A Review of Gary Antonacci’s Dual Momentum Investing Book

This review is a book review of Gary Antonacci’s Dual Momentum Investing book.

The TL;DR: 4.5 out of 5 stars.

So, I honestly have very little criticism of the book beyond the fact that the book sort of insinuates as though equity momentum is the be-all-end-all of investing, which is why I deduct a fraction of a point.

Now, for the book itself: first off, unlike other quantitative trading books I’ve read (aside from Andreas Clenow’s), the book outlines a very simple to follow strategy, to the point that it has already been replicated over at AllocateSmartly. (Side note: I think Walter’s resource at Allocate Smartly is probably the single best one-stop shop for reading up on any tactical asset allocation strategy, as it’s a compendium of many strategies in the risk/return profile of the 7-15% CAGR type strategies, and even has a correlation matrix between them all.)

Regarding the rest of the content, Antonacci does a very thorough job of stepping readers through the history/rationale of momentum, and not just that, but also addressing the alternatives to his strategy.

While the “why momentum works” aspect you can find in this book and others on the subject (I.E. Alpha Architect’s Quantitative Momentum book), I do like the section on other alternative assets. Namely, the book touches upon the fact that commodities no longer trend, so a lot of CTAs are taking it on the chin, and that historically, fixed income has performed much worse from an absolute return than equities. Furthermore, smart beta isn’t (smart), and many of these factors have very low aggregate returns (if they’re significant at all, I believe Wesley Gray at Alpha Architect has a blog post stating that they aren’t). There are a fair amount of footnotes for those interested in corroborating the assertions. Suffice to say, when it comes to strategies that don’t need daily micromanagement, when it comes to how far you can get without leverage (essentially, anything outside the space of volatility trading strategies), equity momentum is about as good as you get.

Antonacci then introduces his readers to his GEM (Global Equities Momentum) strategy, which can be explained in a few sentences: at the end of each month, calculate the total 12-month return of SPY, EAFE, and BIL. If BIL has the highest return, buy AGG for that month, otherwise buy the asset with the highest return. Repeat. That’s literally it, and the performance characteristics, on a risk-adjusted basis, are superior to just about any equity fund tied down to a tiny tracking error. Essentially, the reason for that is that equity markets have bear markets, and a dual momentum strategy lets you preserve your gains instead of giving it back in market corrections (I.E. 2000-2003, 2008, etc.) while keeping pace during the good times.

Lastly, Antonacci provides some ideas for possibly improving on GEM. I may examine on these in the future. However, the low-hanging fruit for improving on this strategy, in my opinion, is to find some other strategies that diversify its drawdowns, and raise its risk-adjusted return profile. Even if the total return goes down, I believe that an interactive brokers account can offer some amount of leverage (either 50% or 100%) to boost the total returns back up, or combine a more diversified portfolio with a volatility strategy.

Lastly, the appendix includes the original dual momentum paper, and a monthly return table for GEM going back to 1974.

All in all, this book is about as accessible and comprehensive as you can get on a solid strategy that readers actually *can* implement themselves in their brokerage account of choice (please use IB or Robinhood because there’s no point paying $8-$10 per trade if you’re retail). That said, I still think that there are venues in which to travel if you’re looking to improve your overall portfolio with GEM as a foundation.

Thanks for reading.

NOTEL I am always interested in networking and hearing about full-time roles which can benefit from my skill set. My linkedin profile can be found here.

Advertisements

Creating a VIX Futures Term Structure In R From Official CBOE Settlement Data

This post will be detailing a process to create a VIX term structure from freely available CBOE VIX settlement data and a calendar of freely obtainable VIX expiry dates. This has applications for volatility trading strategies.

So this post, as has been the usual for quite some time, will not be about a strategy, but rather, a tool that can be used for exploring future strategies. Particularly, volatility strategies–which seems to have been a hot topic on this blog some time ago (and might very well be still, especially since the Volatility Made Simple blog has just stopped tracking open-sourced strategies for the past year).

This post’s topic is the VIX term structure–that is, creating a set of continuous contracts–properly rolled according to VIX contract specifications, rather than a hodgepodge of generic algorithms as found on some other websites. The idea is, as of the settlement of a previous day (or whenever the CBOE actually releases their data), you can construct a curve of contracts, and see if it’s in contango (front month cheaper than next month and so on) or backwardation (front month more expensive than next month, etc.).

The first (and most code-intensive) part of the procedure is fairly simple–map the contracts to an expiration date, then put their settlement dates and times to expiry into two separate xts objects, with one column for each contract.

The expiries text file is simply a collection of copied and pasted expiry dates from this site. It includes the January 2018 expiration date. Here is what it looks like:

> head(expiries)
  V1       V2   V3
1 18  January 2006
2 15 February 2006
3 22    March 2006
4 19    April 2006
5 17      May 2006
6 21     June 2006
require(xts)
require(data.table)

# 06 through 17
years <- c(paste0("0", c(6:9)), as.character(c(10:17)))

# futures months
futMonths <- c("F", "G", "H", "J", "K", "M",
            "N", "Q", "U", "V", "X", "Z")

# expiries come from http://www.macroption.com/vix-expiration-calendar/
expiries <- read.table("expiries.txt", header = FALSE, sep = " ")

# convert expiries into dates in R
dateString <- paste(expiries$V3, expiries$V2, expiries$V1, sep = "-")
dates <- as.Date(dateString, format = "%Y-%B-%d")

