Combining FAA and Stepwise Correlation

Since I debuted the stepwise correlation algorithm, I suppose the punchline that people want to see is: does it actually work?

The short answer? Yes, it does.

A slightly longer answer: it works, With the caveat that having a better correlation algorithm that makes up 25% of the total sum of weighted ranks only has a marginal (but nevertheless positive) effect on returns. Furthermore, when comparing a max decorrelation weighting using default single-pass correlation vs. stepwise, the stepwise gives a bumpier ride, but one with visibly larger returns. Furthermore, for this universe, the difference between starting at the security ranked highest by the momentum and volatility components, or with the security that has the smallest aggregate correlation to all securities, is very small. Essentially, from my inspection, the answer to using stepwise correlation is: “it’s a perfectly viable alternative, if not better.”

Here are the functions used in the script:


stepwiseCorRank <- function(corMatrix, startNames=NULL, stepSize=1, bestHighestRank=FALSE) {
  #edge cases
  if(dim(corMatrix)[1] == 1) {
  } else if (dim(corMatrix)[1] == 2) {
    ranks <- c(1.5, 1.5)
    names(ranks) <- colnames(corMatrix)
  if(is.null(startNames)) {
    corSums <- rowSums(corMatrix)
    corRanks <- rank(corSums)
    startNames <- names(corRanks)[corRanks <= stepSize]
  nameList <- list()
  nameList[[1]] <- startNames
  rankList <- list()
  rankCount <- 1
  rankList[[1]] <- rep(rankCount, length(startNames))
  rankedNames <-, nameList)
  while(length(rankedNames) < nrow(corMatrix)) {
    rankCount <- rankCount+1
    subsetCor <- corMatrix[, rankedNames]
    if(class(subsetCor) != "numeric") {
      subsetCor <- subsetCor[!rownames(corMatrix) %in% rankedNames,]
      if(class(subsetCor) != "numeric") {
        corSums <- rowSums(subsetCor)
        corSumRank <- rank(corSums)
        lowestCorNames <- names(corSumRank)[corSumRank <= stepSize]
        nameList[[rankCount]] <- lowestCorNames
        rankList[[rankCount]] <- rep(rankCount, min(stepSize, length(lowestCorNames)))
      } else { #1 name remaining
        nameList[[rankCount]] <- rownames(corMatrix)[!rownames(corMatrix) %in% names(subsetCor)]
        rankList[[rankCount]] <- rankCount
    } else {  #first iteration, subset on first name
      subsetCorRank <- rank(subsetCor)
      lowestCorNames <- names(subsetCorRank)[subsetCorRank <= stepSize]
      nameList[[rankCount]] <- lowestCorNames
      rankList[[rankCount]] <- rep(rankCount, min(stepSize, length(lowestCorNames)))
    rankedNames <-, nameList)
  ranks <-, rankList)
  names(ranks) <- rankedNames
  if(bestHighestRank) {
    ranks <- 1+length(ranks)-ranks
  ranks <- ranks[colnames(corMatrix)] #return to original order

