Structural “Arbitrage”: Trading the Equity Curve

The last post demonstrated that while Harry Long’s Structural Arbitrage strategy does well on the upside, it’s no free lunch, with drawdowns comparable to the market itself, albeit with a better upside. Although the annualized returns were excellent in comparison to SPY, the drawdowns themselves were in the realm of SPY’s itself. One low-hanging fruit that came to mind to try and improve the performance of the strategy is to trade the equity curve of the strategy with an SMA. Some call the 200-day SMA (aka 10 month) strategy the “Ivy” strategy, after Mebane Faber’s book, that I recommend anyone give a read-through.

In any case, picking up where the last post left off, I decided to use the returns of the strategy using the 60/40 non-adjusted TLT (that is, the simple returns on the close of TLT)-XIV configuration.

Here’s the continuation of the script:

applyWeeklySMA <- function(rets, n=200) {
  cumRets <- cumprod(1+rets)
  sma <- SMA(cumRets, n=n)
  smaCrosses <- xts(rep(NA, length(sma)),
  smaCrosses[cumRets > sma & lag(cumRets) < sma] <- 1
  smaCrosses[cumRets < sma & lag(cumRets) > sma] <- 0
  smaCrosses <- na.locf(smaCrosses)
  weights <- xts(rep(NA, length(sma)),
  weights[endpoints(sma, "weeks")] <- smaCrosses[endpoints(sma, "weeks")]
  weights <- lag(weights)
  weights <- na.locf(weights)
  weights[] <- 1
  weightedRets <- rets*weights

tmp <- list()
for(i in seq(from=100, to=200, by=20)) {
  tmp[[i]] <- applyWeeklySMA(stratTest, n=i)
tmp <-, tmp)
colnames(tmp) <- paste0("SMA_", seq(from=100, to=200, by=20))
origStratAsBM <- merge(tmp, stratTest)
colnames(origStratAsBM)[7] <- "No_SMA"
charts.PerformanceSummary(origStratAsBM, colorset=c("black", "blue", "red", "orange", "green", "purple", "darkgray"), 
                          main="SMAs and original strategy")


returnRisk <- data.frame(t(rbind(Return.annualized(origStratAsBM), maxDrawdown(origStratAsBM))))
chart.RiskReturnScatter(R=returnRisk, method="nocalc", add.sharpe=NA, main=NA)

The first function simply applies an n-day SMA (default 200), and stays in the strategy for a week if the Friday’s close is above the SMA, and starts off in the strategy on day 1 (by contrast, an MA crossover strategy in quantstrat would need to actually wait for the first positive crossover). The rest of it is just getting the returns. Essentially, it’s a very simplified example of what quantstrat does. Of course, none of the trading analytics are available through this function, though since it’s in returns space, all return analytics can be done quite simply.

In any case, here is the performance chart corresponding to testing six different MA settings (100, 120, 140, 160, 180, 200) and the benchmark (no filter)

The gray (original strategy) is basically indistinguishable from the MA filters from a return perspective. In fact, applying the MA filter in many cases results in lower returns.

What do the annualized metrics tell us?

> Return.annualized(origStratAsBM)
                    SMA_100   SMA_120   SMA_140   SMA_160   SMA_180   SMA_200    No_SMA
Annualized Return 0.1757805 0.1923969 0.1926832 0.2069332 0.1850422 0.2291408 0.2328424
> SharpeRatio.annualized(origStratAsBM)
                                  SMA_100   SMA_120   SMA_140   SMA_160   SMA_180  SMA_200    No_SMA
Annualized Sharpe Ratio (Rf=0%) 0.8433103 0.9143868 0.9169305 0.9769476 0.8839841 1.058095 0.8780168
> maxDrawdown(origStratAsBM)
                 SMA_100   SMA_120   SMA_140   SMA_160   SMA_180   SMA_200    No_SMA
Worst Drawdown 0.5044589 0.4358926 0.4059265 0.3943257 0.4106122 0.3886326 0.5040189

Overall, the original strategy has the highest overall returns, but pays for the marginally higher returns with even higher marginal drawdowns. So, the basic momentum filter marginally improved the strategy. Here’s another way to look at that sentiment using a modified risk-return chart (by default, it takes in returns and charts annualized return vs. annualized standard deviations, for the portfolio management world out there).

In short, none of the configurations really alleviated the massive drawdown. At best, you had around 40% drawdown. While the drawdowns are certainly very high, overall, once the risk is factored in, this strategy may give, but it can certainly take on the downside as well. In any case, the performance is superior to SPY, but it’s not as eye-popping as a truncated equity curve that only starts after the crisis passed looks.

Overall, I’ll wrap up this investigation here. Unlike the initial SeekingAlpha backtest, this longer period of time covers both up markets and down markets. Overall, while the initial replication looked promising, looking over a longer time horizon painted a more complete picture. Now it’s time to move on to replicating other ideas.

Thanks for reading.

7 thoughts on “Structural “Arbitrage”: Trading the Equity Curve

    • Well, it was far from my intention to write a “hit job” on a strategy that has a better risk/return profile than the S&P 500 (which is more than can be said for most investment managers), just that everything warrants a second glance.

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  2. Excellent analysis. I’ve found similar conclusions in my own replications and variations. Thanks for taking the time and sharing your results.
    In 1999, TLT (replicated using ^TYX from Yahoo) took a 30% dive. 1998 seems to be a good starting date for replicating anything using TLT/TMF/TMV. (It’s a bit tricky to replicate TLT Adjusted for Dividends using ^TYX (and dividends must be accounted for to replicate TMF/TMV), but it can be done.)

    • Well for the sake of simplicity, I’ll stick to using leveraged TLT, since I doubt using adjusted data is a good idea, since the adjusted definitely overshoots TMF. There’s also the futures 30 year bond which can also serve as a proxy (if an imperfect replication).

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