# map futures months to numbers for dates
monthMaps <- cbind(futMonths, c("01", "02", "03", "04", "05", "06",
                                   "07", "08", "09", "10", "11", "12"))
monthMaps <- data.frame(monthMaps)
colnames(monthMaps) <- c("futureStem", "monthNum")

dates <- data.frame(dates)
dates$dateMon <- substr(dates$dates, 1, 7)

contracts <- expand.grid(futMonths, years)
contracts <- paste0(contracts[,1], contracts[,2])
contracts <- c(contracts, "F18")
stem <- "https://cfe.cboe.com/Publish/ScheduledTask/MktData/datahouse/CFE_"
#contracts <- paste0(stem, contracts, "_VX.csv")

masterlist <- list()
timesToExpiry <- list()
for(i in 1:length(contracts)) {
  
  # obtain data
  contract <- contracts[i]
  dataFile <- paste0(stem, contract, "_VX.csv")
  expiryYear <- paste0("20",substr(contract, 2, 3))
  expiryMonth <- monthMaps$monthNum[monthMaps$futureStem == substr(contract,1,1)]
  expiryDate <- dates$dates[dates$dateMon == paste(expiryYear, expiryMonth, sep="-")]
  data <- suppressWarnings(fread(dataFile))
  
  # create dates
  dataDates <- as.Date(data$`Trade Date`, format = '%m/%d/%Y')
  
  # create time to expiration xts
  toExpiry <- xts(expiryDate - dataDates, order.by=dataDates)
  colnames(toExpiry) <- contract
  timesToExpiry[[i]] <- toExpiry
  
  # get settlements
  settlement <- xts(data$Settle, order.by=dataDates)
  colnames(settlement) <- contract
  masterlist[[i]] <- settlement
}

# cbind outputs
masterlist <- do.call(cbind, masterlist)
timesToExpiry <- do.call(cbind, timesToExpiry)

# NA out zeroes in settlements
masterlist[masterlist==0] <- NA

From there, we need to visualize how many contracts are being traded at once on any given day (I.E. what’s a good steady state number for the term structure)?

sumNonNA <- function(row) {
  return(sum(!is.na(row)))
}

simultaneousContracts <- xts(apply(masterlist, 1, sumNonNA), order.by=index(masterlist))
chart.TimeSeries(simultaneousContracts)

The result looks like this:

So, 8 contracts (give or take) at any given point in time. This is confirmed by the end of the master list of settlements.

dim(masterlist)
tail(masterlist[,135:145])
> dim(masterlist)
[1] 3002  145
> tail(masterlist[,135:145])
           H17    J17    K17    M17    N17    Q17    U17    V17    X17    Z17   F18
2017-04-18  NA 14.725 14.325 14.525 15.175 15.475 16.225 16.575 16.875 16.925    NA
2017-04-19  NA 14.370 14.575 14.525 15.125 15.425 16.175 16.575 16.875 16.925    NA
2017-04-20  NA     NA 14.325 14.325 14.975 15.375 16.175 16.575 16.875 16.900    NA
2017-04-21  NA     NA 14.325 14.225 14.825 15.175 15.925 16.350 16.725 16.750    NA
2017-04-24  NA     NA 12.675 13.325 14.175 14.725 15.575 16.025 16.375 16.475 17.00
2017-04-25  NA     NA 12.475 13.125 13.975 14.425 15.225 15.675 16.025 16.150 16.75

Using this information, an algorithm can create eight continuous contracts, ranging from front month to eight months out. The algorithm starts at the first day of the master list to the first expiry, then moves between expiry windows, and just appends the front month contract, and the next seven contracts to a list, before rbinding them together, and does the same with the expiry structure.

termStructure <- list()
expiryStructure <- list()
masterDates <- unique(c(first(index(masterlist)), dates$dates[dates$dates %in% index(masterlist)], Sys.Date()-1))
for(i in 1:(length(masterDates)-1)) {
  subsetDates <- masterDates[c(i, i+1)]
  dateRange <- paste(subsetDates[1], subsetDates[2], sep="::")
  subset <- masterlist[dateRange,c(i:(i+7))]
  subset <- subset[-1,]
  expirySubset <- timesToExpiry[index(subset), c(i:(i+7))]
  colnames(subset) <- colnames(expirySubset) <- paste0("C", c(1:8))
  termStructure[[i]] <- subset
  expiryStructure[[i]] <- expirySubset
}

termStructure <- do.call(rbind, termStructure)
expiryStructure <- do.call(rbind, expiryStructure)

Again, one more visualization of when we have a suitable number of contracts:

simultaneousContracts <- xts(apply(termStructure, 1, sumNonNA), order.by=index(termStructure))
chart.TimeSeries(simultaneousContracts)

And in order to preserve the most data, we’ll cut the burn-in period off when we first have 7 contracts trading at once.

first(index(simultaneousContracts)[simultaneousContracts >= 7])
termStructure <- termStructure["2006-10-23::"]
expiryStructure <- expiryStructure[index(termStructure)]

So there you have it–your continuous VIX futures contract term structure, as given by the official CBOE settlements. While some may try and simulate a trading strategy based on these contracts, I myself prefer to use them as indicators or features to a model that would rather buy XIV or VXX.

One last trick, for those that want to visualize things, a way to actually visualize the term structure on any given day, in particular, the most recent one in the term structure.

plot(t(coredata(last(termStructure))), type = 'b')

A clear display of contango.

A post on how to compute synthetic constant-expiry contracts (EG constant 30 day expiry contracts) will be forthcoming in the near future.

Thanks for reading.

NOTE: I am currently interested in networking and full-time positions which may benefit from my skills. I may be contacted at my LinkedIn profile found here.