FAAreturns <- function(prices, monthLookback = 4,
                       weightMom=1, weightVol=.5, weightCor=.5, 
                       riskFreeName="VFISX", bestN=3,
                       stepCorRank = FALSE, stepStartMethod=c("best", "default")) {
  stepStartMethod <- stepStartMethod[1]
  returns <- Return.calculate(prices)
  monthlyEps <- endpoints(prices, on = "months")
  riskFreeCol <- grep(riskFreeName, colnames(prices))
  tmp <- list()
  dates <- list()
  for(i in 2:(length(monthlyEps) - monthLookback)) {
    #subset data
    priceData <- prices[monthlyEps[i]:monthlyEps[i+monthLookback],]
    returnsData <- returns[monthlyEps[i]:monthlyEps[i+monthLookback],]
    #perform computations
    momentum <- data.frame(t(t(priceData[nrow(priceData),])/t(priceData[1,]) - 1))
    priceData <- priceData[, momentum > 0] #remove securities with momentum < 0
    returnsData <- returnsData[, momentum > 0]
    momentum <- momentum[momentum > 0]
    names(momentum) <- colnames(returnsData)
    vol <- as.numeric(-sd.annualized(returnsData))
    if(length(momentum) > 1) {
      #perform ranking
      if(!stepCorRank) {
        sumCors <- -colSums(cor(returnsData, use="complete.obs"))
        stats <- data.frame(cbind(momentum, vol, sumCors))
        ranks <- data.frame(apply(stats, 2, rank))
        weightRankSum <- weightMom*ranks$momentum + weightVol*ranks$vol + weightCor*ranks$sumCors
        names(weightRankSum) <- rownames(ranks)
      } else {
        corMatrix <- cor(returnsData, use="complete.obs")
        momRank <- rank(momentum)
        volRank <- rank(vol)
        compositeMomVolRanks <- weightMom*momRank + weightVol*volRank
        maxRank <- compositeMomVolRanks[compositeMomVolRanks==max(compositeMomVolRanks)]
        if(stepStartMethod=="default") {
          stepCorRanks <- stepwiseCorRank(corMatrix=corMatrix, startNames = NULL, 
                                          stepSize = 1, bestHighestRank = TRUE)
        } else {
          stepCorRanks <- stepwiseCorRank(corMatrix=corMatrix, startNames = names(maxRank), 
                                          stepSize = 1, bestHighestRank = TRUE)
        weightRankSum <- weightMom*momRank + weightVol*volRank + weightCor*stepCorRanks
      totalRank <- rank(weightRankSum)
      #find top N values, from
      #thanks to Dr. Rob J. Hyndman
      upper <- length(names(returnsData))
      lower <- max(upper-bestN+1, 1)
      topNvals <- sort(totalRank, partial=seq(from=upper, to=lower))[c(upper:lower)]
      #compute weights
      longs <- totalRank %in% topNvals #invest in ranks length - bestN or higher (in R, rank 1 is lowest)
      longs <- longs/sum(longs) #equal weight all candidates
      longs[longs > 1/bestN] <- 1/bestN #in the event that we have fewer than top N invested into, lower weights to 1/top N
      names(longs) <- names(totalRank)
    } else if(length(momentum) == 1) { #only one security had positive momentum 
      longs <- 1/bestN
      names(longs) <- names(momentum)
    } else { #no securities had positive momentum 
      longs <- 1
      names(longs) <- riskFreeName
    #append removed names (those with momentum < 0)
    removedZeroes <- rep(0, ncol(returns)-length(longs))
    names(removedZeroes) <- names(returns)[!names(returns) %in% names(longs)]
    longs <- c(longs, removedZeroes)
    #reorder to be in the same column order as original returns/prices
    longs <- data.frame(t(longs))
    longs <- longs[, names(returns)]
    #append lists
    tmp[[i]] <- longs
    dates[[i]] <- index(returnsData)[nrow(returnsData)]
  weights <-, tmp)
  dates <-, dates)
  weights <- xts(weights, 
  weights[, riskFreeCol] <- weights[, riskFreeCol] + 1-rowSums(weights)
  strategyReturns <- Return.rebalancing(R = returns, weights = weights, geometric = FALSE)
  colnames(strategyReturns) <- paste(monthLookback, weightMom, weightVol, weightCor, sep="_")

The FAAreturns function has been modified to transplant the stepwise correlation algorithm I discussed earlier. Essentially, the chunk of code that performs the ranking inside the function got a little bit larger, and some new arguments to the function have been introduced.

First off, there’s the option to use the stepwise correlation algorithm in the first place–namely, the stepCorRank defaulting to FALSE (the default settings replicate the original FAA idea demonstrated in the first post on this idea). However, the argument that comes next, the stepStartMethod argument does the following:

Using the “default” setting, the algorithm will start off using the security that is simply least correlated among the securities (that is, the lowest sum of correlations among securities). However, the “best” setting instead will use the weighted sum of ranks using the prior two factors (momentum and volatility). This argument defaults to using the best security (aka the one best ranked prior by the previous two factors), as opposed to the default. At the end of the day, I suppose the best way of illustrating functionality is with some examples of taking this piece of engineering out for a spin. So here goes!

mutualFunds <- c("VTSMX", #Vanguard Total Stock Market Index
                 "FDIVX", #Fidelity Diversified International Fund
                 "VEIEX", #Vanguard Emerging Markets Stock Index Fund
                 "VFISX", #Vanguard Short-Term Treasury Fund
                 "VBMFX", #Vanguard Total Bond Market Index Fund
                 "QRAAX", #Oppenheimer Commodity Strategy Total Return 
                 "VGSIX" #Vanguard REIT Index Fund

#mid 1997 to end of 2012
getSymbols(mutualFunds, from="1997-06-30", to="2012-12-31")
tmp <- list()
for(fund in mutualFunds) {
  tmp[[fund]] <- Ad(get(fund))

