-value: 3.32e-15

levLM <- lm(meltedLeverage\$MaxLeverage~meltedLeverage\$volFormation + meltedDDs\$momentumFormation)
summary(levLM)

Call:
lm(formula = meltedLeverage\$MaxLeverage ~ meltedLeverage\$volFormation +
meltedDDs\$momentumFormation)

Residuals:
Min 1Q Median 3Q Max
-0.9592 -0.5179 -0.0908 0.3679 3.1022

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.076870 0.164243 24.82 <2e-16 ***
meltedLeverage\$volFormation -0.009916 0.000797 -12.45 <2e-16 ***
meltedDDs\$momentumFormation 0.009869 0.016727 0.59 0.56
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.693 on 141 degrees of freedom
Multiple R-squared: 0.524, Adjusted R-squared: 0.517
F-statistic: 77.7 on 2 and 141 DF, p-value: <2e-16
```

Easy interpretation here–the shorter-term volatility estimates are unstable due to the one-asset rotation nature of the system. Particularly silly is using the one-month volatility estimate. Imagine the system just switched from the lowest-volatility instrument to the highest. It would then take excessive leverage and get blown up that month for no particularly good reason. A longer-term volatility estimate seems to do much better for this system. So, while the Sharpe is generally improved, the results become far more palatable when using a more stable calculation for volatility, which sets maximum leverage to about 2 when targeting an annualized volatility of 10%. Also, to note, the period to compute volatility matters far more than the momentum formation period when addressing volatility targeting, which lends credence (at least in this case) to so many people that say “the individual signal rules matter far less than the position-sizing rules!”. According to some, position sizing is often a way for people to mask only marginally effective (read: bad) strategies with a separate layer to create a better result. I’m not sure which side of the debate (even assuming there is one) I fall upon, but for what it’s worth, there it is.

Moving on, I want to test out one more rule, which is inspired by Andrew Lo’s stop-loss rule. Essentially, the way it works is this (to my interpretation): it evaluates a running standard deviation, and if the drawdown exceeds some threshold of the running standard deviation, to sit out for some fixed period of time, and then re-enter. According to Andrew Lo, stop-losses help momentum strategies, so it seems as good a rule to test as any.

However, rather than test different permutations of the stop rule on all 144 prior combinations of volatility-adjusted configurations, I’m going to take an ensemble strategy, inspired by a conversation I had with Adam Butler, the CEO of ReSolve Asset Management, who stated that “we know momentum exists, but we don’t know the perfect way to measure it”, from the section I just finished up and use an equal weight of all 12 of the momentum formation periods with a 252-day rolling annualized volatility calculation, and equal weight them every month.

Here are the base case results from that trial (bringing our total to 169).

```strat <- list()
for(i in 1:12) {
strat[[i]] <- ruleBacktest(returns = monthRets, nMonths = i, dailyReturns = sample, nSD = 252)[[1]]
}
strat <- do.call(cbind, strat)
strat <- Return.portfolio(R = na.omit(strat), rebalance_on="months")

rbind(table.AnnualizedReturns(strat), maxDrawdown(strat), CalmarRatio(strat))
```

With the following result:

```portfolio.returns
Annualized Return 0.12230
Annualized Std Dev 0.10420
Annualized Sharpe (Rf=0%) 1.17340
Worst Drawdown 0.09616
Calmar Ratio 1.27167
```

Of course, also worth nothing is that the annualized standard deviation is indeed very close to 10%, even with the ensemble. And it’s nice that there is a Sharpe past 1. Of course, given that these are mutual funds being backtested, these results are optimistic due to the unrealistic execution assumptions (can’t trade sooner than once every *two* months).

Anyway, let’s introduce our stop-loss rule, inspired by Andrew Lo’s paper.