Nuts and Bolts of Quantstrat, Part V

This post will be about pre-processing custom indicators in quantstrat–that is, how to add values to your market data that do not arise from the market data itself.

The first four parts of my nuts and bolts of quantstrat were well received. They are even available as a datacamp course. For those that want to catch up to today’s post, I highly recommend the datacamp course.

To motivate this post, the idea is that say you’re using alternative data that isn’t simply derived from a transformation of the market data itself. I.E. you have a proprietary alternative data stream that may predict an asset’s price, you want to employ a cross-sectional ranking system, or any number of things. How do you do this within the context of quantstrat?

The answer is that it’s as simple as binding a new xts to your asset data, as this demonstration will show.

First, let’s get the setup out of the way.

require(quantstrat)
require(PerformanceAnalytics)

initDate="1990-01-01"
from="2003-01-01"
to="2012-12-31"
options(width=70)

options("getSymbols.warning4.0"=FALSE)

currency('USD')
Sys.setenv(TZ="UTC")

symbols <- 'SPY'
suppressMessages(getSymbols(symbols, from=from, to=to, src="yahoo", adjust=TRUE))  

stock(symbols, currency="USD", multiplier=1)

Now, we have our non-derived indicator. In this case, it’s a toy example–the value is 1 if the year is odd (I.E. 2003, 2005, 2007, 2009), and 0 if it’s even. We compute that and simply column-bind (cbind) it to the asset data.

nonDerivedIndicator <- as.numeric(as.character(substr(index(SPY), 1, 4)))%%2 == 1
nonDerivedIndicator <- xts(nonDerivedIndicator, order.by=index(SPY))

SPY <- cbind(SPY, nonDerivedIndicator)
colnames(SPY)[7] = "nonDerivedIndicator"

Next, we just have a very simple strategy–buy a share of SPY on odd years, sell on even years. That is, buy when the nonDerivedIndicator column crosses above 0.5 (from 0 to 1), and sell when the opposite occurs.

strategy.st <- portfolio.st <- account.st <- "nonDerivedData"
rm.strat(strategy.st)
initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD')
initAcct(account.st, portfolios=portfolio.st, initDate=initDate, currency='USD')
initOrders(portfolio.st, initDate=initDate)
strategy(strategy.st, store=TRUE)

add.signal(strategy.st, name = sigThreshold, 
           arguments = list(column = "nonDerivedIndicator", threshold = 0.5, relationship = "gte", cross = TRUE),
           label = "longEntry")

add.signal(strategy.st, name = sigThreshold, 
           arguments = list(column = "nonDerivedIndicator", threshold = 0.5, relationship = "lte", cross = TRUE),
           label = "longExit")


tmp <- applySignals(strategy = strategy.st, mktdata=SPY)


add.rule(strategy.st, name="ruleSignal", 
         arguments=list(sigcol="longEntry", sigval=TRUE, ordertype="market", 
                        orderside="long", replace=FALSE, prefer="Open", orderqty = 1), 
         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)

And the result:

chart.Posn(portfolio.st, 'SPY')

In conclusion, you can create signals based off of any data in quantstrat. Whether that means volatility ratios, fundamental data, cross-sectional ranking, or whatever proprietary alternative data source you may have access to, this very simple process is how you can use quantstrat to add all of those things to your systematic trading backtest research.

Thanks for reading.

Note: I am always interested in full-time opportunities which may benefit from my skills. I have experience in data analytics, asset management, and systematic trading research. If you know of any such opportunities, do not hesitate to contact me on my LinkedIn, found here.

Ehlers’s Autocorrelation Periodogram

This post will introduce John Ehlers’s Autocorrelation Periodogram mechanism–a mechanism designed to dynamically find a lookback period. That is, the most common parameter optimized in backtests is the lookback period.

Before beginning this post, I must give credit where it’s due, to one Mr. Fabrizio Maccallini, the head of structured derivatives at Nordea Markets in London. You can find the rest of the repository he did for Dr. John Ehlers’s Cycle Analytics for Traders on his github. I am grateful and honored that such intelligent and experienced individuals are helping to bring some of Dr. Ehlers’s methods into R.

The point of the Ehlers Autocorrelation Periodogram is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a lookback period.

SMA of 50 days? 100 days? 200 days? Well, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your lookback, and it does the rest. How well it does it is a topic of discussion for those well-versed in the methodologies of electrical engineering (I’m not), so feel free to leave comments that discuss how well the algorithm does its job, and feel free to blog about it as well.

In any case, here’s the original algorithm code, courtesy of Mr. Maccallini:

AGC <- function(loCutoff = 10, hiCutoff = 48, slope = 1.5) {      accSlope = -slope # acceptableSlope = 1.5 dB   ratio = 10 ^ (accSlope / 20)   if ((hiCutoff - loCutoff) > 0)
    factor <-  ratio ^ (2 / (hiCutoff - loCutoff));
  return (factor)
}