#always use a list hwne intending to cbind/rbind large quantities of objects
adPrices <-, args = tmp)
colnames(adPrices) <- gsub(".Adjusted", "", colnames(adPrices))

original <- FAAreturns(adPrices, stepCorRank=FALSE)
originalSWCbest <- FAAreturns(adPrices, stepCorRank=TRUE)
originalSWCdefault <- FAAreturns(adPrices, stepCorRank=TRUE, stepStartMethod="default")
stepMaxDecorBest <- FAAreturns(adPrices, weightMom=.05, weightVol=.025, 
                               weightCor=1, riskFreeName="VFISX", 
                               stepCorRank = TRUE, stepStartMethod="best")
stepMaxDecorDefault <- FAAreturns(adPrices, weightMom=.05, weightVol=.025, 
                                  weightCor=1, riskFreeName="VFISX", 
                                  stepCorRank = TRUE, stepStartMethod="default")
w311 <- FAAreturns(adPrices, weightMom=3, weightVol=1, weightCor=1, stepCorRank=TRUE)
originalMaxDecor <- FAAreturns(adPrices, weightMom=0, weightVol=1, stepCorRank=FALSE)
tmp <- cbind(original, originalSWCbest, originalSWCdefault, 
             stepMaxDecorBest, stepMaxDecorDefault, w311, originalMaxDecor)
names(tmp) <- c("original", "originalSWCbest", "originalSWCdefault", "SMDB", 
                "SMDD", "w311", "originalMaxDecor")
charts.PerformanceSummary(tmp, colorset=c("black", "orange", "blue", "purple", "green", "red", "darkred"))

statsTable <- data.frame(t(rbind(Return.annualized(tmp)*100, maxDrawdown(tmp)*100, SharpeRatio.annualized(tmp))))
statsTable$ReturnDrawdownRatio <- statsTable[,1]/statsTable[,2]

Same seven securities as the original paper, with the following return streams:

Original: the FAA original replication
originalSWCbest: original weights, stepwise correlation algorithm, using the best security as ranked by momentum and volatility as a starting point.
originalSWCdefault: original weights, stepwise correlation algorithm, using the default (minimum sum of correlations) security as a starting point.
stepMaxDecorBest: a max decorrelation algorithm that sets the momentum and volatility weights at .05 and .025 respectively, compared to 1 for correlation, simply to get the best starting security through the first two factors.
stepMaxDecorDefault: analogous to originalSWCdefault, except with the starting security being defined as the one with minimum sum of correlations.
w311: using a weighting of 3, 1, and 1 on momentum, vol, and correlation, respectively, while using the stepwise correlation rank algorithm, starting with the best security (the default for the function), since I suspected that not weighing momentum at 1 or higher was the reason any other equity curves couldn’t top out above the paper’s original.
originalMaxDecor: max decorrelation using the original 1-pass correlation matrix

Here is the performance chart:

Here’s the way I interpret it:

Does David Varadi’s stepwise correlation ranking algorithm help performance? From this standpoint, the answers lead to yes. Using the original paper’s parameters, the performance over the paper’s backtest period is marginally better in terms of the equity curves. Comparing max decorrelation algorithms (SMDB and SMDD stand for stepwise max decorrelation best and default, respectively), the difference is even more clear.

However, I was wondering why I could never actually outdo the original paper’s annualized return, and out of interest, decided to more heavily weigh the momentum ranking than the original paper eventually had it set at. The result is a bumpier equity curve, but one that has a higher annualized return than any of the others. It’s also something that I didn’t try in my walk-forward example (though interested parties can simply modify the original momentum vector to contain a 1.5 weight, for instance).

Here’s the table of statistics for the different permutations:

> statsTable
                   Annualized.Return Worst.Drawdown Annualized.Sharpe.Ratio..Rf.0.. ReturnDrawdownRatio
original                    14.43802       13.15625                        1.489724            1.097427
originalSWCbest             14.70544       13.15625                        1.421045            1.117753
originalSWCdefault          14.68145       13.37059                        1.457418            1.098041
SMDB                        13.55656       12.33452                        1.410072            1.099075
SMDD                        13.18864       11.94587                        1.409608            1.104033
w311                        15.76213       13.85615                        1.398503            1.137555
originalMaxDecor            11.89159       11.68549                        1.434220            1.017637

At the end of the day, all of the permutations exhibit solid results, and fall along different ends of the risk/return curve. The original settings exhibit the highest Sharpe Ratio (barely), but not the highest annualized return to max drawdown ratio (which surprisingly, belongs to the setting that overweights momentum).