```loStopLoss <- function(returns, sdPeriod = 12, sdScaling = 1, sdThresh = 1.5, cooldown = 3) {
stratRets <- list()
count <- 1
stratComplete <- FALSE
originalRets <- returns
ddThresh <- -runSD(returns, n = sdPeriod) * sdThresh * sdScaling
while(!stratComplete) {
retDD <- PerformanceAnalytics:::Drawdowns(returns)
DDbreakthrough <- retDD < ddThresh & lag(retDD) > ddThresh
firstBreak <- which.max(DDbreakthrough) #first threshold breakthrough, if 1, we have no breakthrough
#the above line is unintuitive since this is a boolean vector, so it returns the first value of TRUE
if(firstBreak > 1) { #we have a drawdown breakthrough if this is true
stratRets[[count]] <- returns[1:firstBreak,] #subset returns through our threshold breakthrough
nextPoint <- firstBreak + cooldown + 1 #next point of re-entry is the point after the cooldown period
if(nextPoint <= (nrow(returns)-1)) { #if we can re-enter, subset the returns and return to top of loop
returns <- returns[nextPoint:nrow(returns),]
ddThresh <- ddThresh[nextPoint:nrow(ddThresh),]
count <- count+1
} else { #re-entry point is after data exhausted, end strategy
stratComplete <- TRUE
}
} else { #there are no more critical drawdown breakthroughs, end strategy
stratRets[[count]] <- returns
stratComplete <- TRUE
}
}
stratRets <- do.call(rbind, stratRets) #combine returns
expandRets <- cbind(stratRets, originalRets) #account for all the days we missed
expandRets[is.na(expandRets[,1]), 1] <- 0 #cash positions will be zero
rets <- expandRets[,1]
colnames(rets) <- paste(cooldown, sdThresh, sep="_")
return(rets)
}
```

Essentially, the way it works is like this: the function computes all the drawdowns for a return series, along with its running standard deviation (non-annualized–if you want to annualize it, change the sdScaling parameter to something like sqrt(12) for monthly or sqrt(252) for daily data). Next, it looks for when the drawdown crossed a critical threshold, then cuts off that portion of returns and standard deviation history, and moves ahead in history by the cooldown period specified, and repeats. Most of the code is simply dealing with corner cases (is there even a time to use the stop rule? What about iterating when there isn’t enough data left?), and then putting the results back together again.

In any case, for the sake of simplicity, this function doesn’t use two different time scales (IE compute volatility using daily data, make decisions monthly), so I’m sticking with using a 12-month rolling volatility, as opposed to 252 day rolling volatility multiplied by the square root of 21.

Finally, here are another 54 runs to see if Andrew Lo’s stop-loss rule works here. Essentially, the intuition behind this is that if the strategy breaks down, it’ll continue to break down, so it would be prudent to just turn it off for a little while.

Here are the trial runs:

```
threshVec <- seq(0, 2, by=.25)
cooldownVec <- c(1:6)
sharpes <- list()
params <- expand.grid(threshVec, cooldownVec)
for(i in 1:nrow(params)) {
configuration <- loStopLoss(returns = strat, sdThresh = params[i,1],
cooldown = params[i, 2])
sharpes[[i]] <- table.AnnualizedReturns(configuration)[3,]
}
sharpes <- do.call(c, sharpes)

loStoplossFrame <- cbind(params, sharpes)
loStoplossFrame\$improvement <- loStoplossFrame[,3] - table.AnnualizedReturns(strat)[3,]

colnames(loStoplossFrame) <- c("Threshold", "Cooldown", "Sharpe", "Improvement")
```

And a plot of the results.

```ggplot(loStoplossFrame, aes(x = Threshold, y = Cooldown, fill=Improvement)) +
geom_tile()+scale_fill_gradient2(high="green", mid="yellow", low="red", midpoint = 0)
```

Result:

Result: at this level, and at this frequency (retaining the monthly decision-making process), the stop-loss rule basically does nothing in order to improve the risk-reward trade-off in the best case scenarios, and in most scenarios, simply hurts. 54 trials down the drain, bringing us up to 223 trials. So, what does the final result look like?

```charts.PerformanceSummary(strat)
```

Here’s the final in-sample equity curve–and the first one featured in this entire series. This is, of course, a *feature* of hypothesis-driven development. Playing whack-a-mole with equity curve bumps is what is a textbook case of overfitting. So, without further ado:

And now we can see why stop-loss rules generally didn’t add any value to this strategy. Simply, it had very few periods of sustained losses at the monthly frequency, and thus, very little opportunity for a stop-loss rule to add value. Sure, the occasional negative month crept in, but there was no period of sustained losses. Furthermore, Yahoo Finance may not have perfect fidelity on dividends on mutual funds from the late 90s to early 2000s, so the initial flat performance may also be a rather conservative estimate on the strategy’s performance (then again, as I stated before, using mutual funds themselves is optimistic given the unrealistic execution assumptions, so maybe it cancels out). Now, if this equity curve were to be presented without any context, one may easily question whether or not it was curve-fit. To an extent, one can argue that the volatility computation period may be optimized, though I’d hardly call a 252-day (one-year) rolling volatility estimate a curve-fit.

Next, I’d like to introduce another concept on this blog that I’ve seen colloquially addressed in other parts of the quantitative blogging space, particularly by Mike Harris of Price Action Lab, namely that of multiple hypothesis testing, and about the need to correct for that.

Luckily for that, Drs. David H. Bailey and Marcos Lopez De Prado wrote a paper to address just that. Also, I’d like to note one very cool thing about this paper: it actually has a worked-out numerical example! In my opinion, there are very few things as helpful as showing a simple result that transforms a collection of mathematical symbols into a result to demonstrate what those symbols actually mean in the span of one page. Oh, and it also includes *code* in the appendix (albeit Python — even though, you know, R is far more developed. If someone can get Marcos Lopez De Prado to switch to R–aka the better research language, that’d be a godsend!).

In any case, here’s the formula for the deflated Sharpe ratio, implemented straight from the paper.