autocorrPeriodogram <- function(x, period1 = 10, period2 = 48, avgLength = 3) {
  # high pass filter
  alpha1 <- (cos(sqrt(2) * pi / period2) + sin(sqrt(2) * pi / period2) - 1) / cos(sqrt(2) * pi / period2)
  hp <- (1 - alpha1 / 2) ^ 2 * (x - 2 * lag(x) + lag(x, 2))
  hp <- hp[-c(1, 2)]
  hp <- filter(hp, (1 - alpha1), method = "recursive")
  hp <- c(NA, NA, hp)
  hp <- xts(hp, order.by = index(x))
  # super smoother
  a1 <- exp(-sqrt(2) * pi / period1)
  b1 <- 2 * a1 * cos(sqrt(2) * pi / period1)
  c2 <- b1
  c3 <- -a1 * a1
  c1 <- 1 - c2 - c3
  filt <- c1 * (hp + lag(hp)) / 2
  leadNAs <- sum(is.na(filt))
  filt <- filt[-c(1: leadNAs)]
  filt <- filter(filt, c(c2, c3), method = "recursive")
  filt <- c(rep(NA, leadNAs), filt)
  filt <- xts(filt, order.by = index(x))
  # Pearson correlation for each value of lag
  autocorr <- matrix(0, period2, length(filt))
  for (lag in 2: period2) {
    # Set the average length as M
    if (avgLength == 0) M <- lag
    else M <- avgLength
    autocorr[lag, ] <- runCor(filt, lag(filt, lag), M)
  }
  autocorr[is.na(autocorr)] <- 0
  # Discrete Fourier transform
  # Correlate autocorrelation values with the cosine and sine of each period of interest
  # The sum of the squares of each value represents relative power at each period
  cosinePart <- sinePart <- sqSum <- R <- Pwr <- matrix(0, period2, length(filt))
  for (period in period1: period2) {
    for (N in 2: period2) {
      cosinePart[period, ] = cosinePart[period, ] + autocorr[N, ] * cos(2 * N * pi / period)
      sinePart[period, ] = sinePart[period, ] + autocorr[N, ] * sin(2 * N * pi / period)
    }
    sqSum[period, ] = cosinePart[period, ] ^ 2 + sinePart[period, ] ^ 2
    R[period, ] <- EMA(sqSum[period, ] ^ 2, ratio = 0.2)
  }
  R[is.na(R)] <- 0
  # Normalising Power
  K <- AGC(period1, period2, 1.5)
  maxPwr <- rep(0, length(filt))   for(period in period1: period2) {     for (i in 1: length(filt)) {       if (R[period, i] >= maxPwr[i]) maxPwr[i] <- R[period, i]
      else maxPwr[i] <- K * maxPwr[i]
    }
  }
  for(period in 2: period2) {
    Pwr[period, ] <- R[period, ] / maxPwr
  }
  # Compute the dominant cycle using the Center of Gravity of the spectrum
  Spx <- Sp <- rep(0, length(filter))
  for(period in period1: period2) {
    Spx <- Spx + period * Pwr[period, ] * (Pwr[period, ] >= 0.5)
    Sp <- Sp + Pwr[period, ] * (Pwr[period, ] >= 0.5)
  }
  dominantCycle <- Spx / Sp
  dominantCycle[is.nan(dominantCycle)] <- 0
  dominantCycle <- xts(dominantCycle, order.by=index(x))
  dominantCycle <- dominantCycle[dominantCycle > 0]
  return(dominantCycle)
  #heatmap(Pwr, Rowv = NA, Colv = NA, na.rm = TRUE, labCol = "", add.expr = lines(dominantCycle, col = 'blue'))
}

One thing I do notice is that this code uses a loop that says for(i in 1:length(filt)), which is an O(data points) loop, which I view as the plague in R. While I’ve used Rcpp before, it’s been for only the most basic of loops, so this is definitely a place where the algorithm can stand to be improved with Rcpp due to R’s inherent poor looping.

Those interested in the exact logic of the algorithm will, once again, find it in John Ehlers’s Cycle Analytics For Traders book (see link earlier in the post).

Of course, the first thing to do is to test how well the algorithm does what it purports to do, which is to dictate the lookback period of an algorithm.

Let’s run it on some data.

getSymbols('SPY', from = '1990-01-01')

t1 <- Sys.time()
out <- autocorrPeriodogram(Ad(SPY), period1 = 120, period2 = 252, avgLength = 3)
t2 <- Sys.time() print(t2-t1) 

And the result:

 > t1 <- Sys.time() > out <- autocorrPeriodogram(Ad(SPY), period1 = 120, period2 = 252, avgLength = 3) > t2 <- Sys.time() > print(t2-t1)
Time difference of 33.25429 secs

Now, what does the algorithm-set lookback period look like?

plot(out)

Let’s zoom in on 2001 through 2003, when the markets went through some upheaval.

plot(out['2001::2003']

In this zoomed-in image, we can see that the algorithm’s estimates seem fairly jumpy.

Here’s some code to feed the algorithm’s estimates of n into an indicator to compute an indicator with a dynamic lookback period as set by Ehlers’s autocorrelation periodogram.

acpIndicator <- function(x, minPeriod, maxPeriod, indicatorFun = EMA, ...) {
  acpOut <- autocorrPeriodogram(x = x, period1 = minPeriod, period2 = maxPeriod)
  roundedAcpNs <- round(acpOut, 0) # round to the nearest integer
  uniqueVals <- unique(roundedAcpNs) # unique integer values
  out <- xts(rep(NA, length(roundedAcpNs)), order.by=index(roundedAcpNs))

  for(i in 1:length(uniqueVals)) { # loop through unique values, compute indicator
    tmp <- indicatorFun(x, n = uniqueVals[i], ...)
    out[roundedAcpNs==uniqueVals[i]] <- tmp[roundedAcpNs==uniqueVals[i]]
  }
  return(out)
}

And here is the function applied with an SMA, to tune between 120 and 252 days.

ehlersSMA <- acpIndicator(Ad(SPY), 120, 252, indicatorFun = SMA)

plot(Ad(SPY)['2008::2010'])
lines(ehlersSMA['2008::2010'], col = 'red')

And the result:

As seen, this algorithm is less consistent than I would like, at least when it comes to using a simple moving average.