To wrap this analysis up (since there are other strategies that I wish to replicate), here is the out-of-sample performance of these seven strategies (to Oct 30, 2014):

Maybe not the greatest thing in the world considering the S&P has made some spectacular returns in 2013, but nevertheless, the momentum variant strategies established new equity highs fairly recently, and look to be on their way up from their latest slight drawdown. Here are the statistics for 2013-2014:

statsTable <- data.frame(t(rbind(Return.annualized(tmp["2013::"])*100, maxDrawdown(tmp["2013::"])*100, SharpeRatio.annualized(tmp["2013::"]))))
statsTable$ReturnDrawdownRatio <- statsTable[,1]/statsTable[,2]

> statsTable
                   Annualized.Return Worst.Drawdown Annualized.Sharpe.Ratio..Rf.0.. ReturnDrawdownRatio
original                    9.284678       8.259298                       1.1966581           1.1241485
originalSWCbest             8.308246       9.657667                       0.9627413           0.8602746
originalSWCdefault          8.916144       8.985685                       1.0861781           0.9922609
SMDB                        6.406438       9.657667                       0.8366559           0.6633525
SMDD                        5.641980       5.979313                       0.7840507           0.9435833
w311                        8.921268       9.025100                       1.0142871           0.9884953
originalMaxDecor            2.888778       6.670709                       0.4244202           0.4330542

So, the original parameters are working solidly, the stepwise correlation algorithm seems to be in a slight rut, and the variants without any emphasis on momentum simply aren’t that great (they were created purely as illustrative tools to begin with). Whether you prefer to run FAA with these securities, or with trading strategies of your own, my only caveat is that transaction costs haven’t been taken into consideration (from what I hear, interactive brokers charges you $1 per transaction, so it shouldn’t make a world of a difference), but beyond that, I believe these last four posts have shown that FAA is something that works. While it doesn’t always work perfectly (EG the S&P 500 had a very good 2013), the logic is sound, and the results are solid, even given some rather plain-vanilla type securities.

In any case, I think I’ll conclude with the fact that FAA works, and the stepwise correlation algorithm provides a viable alternative to computing your weights. I’ll update my IKTrading package with some formal documentation regarding this algorithm soon.

Thanks for reading.

18 thoughts on “Combining FAA and Stepwise Correlation

  1. Error in source(“~/.active-rstudio-document”, echo = TRUE) :
    ~/.active-rstudio-document:116:72: unexpected symbol
    115: lower <- max(upper-bestN+1, 1)
    116: topNvals <- sort(totalRank, partial=seq(from=upper, to=lower))[c language

    • Here’s my sessionInfo() . Make sure to have your packages updated to the latest version from R-forge.

      > sessionInfo()
      R version 3.1.1 (2014-07-10)
      Platform: x86_64-pc-linux-gnu (64-bit)


      attached base packages:
      [1] stats graphics grDevices utils datasets methods base

      other attached packages:
      [1] downloader_0.3 Quandl_2.3.2 dynlm_0.3-3
      [4] IKTrading_1.0 Rcpp_0.11.2 quantstrat_0.8.2
      [7] foreach_1.4.1 blotter_0.8.19 FinancialInstrument_1.1.9
      [10] quantmod_0.4-1 TTR_0.22-0.1 Defaults_1.1-1
      [13] PerformanceAnalytics_1.4.3541 xts_0.9-7 zoo_1.7-11

      loaded via a namespace (and not attached):
      [1] MASS_7.3-33 RCurl_1.95-4.1 RJSONIO_1.3-0 car_2.0-21 codetools_0.2-8 digest_0.6.3 grid_3.1.1
      [8] iterators_1.0.6 lattice_0.20-29 lmtest_0.9-33 nnet_7.3-8 tools_3.1.1

  2. Pingback: The Whole Street’s Daily Wrap for 10/31/2014 | The Whole Street

  3. Fantastic work again Ilya! As I said, you have done more to advance (and understand) this work than anyone else.
    I was able to spend a few hours with the code and I tried a few different asset universes. I plan on doing a lot more work on this.The code appears to be quite robust but it will crash if your equities are not all the same length. For example, if you use SPY and BND and other equities that existed before the inception of BND, you can only run this code using equity data from the start date of BND (Jan 30th, 2009). If you leave all the data for the other equities and BND in the list, they will be unequal in length and somewhere in the code, that craters it. So a simple cleaning function to align all the data to the length of the shortest existing equity would make it easier to quickly try different ideas.
    One question (for now): How do you manage ties in the ranking algorithm? I have seen situations were I was using bestN = 4 but on some months, there would be 5 or even 6 assets selected out of a 12 asset universe (the total weight always be 100). Most months it was just 4 that were selected but there were always months where more than 4 were chosen. I suspect when there is a tie between two assets, both are selected.
    I will continue to work with it and try out different ideas and I will let you know what I find.
    Again, thanks for the great work on you blog!