```deflatedSharpe <- function(sharpe, nTrials, varTrials, skew, kurt, numPeriods, periodsInYear) {
emc <- .5772
sr0_term1 <- (1 - emc) * qnorm(1 - 1/nTrials)
sr0_term2 <- emc * qnorm(1 - 1/nTrials * exp(-1))
sr0 <- sqrt(varTrials * 1/periodsInYear) * (sr0_term1 + sr0_term2)

numerator <- (sharpe/sqrt(periodsInYear) - sr0)*sqrt(numPeriods - 1)

skewnessTerm <- 1 - skew * sharpe/sqrt(periodsInYear)
kurtosisTerm <- (kurt-1)/4*(sharpe/sqrt(periodsInYear))^2

denominator <- sqrt(skewnessTerm + kurtosisTerm)

result <- pnorm(numerator/denominator)
pval <- 1-result
return(pval)
}
```

The inputs are the strategy’s Sharpe ratio, the number of backtest runs, the variance of the sharpe ratios of those backtest runs, the skewness of the candidate strategy, its non-excess kurtosis, the number of periods in the backtest, and the number of periods in a year. Unlike the De Prado paper, I choose to return the p-value (EG 1-.

Let’s collect all our Sharpe ratios now.

```allSharpes <- c(as.numeric(table.AnnualizedReturns(sigBoxplots)[3,]),
meltedSharpes\$Sharpe,
as.numeric(table.AnnualizedReturns(strat)[3,]),
loStoplossFrame\$Sharpe)
```

And now, let’s plug and chug!

```stratSignificant <- deflatedSharpe(sharpe = as.numeric(table.AnnualizedReturns(strat)[3,]),
nTrials = length(allSharpes), varTrials = var(allSharpes),
skew = as.numeric(skewness(strat)), kurt = as.numeric(kurtosis(strat)) + 3,
numPeriods = nrow(strat), periodsInYear = 12)
```

And the result!

```> stratSignificant
[1] 0.01311
```

Success! At least at the 5% level…and a rejection at the 1% level, and any level beyond that.

So, one last thing! Out-of-sample testing on ETFs (and mutual funds during the ETF burn-in period)!

```symbols2 <- c("CWB", "JNK", "TLT", "SHY", "PCY")
getSymbols(symbols2, from='1900-01-01')
prices2 <- list()
for(tmp in symbols2) {
}
prices2 <- do.call(cbind, prices2)
colnames(prices2) <- substr(colnames(prices2), 1, 3)
returns2 <- na.omit(Return.calculate(prices2))

monthRets2 <- apply.monthly(returns2, Return.cumulative)

oosStrat <- list()
for(i in 1:12) {
oosStrat[[i]] <- ruleBacktest(returns = monthRets2, nMonths = i, dailyReturns = returns2, nSD = 252)[[1]]
}
oosStrat <- do.call(cbind, oosStrat)
oosStrat <- Return.portfolio(R = na.omit(oosStrat), rebalance_on="months")

symbols <- c("CNSAX", "FAHDX", "VUSTX", "VFISX", "PREMX")
getSymbols(symbols, from='1900-01-01')
prices <- list()
for(symbol in symbols) {
}
prices <- do.call(cbind, prices)
colnames(prices) <- substr(colnames(prices), 1, 5)
oosMFreturns <- na.omit(Return.calculate(prices))
oosMFmonths <- apply.monthly(oosMFreturns, Return.cumulative)

oosMF <- list()
for(i in 1:12) {
oosMF[[i]] <- ruleBacktest(returns = oosMFmonths, nMonths = i, dailyReturns = oosMFreturns, nSD=252)[[1]]
}
oosMF <- do.call(cbind, oosMF)
oosMF <- Return.portfolio(R = na.omit(oosMF), rebalance_on="months")
oosMF <- oosMF["2009-04/2011-03"]

fullOOS <- rbind(oosMF, oosStrat)

rbind(table.AnnualizedReturns(fullOOS), maxDrawdown(fullOOS), CalmarRatio(fullOOS))
charts.PerformanceSummary(fullOOS)
```

And the results:

```portfolio.returns
Annualized Return 0.1273
Annualized Std Dev 0.0901
Annualized Sharpe (Rf=0%) 1.4119
Worst Drawdown 0.1061
Calmar Ratio 1.1996
```

And one more equity curve (only the second!).

In other words, the out-of-sample statistics compare to the in-sample statistics. The Sharpe ratio is higher, the Calmar slightly lower. But on a whole, the performance has kept up. Unfortunately, the strategy is currently in a drawdown, but that’s the breaks.

So, whew. That concludes my first go at hypothesis-driven development, and has hopefully at least demonstrated the process to a satisfactory degree. What started off as a toy strategy instead turned from a rejection to a not rejection to demonstrating ideas from three separate papers, and having out-of-sample statistics that largely matched if not outperformed the in-sample statistics. For those thinking about investing in this strategy (again, here is the strategy: take 12 different portfolios, each selecting the asset with the highest momentum over months 1-12, target an annualized volatility of 10%, with volatility defined as the rolling annualized 252-day standard deviation, and equal-weight them every month), what I didn’t cover was turnover and taxes (this is a bond ETF strategy, so dividends will play a large role).

Now, one other request–many of the ideas for this blog come from my readers. I am especially interested in things to think about from readers with line-management responsibilities, as I think many of the questions from those individuals are likely the most universally interesting ones. If you’re one such individual, I’d appreciate an introduction, and knowing who more of the individuals in my reader base are.

NOTE: while I am currently consulting, I am always open to networking, meeting up, consulting arrangements, and job discussions. Contact me through my email at ilya.kipnis@gmail.com, or through my LinkedIn, found here.

18 thoughts on “Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample”

1. Ilya,

Excellent analysis.

However, I think you are being too conservative by including 144 prior tests in your adjusted Sharpe ratio calculation. By simply averaging the results from the 144 tests in the ‘ensemble’ method, you have actually run just 1 test. That’s because by averaging you have chosen not to optimize on the momentum formation or volatility formation parameters. Therefore these are not independent variables. The fact that you ran them independently before is irrelevant. Also, if your hypothesis was that Lo’s stop-loss methodology is effective, but not that it was effective with any particular parameter, and you had no intention of choosing an optimal parameter with this test, then it also counts as just one test.

While I really like this new direction, it is just as unhelpful to be over-conservative as it is to be overly enthusiastic. One should treat both Type I and Type II error with equal respect.

Again, great job and keep up the superb work.

Quick remark: I didn’t average all 144 tests–just the 252-day volatility ones. So that’s 12 of them.

You’re right about the De Prado methodology. A lot of these tests I run aren’t to cherry-pick best results, but to gain a general feel of the robustness of whatever it is I’m evaluating (indicator, signal, rule, etc.).

2. Nice series of posts Ilya,

Not too sure about the Python bashing but I’ll let it slide this one time hehe. I would strongly suggest you give it a fair shot before you declare R the undisputed champ. You would certainly appreciate why more and more authors are using Python across multiple disciplines.

Regarding the HDD now that you have carried it through fully, do you think there was a point before this last step where you could have rejected the idea and switched to another line of inquiry? As you know there is a significant opportunity cost to a trader/researcher time so being able to discard a bad idea earlier than not is better than the alternative. How would you handle that?

Cheers,

• Heya JP. I’ve tried to wrap my head around zipline and quantopian’s stuff, but that syntax just has me scratching my head.

As for when I could have rejected the strategy, if you recall, I wanted to reject it in part III after the Monte Carlo showed that the configurations weren’t really more intelligent than a bunch of random portfolios. However, Brian told me to see this through to the end, so I did.

That said, if you want to start blogging again and give some Python tutorials from the ground up the way I did with quantstrat, I’d be really appreciative. I know it’s a popular language, but between the disparate IDEs and library installations and god knows what else, I find it difficult to get started.

• Howard Bandy covers a couple of Python environments in his latest book “Quantitative Technical Analysy” including concise installation walk-through’s. The QTA book has quite a number of Python code listings including a complete trading system example in Python too. For more info: http://www.quantitativetechnicalanalysis.com/

3. tedthedog says:

Love your work, thanks so much for sharing it!
Have you considered block bootstrapping to address significance issues?
If a strategy isn’t over-tuned to details of the one price history that we’ve experienced for each asset, then the strategy should also work on block-bootstrapped histories of assets.
‘Work’ means a statistically significant difference in values for the strategy from e.g. values of buy&hold, where statistical significance is determined using the bootstrapped data.

• Bad idea. Momentum strategies depend on the autocorrelation of returns. Simply generating alternate realizations while ignoring this artifact defeats the purpose.

• tedthedog says:

I wasn’t being clear enough, I’m talking about block bootstrap (there is a literature on block bootstrap of time series).
Block bootstrap does not bootstrap individual daily returns, this would destroy autocorrelation as you mention.
Block bootstrap instead bootstraps blocks of returns (e.g. 1, 2, 3 … months as a block) and so it preserves autocorrelation within the blocksize.
Also, if one chooses the same block position within each asset then you also preserve cross-correlations among assets (as well as autocorrelations within assets).

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6. Victor says:

Ilya, I haven’t been checking lately since your post about going on hiatus from the blog for a while. What a way to return! Excellent stuff. I’ll be checking in more frequently.

Thank you.

7. hi,
thx for the posts.