For now, I’m going to leave this code here, and let people experiment with it. I hope that someone will find that this indicator is helpful to them.

Thanks for reading.

NOTES: I am always interested in networking/meet-ups in the northeast (Philadelphia/NYC). Furthermore, if you believe your firm will benefit from my skills, please do not hesitate to reach out to me. My linkedin profile can be found here.

Lastly, I am volunteering to curate the R section for books on quantocracy. If you have a book about R that can apply to finance, be sure to let me know about it, so that I can review it and possibly recommend it. Thakn you.

A Review of Alpha Architect’s (Wes Gray/Jack Vogel) Quantitative Momentum book

This post will be an in-depth review of Alpha Architect’s Quantitative Momentum book. Overall, in my opinion, the book is terrific for those that are practitioners in fund management in the individual equity space, and still contains ideas worth thinking about outside of that space. However, the system detailed in the book benefits from nested ranking (rank along axis X, take the top decile, rank along axis Y within the top decile in X, and take the top decile along axis Y, essentially restricting selection to 1% of the universe). Furthermore, the book does not do much to touch upon volatility controls, which may have enhanced the system outlined greatly.

Before I get into the brunt of this post, I’d like to let my readers know that I formalized my nuts and bolts of quantstrat series of posts as a formal datacamp course. Datacamp is a very cheap way to learn a bunch of R, and financial applications are among those topics. My course covers the basics of quantstrat, and if those who complete the course like it, I may very well create more advanced quantstrat modules on datacamp. I’m hoping that the finance courses are well-received, since there are financial topics in R I’d like to learn myself that a 45 minute lecture doesn’t really suffice for (such as Dr. David Matteson’s change points magic, PortfolioAnalytics, and so on). In any case, here’s the link.

So, let’s start with a summary of the book:

Part 1 is several chapters that are the giant expose- of why momentum works (or at least, has worked for at least 20 years since 1993)…namely that human biases and irrational behaviors act in certain ways to make the anomaly work. Then there’s also the career risk (AKA it’s a risk factor, and so, if your benchmark is SPY and you run across a 3+ year period of underperformance, you have severe career risk), and essentially, a whole litany of why a professional asset manager would get fired but if you just stick with the anomaly over many many years and ride out multi-year stretches of relative underperformance, you’ll come out ahead in the very long run.

Generally, I feel like there’s work to be done if this is the best that can be done, but okay, I’ll accept it.

Essentially, part 1 is for the uninitiated. For those that have been around the momentum block a couple of times, they can skip right past this. Unfortunately, it’s half the book, so that leaves a little bit of a sour taste in the mouth.

Next, part two is where, in my opinion, the real meat and potatoes of the book–the “how”.

Essentially, the algorithm can be boiled down into the following:

Taking the universe of large and mid-cap stocks, do the following:

1) Sort the stocks into deciles by 2-12 momentum–that is, at the end of every month, calculate momentum by last month’s closing price minus the closing price 12 months ago. Essentially, research states that there’s a reversion effect on the 1-month momentum. However, this effect doesn’t carry over into the ETF universe in my experience.

2) Here’s the interesting part which makes the book worth picking up on its own (in my opinion): after sorting into deciles, rank the top decile by the following metric: multiply the sign of the 2-12 momentum by the following equation: (% negative returns – % positive). Essentially, the idea here is to determine smoothness of momentum. That is, in the most extreme situation, imagine a stock that did absolutely nothing for 230 days, and then had one massive day that gave it its entire price appreciation (think Google when it had a 10% jump off of better-than-expected numbers reports), and in the other extreme, a stock that simply had each and every single day be a small positive price appreciation. Obviously, you’d want the second type of stock. That’s this idea. Again, sort into deciles, and take the top decile. Therefore, taking the top decile of the top decile leaves you with 1% of the universe. Essentially, this makes the idea very difficult to replicate–since you’d need to track down a massive universe of stocks. That stated, I think the expression is actually a pretty good idea as a stand-in for volatility. That is, regardless of how volatile an asset is–whether it’s as volatile as a commodity like DBC, or as non-volatile as a fixed-income product like SHY, this expression is an interesting way of stating “this path is choppy” vs. “this path is smooth”. I might investigate this expression on my blog further in the future.

3) Lastly, if the portfolio is turning over quarterly instead of monthly, the best months to turn it over are the months preceding end-of-quarter month (that is, February, May, August, November) because a bunch of amateur asset managers like to “window dress” their portfolios. That is, they had a crummy quarter, so at the last month before they have to send out quarterly statements, they load up on some recent winners so that their clients don’t think they’re as amateur as they really let on, and there’s a bump for this. Similarly, January has some selling anomalies due to tax-loss harvesting. As far as practical implementations go, I think this is a very nice touch. Conceding the fact that turning over every month may be a bit too expensive, I like that Wes and Jack say “sure, you want to turn it over once every three months, but on *which* months?”. It’s a very good question to ask if it means you get an additional percentage point or 150 bps a year from that, as it just might cover the transaction costs and then some.

All in all, it’s a fairly simple to understand strategy. However, the part that sort of gates off the book to a perfect replication is the difficulty in obtaining the CRSP data.

However, I do commend Alpha Architect for disclosing the entire algorithm from start to finish.

Furthermore, if the basic 2-12 momentum is not enough, there’s an appendix detailing other types of momentum ideas (earnings momentum, ranking by distance to 52-week highs, absolute historical momentum, and so on). None of these strategies are really that much better than the basic price momentum strategy, so they’re there for those interested, but it seems there’s nothing really ground-breaking there. That is, if you’re trading once a month, there’s only so many ways of saying “hey, I think this thing is going up!”