    • Gerald,

      When it comes to the final selection, what you have is a rank of weighted rank-sums. The algorithm then selects the securities with the top N rank values, NOT the top N securities. So in the event of a tie, that is exactly what happens.

      Regarding the crash if there are more recent equities, I suppose I’ll leave a note, or possibly find a way to identify the least-history security and then truncate the data. Thanks for that.

      If you have a blog or other online presence, I’d love to see the way you extend this.

      • Ilya, Thank you for the explanation. I don’t have an online presence as I am just a retail investor with a keen interest in this stuff. I will eventually be looking at the Sharpe space based on different look backs and weightings of momentum, correlation and volatility. I strongly suspect the out-performance is mostly due to the momentum factor. I won’t know until I get a chance to do some serious work with the method.

        I read in your comments on how to extract the weights list – very helpful – thanks! I would also like to get a list of trades. Can you provide me with some guidance on how to return the trade list? My intent is to model the trades and run some Monte Carlo testing on that data. I am working with a few different asset universes. I will email you my results when I get them.

        Again, thank you for your generous efforts. It’s a heck-of-a-lot of work!

  4. Trades? It’s just a monthly re-weighting. There are no signal-based trades in FAA, if that’s what you’re asking. When you get the weights (it’s one of the inputs into Return.rebalancing), it’s just a “sell my stake in what I had, buy the new weights”, each month.

    • What I am calling a trade list is really just a P&L report based on the monthly trades/rebalancing (not the daily P&L). Ideally, the report would have the date that the equities were purchased, when they were sold, the profit or loss and how many days the trade was opened. This gets complicated as some individual equities will be open for many months and others for just a single month. Perhaps it would be better to just model the trades on a portfolio basis instead of an equity basis. That way, there is one trade on the first or last day of every month with a respective profit or loss. I will need more time with the program to see where everything is as I am sure that the Monthly Portfolio P&L is in there somewhere.

      • I suppose what you may want to do then is take the original price data, and subset them on the endpoints. The returns data is derived from price data. So while you won’t be getting blotter’s advanced analytics (after all, this isn’t a signal-based trading system), the P&L shouldn’t be too hard, since you can just take a notional amount of capital (EG $100k), multiply it by the weight every month, calculate the number of shares you bought at the beginning of the month with that dollar amount, and then multiply by the difference at the end of the month. I didn’t include that functionality though, since I tend to think of P&L as something exclusive to the quantstrat world. I’ll be getting back there very soon, however.

      • I need to enter the quantstrat world – something that is definitely on my to-do list. I know you have provided excellent materials to help people get up to speed. I will figure out how to get a monthly P&L. Thank you for your help and timely responses.

  5. Sure thing. You can also try posting to the R-SIG finance mailing list and they may be able to point you to some more resources, as the authorities there are far more knowledgeable than I am.

  6. Um… hello? This strategy has lookahead bias. Based on your code, you are holding positions during a given 3-month period based on whether they exhibited momentum during that same period. I cannot believe you wrote so many articles on this when it doesn’t even work if you lag the positions properly (i.e only hold the positions from the end of the period over which momentum was being measured to the next month). I coded it up myself before noticing your error, and after correcting the lookahead bias it doesn’t work at all. How can you be so careless?

    • I am not quite sure what you’re talking about. I assign my security selection at the very final date of the data I consider for that given time period.

      Here’s the salient line:

      dates[[i]] <- index(returnsData)[nrow(returnsData)]

      That is, no matter which dates I'm looking at, I assign my weights at the VERY LAST DATE, meaning the returns I'd obtain would be ones going forward.

      Furthermore, I've used this particular framework to replicate other papers by this author and my results checked out.

      Where is your interpretation different?

      • “dates[[i]] <- index(returnsData)[nrow(returnsData)]" and where did those dates come from? oh right: the same 'returnsData' table that was used to calculate momentum in the first place.

      • Okay, simple example: I have four months of data, say…Sep-01 through Dec-31. I assign my weights on Dec. 31. So the returns I obtain would be Jan 1 through Jan 31. Does that make it more clear?

      • hmm… you’re right; that’s only the last day. perhaps my implementation made the opposite error somehow. I will check more thoroughly.

      • Yeah, notice that I said nrow(returnsData), which gives me the very LAST day of the index. If I wanted to be a lookahead cheater, I would have assigned the weights on index(returnsData)[1].

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