I also like that Wes and Jack touched on the fact that trend-following, while it doesn’t improve overall CAGR or Sharpe, does a massive amount to improve on max drawdown. That is, if faced with the prospect of losing 70-80% of everything, and losing only 30%, that’s an easy choice to make. Trend-following is good, even a simplistic version.

All in all, I think the book accomplishes what it sets out to do, which is to present a well-researched algorithm. Ultimately, the punchline is on Alpha Architect’s site (I believe they have some sort of monthly stock filter). Furthermore, the book states that there are better risk-adjusted returns when combined with the algorithm outlined in the “quantitative value” book. In my experience, I’ve never had value algorithms impress me in the backtests I’ve done, but I can chalk that up to me being inexperienced with all the various valuation metrics.

My criticism of the book, however, is this:

The momentum algorithm in the book misses what I feel is one key component: volatility targeting control. Simply, the paper “momentum has its moments” (which I covered in my hypothesis-driven development series of posts) essentially states that the usual Fama-French momentum strategy does far better from a risk-reward strategy by deleveraging during times of excessive volatility, and avoiding momentum crashes. I’m not sure why Wes and Jack didn’t touch upon this paper, since the implementation is very simple (target/realized volatility = leverage factor). Ideally, I’d love if Wes or Jack could send me the stream of returns for this strategy (preferably daily, but monthly also works).

Essentially, I think this book is very comprehensive. However, I think it also has a somewhat “don’t try this at home” feel to it due to the data requirement to replicate it. Certainly, if your broker charges you $8 a transaction, it’s not a feasible strategy to drop several thousand bucks a year on transaction costs that’ll just give your returns to your broker. However, I do wonder if the QMOM ETF (from Alpha Architect, of course) is, in fact, a better version of this strategy, outside of the management fee.

In any case, my final opinion is this: while this book leaves a little bit of knowledge on the table, on a whole, it accomplishes what it sets out to do, is clear with its procedures, and provides several worthwhile ideas. For the price of a non-technical textbook (aka those $60+ books on amazon), this book is a steal.

4.5/5 stars.

Thanks for reading.

NOTE: While I am currently employed in a successful analytics capacity, I am interested in hearing about full-time positions more closely related to the topics on this blog. If you have a full-time position which can benefit from my current skills, please let me know. My Linkedin can be found here.

The Problem With Depmix For Online Regime Prediction

This post will be about attempting to use the Depmix package for online state prediction. While the depmix package performs admirably when it comes to describing the states of the past, when used for one-step-ahead prediction, under the assumption that tomorrow’s state will be identical to today’s, the hidden markov model process found within the package does not perform to expectations.

So, to start off, this post was motivated by Michael Halls-Moore, who recently posted some R code about using the depmixS4 library to use hidden markov models. Generally, I am loath to create posts on topics I don’t feel I have an absolutely front-to-back understanding of, but I’m doing this in the hope of learning from others on how to appropriately do online state-space prediction, or “regime switching” detection, as it may be called in more financial parlance.

Here’s Dr. Halls-Moore’s post.

While I’ve seen the usual theory of hidden markov models (that is, it can rain or it can be sunny, but you can only infer the weather judging by the clothes you see people wearing outside your window when you wake up), and have worked with toy examples in MOOCs (Udacity’s self-driving car course deals with them, if I recall correctly–or maybe it was the AI course), at the end of the day, theory is only as good as how well an implementation can work on real data.

For this experiment, I decided to take SPY data since inception, and do a full in-sample “backtest” on the data. That is, given that the HMM algorithm from depmix sees the whole history of returns, with this “god’s eye” view of the data, does the algorithm correctly classify the regimes, if the backtest results are any indication?

Here’s the code to do so, inspired by Dr. Halls-Moore’s.

require(depmixS4)
require(quantmod)
getSymbols('SPY', from = '1990-01-01', src='yahoo', adjust = TRUE)
spyRets <- na.omit(Return.calculate(Ad(SPY)))

set.seed(123)

hmm <- depmix(SPY.Adjusted ~ 1, family = gaussian(), nstates = 3, data=spyRets)
hmmfit <- fit(hmm, verbose = FALSE)
post_probs <- posterior(hmmfit)
post_probs <- xts(post_probs, order.by=index(spyRets))
plot(post_probs$state)
summaryMat <- data.frame(summary(hmmfit))
colnames(summaryMat) <- c("Intercept", "SD")
bullState <- which(summaryMat$Intercept > 0)
bearState <- which(summaryMat$Intercept < 0)

hmmRets <- spyRets * lag(post_probs$state == bullState) - spyRets * lag(post_probs$state == bearState)
charts.PerformanceSummary(hmmRets)
table.AnnualizedReturns(hmmRets)

Essentially, while I did select three states, I noted that anything with an intercept above zero is a bull state, and below zero is a bear state, so essentially, it reduces to two states.

With the result:

table.AnnualizedReturns(hmmRets)
                          SPY.Adjusted
Annualized Return               0.1355
Annualized Std Dev              0.1434
Annualized Sharpe (Rf=0%)       0.9448

So, not particularly terrible. The algorithm works, kind of, sort of, right?

Well, let’s try online prediction now.

require(DoMC)

dailyHMM <- function(data, nPoints) {
  subRets <- data[1:nPoints,]
  hmm <- depmix(SPY.Adjusted ~ 1, family = gaussian(), nstates = 3, data = subRets)
  hmmfit <- fit(hmm, verbose = FALSE)
  post_probs <- posterior(hmmfit)
  summaryMat <- data.frame(summary(hmmfit))
  colnames(summaryMat) <- c("Intercept", "SD")
  bullState <- which(summaryMat$Intercept > 0)
  bearState <- which(summaryMat$Intercept < 0)
  if(last(post_probs$state) %in% bullState) {
    state <- xts(1, order.by=last(index(subRets)))
  } else if (last(post_probs$state) %in% bearState) {
    state <- xts(-1, order.by=last(index(subRets)))
  } else {
    state <- xts(0, order.by=last(index(subRets)))
  }
  colnames(state) <- "State"
  return(state)
}

# took 3 hours in parallel
t1 <- Sys.time()
set.seed(123)
registerDoMC((detectCores() - 1))
states <- foreach(i = 500:nrow(spyRets), .combine=rbind) %dopar% {
  dailyHMM(data = spyRets, nPoints = i)
}
t2 <- Sys.time()
print(t2-t1)

So what I did here was I took an expanding window, starting from 500 days since SPY’s inception, and kept increasing it, by one day at a time. My prediction, was, trivially enough, the most recent day, using a 1 for a bull state, and a -1 for a bear state. I ran this process in parallel (on a linux cluster, because windows’s doParallel library seems to not even know that certain packages are loaded, and it’s more messy), and the first big issue is that this process took about three hours on seven cores for about 23 years of data. Not exactly encouraging, but computing time isn’t expensive these days.

So let’s see if this process actually works.

First, let’s test if the algorithm does what it’s actually supposed to do and use one day of look-ahead bias (that is, the algorithm tells us the state at the end of the day–how correct is it even for that day?).


onlineRets <- spyRets * states 
charts.PerformanceSummary(onlineRets)
table.AnnualizedReturns(onlineRets)

With the result:

> table.AnnualizedReturns(onlineRets)
                          SPY.Adjusted
Annualized Return               0.2216
Annualized Std Dev              0.1934
Annualized Sharpe (Rf=0%)       1.1456

So, allegedly, the algorithm seems to do what it was designed to do, which is to classify a state for a given data set. Now, the most pertinent question: how well do these predictions do even one day ahead? You’d think that state space predictions would be parsimonious from day to day, given the long history, correct?


onlineRets <- spyRets * lag(states)
charts.PerformanceSummary(onlineRets)
table.AnnualizedReturns(onlineRets)

With the result:

> table.AnnualizedReturns(onlineRets)
                          SPY.Adjusted
Annualized Return               0.0172
Annualized Std Dev              0.1939
Annualized Sharpe (Rf=0%)       0.0888

That is, without the lookahead bias, the state space prediction algorithm is atrocious. Why is that?

Well, here’s the plot of the states:

In short, the online hmm algorithm in the depmix package seems to change its mind very easily, with obvious (negative) implications for actual trading strategies.

So, that wraps it up for this post. Essentially, the main message here is this: there’s a vast difference between loading doing descriptive analysis (AKA “where have you been, why did things happen”) vs. predictive analysis (that is, “if I correctly predict the future, I get a positive payoff”). In my opinion, while descriptive statistics have their purpose in terms of explaining why a strategy may have performed how it did, ultimately, we’re always looking for better prediction tools. In this case, depmix, at least in this “out-of-the-box” demonstration does not seem to be the tool for that.

If anyone has had success with using depmix (or other regime-switching algorithm in R) for prediction, I would love to see work that details the procedure taken, as it’s an area I’m looking to expand my toolbox into, but don’t have any particular good leads. Essentially, I’d like to think of this post as me describing my own experiences with the package.

Thanks for reading.

NOTE: On Oct. 5th, I will be in New York City. On Oct. 6th, I will be presenting at The Trading Show on the Programming Wars panel.

NOTE: My current analytics contract is up for review at the end of the year, so I am officially looking for other offers as well. If you have a full-time role which may benefit from the skills you see on my blog, please get in touch with me. My linkedin profile can be found here.

An Introduction to Portfolio Component Conditional Value At Risk

This post will introduce component conditional value at risk mechanics found in PerformanceAnalytics from a paper written by Brian Peterson, Kris Boudt, and Peter Carl. This is a mechanism that is an easy-to-call mechanism for computing component expected shortfall in asset returns as they apply to a portfolio. While the exact mechanics are fairly complex, the upside is that the running time is nearly instantaneous, and this method is a solid tool for including in asset allocation analysis.

For those interested in an in-depth analysis of the intuition of component conditional value at risk, I refer them to the paper written by Brian Peterson, Peter Carl, and Kris Boudt.

Essentially, here’s the idea: all assets in a given portfolio have a marginal contribution to its total conditional value at risk (also known as expected shortfall)–that is, the expected loss when the loss surpasses a certain threshold. For instance, if you want to know your 5% expected shortfall, then it’s the average of the worst 5 returns per 100 days, and so on. For returns using daily resolution, the idea of expected shortfall may sound as though there will never be enough data in a sufficiently fast time frame (on one year or less), the formula for expected shortfall in the PerformanceAnalytics defaults to an approximation calculation using a Cornish-Fisher expansion, which delivers very good results so long as the p-value isn’t too extreme (that is, it works for relatively sane p values such as the 1%-10% range).

Component Conditional Value at Risk has two uses: first off, given no input weights, it uses an equal weight default, which allows it to provide a risk estimate for each individual asset without burdening the researcher to create his or her own correlation/covariance heuristics. Secondly, when provided with a set of weights, the output changes to reflect the contribution of various assets in proportion to those weights. This means that this methodology works very nicely with strategies that exclude assets based on momentum, but need a weighting scheme for the remaining assets. Furthermore, using this methodology also allows an ex-post analysis of risk contribution to see which instrument contributed what to risk.

First, a demonstration of how the mechanism works using the edhec data set. There is no strategy here, just a demonstration of syntax.

require(quantmod)
require(PerformanceAnalytics)

data(edhec)

tmp &lt;- CVaR(edhec, portfolio_method = &quot;component&quot;)

This will assume an equal-weight contribution from all of the funds in the edhec data set.

So tmp is the contribution to expected shortfall from each of the various edhec managers over the entire time period. Here’s the output:

$MES
           [,1]
[1,] 0.03241585

$contribution
 Convertible Arbitrage             CTA Global  Distressed Securities       Emerging Markets  Equity Market Neutral
          0.0074750513          -0.0028125166           0.0039422674           0.0069376579           0.0008077760
          Event Driven Fixed Income Arbitrage           Global Macro      Long/Short Equity       Merger Arbitrage
          0.0037114666           0.0043125937           0.0007173036           0.0036152960           0.0013693293
        Relative Value          Short Selling         Funds of Funds
          0.0037650911          -0.0048178690           0.0033924063 

$pct_contrib_MES
 Convertible Arbitrage             CTA Global  Distressed Securities       Emerging Markets  Equity Market Neutral
            0.23059863            -0.08676361             0.12161541             0.21402052             0.02491917
          Event Driven Fixed Income Arbitrage           Global Macro      Long/Short Equity       Merger Arbitrage
            0.11449542             0.13303965             0.02212817             0.11152864             0.04224258
        Relative Value          Short Selling         Funds of Funds
            0.11614968            -0.14862694             0.10465269

The salient part of this is the percent contribution (the last output). Notice that it can be negative, meaning that certain funds gain when others lose. At least, this was the case over the current data set. These assets diversify a portfolio and actually lower expected shortfall.

&gt; tmp2 &lt;- CVaR(edhec, portfolio_method = &quot;component&quot;, weights = c(rep(.1, 10), rep(0,3)))
&gt; tmp2
$MES
           [,1]
[1,] 0.04017453

$contribution
 Convertible Arbitrage             CTA Global  Distressed Securities       Emerging Markets  Equity Market Neutral
          0.0086198045          -0.0046696862           0.0058778855           0.0109152240           0.0009596620
          Event Driven Fixed Income Arbitrage           Global Macro      Long/Short Equity       Merger Arbitrage
          0.0054824325           0.0050398011           0.0009638502           0.0044568333           0.0025287234
        Relative Value          Short Selling         Funds of Funds
          0.0000000000           0.0000000000           0.0000000000 

$pct_contrib_MES
 Convertible Arbitrage             CTA Global  Distressed Securities       Emerging Markets  Equity Market Neutral
            0.21455894            -0.11623499             0.14630875             0.27169512             0.02388732
          Event Driven Fixed Income Arbitrage           Global Macro      Long/Short Equity       Merger Arbitrage
            0.13646538             0.12544767             0.02399157             0.11093679             0.06294345
        Relative Value          Short Selling         Funds of Funds
            0.00000000             0.00000000             0.00000000

In this case, I equally weighted the first ten managers in the edhec data set, and put zero weight in the last three. Furthermore, we can see what happens when the weights are not equal.

&gt; tmp3 &lt;- CVaR(edhec, portfolio_method = &quot;component&quot;, weights = c(.2, rep(.1, 9), rep(0,3)))
&gt; tmp3
$MES
           [,1]
[1,] 0.04920372

$contribution
 Convertible Arbitrage             CTA Global  Distressed Securities       Emerging Markets  Equity Market Neutral
          0.0187406982          -0.0044391078           0.0057235762           0.0102706768           0.0007710434
          Event Driven Fixed Income Arbitrage           Global Macro      Long/Short Equity       Merger Arbitrage
          0.0051541429           0.0055944367           0.0008028457           0.0044085104           0.0021768951
        Relative Value          Short Selling         Funds of Funds
          0.0000000000           0.0000000000           0.0000000000 

$pct_contrib_MES
 Convertible Arbitrage             CTA Global  Distressed Securities       Emerging Markets  Equity Market Neutral
            0.38087972            -0.09021895             0.11632406             0.20873782             0.01567043
          Event Driven Fixed Income Arbitrage           Global Macro      Long/Short Equity       Merger Arbitrage
            0.10475109             0.11369947             0.01631677             0.08959710             0.04424249
        Relative Value          Short Selling         Funds of Funds
            0.00000000             0.00000000             0.00000000

This time, notice that as the weight increased in the convertible arb manager, so too did his contribution to maximum expected shortfall.

For a future backtest, I would like to make some data requests. I would like to use the universe found in Faber’s Global Asset Allocation book. That said, the simulations in that book go back to 1972, and I was wondering if anyone out there has daily returns for those assets/indices. While some ETFs go back into the early 2000s, there are some that start rather late such as DBC (commodities, early 2006), GLD (gold, early 2004), BWX (foreign bonds, late 2007), and FTY (NAREIT, early 2007). As an eight-year backtest would be a bit short, I was wondering if anyone had data with more history.

One other thing, I will in New York for the trading show, and speaking on the “programming wars” panel on October 6th.

Thanks for reading.

NOTE: While I am currently contracting, I am also looking for a permanent position which can benefit from my skills for when my current contract ends. If you have or are aware of such an opening, I will be happy to speak with you.