If that’s the case, Standard and Poor’s might have achieved the “marketing coup” of the year/decade/century (time will tell) with their downgrade of US credit rating last Friday (possibly thanks to a $2 Trillion Mistake).

SGMI: a new Trend Following Index

But this is not the piece of news that caught my attention today.
Standard and Poor’s announced the creation of the S&P Systematic Global Macro Index (SGMI).

With this index, S&P basically intends to track – or rather replicate – the performance from the Managed Futures / CTA space:

London, August 9, 2011 – S&P Indices has launched the S&P Systematic Global Macro Index (SGMI), which aims to reflect price trends of highly liquid global futures, representing the general level of volatility taken by managers in the global macro and managed futures/Commodity Trading Advisor (CTA) space.

This is a rules-based index, which will implement a trend following system to trade a (semi-diversified) portfolio of liquid futures markets, with a fairly standard risk-adjusted equal allocation per sector and per instrument:

The Index is diversified globally across 37 constituents, falling into the six most widely traded sectors– Commodities, Energy, Fixed Income, Foreign Exchange, Short Term Interest Rates and Equity Indices.

The weighting scheme applies an even risk capital allocation across the index by sector and again to each constituent within each sector so that no single sector or constituent drives the volatility of the index.

Other Trend Following Indices

Of course, this is not a new concept. There are several companies tracking the performance of Managed Futures (Newedge, BarclayHedge) with their respective indices – or even “closer to home” the Trend Following Wizards report on this blog.

Even the concept of using a rules-based systematic trend following approach has been implemented before. Apart from the State of Trend Following monthly report, also on this blog, long-time readers will recall this Beta of Managed Futures paper by Conquest Capital Group, which developed a mechanical trend following benchmark aimed at replicating CTAs performance. I discussed the paper more than a year ago in this “Betafication of Alpha: towards a Commoditization of Trend Following?” post, and was asking then “when the launch of a Trend Following ETF?“.

The end game of Conquest is of course to market their fund implementing this benchmark and it is pretty certain that the end game with this S&P index is similar. One can only speculate (again) as to how long it will take to see a SGMI Trend Following ETF.

The underlying strategy in this new S&P index seems to contain an adaptive timeframe logic (whereas the Conquest Capital Group benchmark uses a combination of several fixed timeframes to capture trends at different levels):

Uniquely, the trend-following model used [by the SGMI] to determine the position of each constituent is flexible enough to allow a customized time-period for each constituent on a monthly basis, unlike models which are based on fixed time-periods. This means that if a longer term trend is driving the market, the Index reflects that, but if a shorter-term trend becomes significant the Index picks that up, using an iterative process to test the stability of each trend.

Trend Following ETFs: a Viable Possibility?

Of course, despite the potential appeal of strong replication and much lower fees for a “speculative” SGMI ETF, this is not necessarily a winning proposition for the investor. The debate resides in whether CTA managers can justify their fees.

Using a Geometric Information Ratio calculation based on the Conquest benchmark, I showed previously that CTAs still produce alpha over the benchmark on a net-of-fees basis – most likely thanks to their research in other areas such as enhanced rolling methodology, execution efficiency, extra diversification (100+ markets instead of 37), etc.
Note that this calculation was not free of survivorship bias though.

This could be one of the reasons why the Conquest fund has not taken off in any substantial manner (less than $200M in AUM after 7 years trading).

Another similar Trend Following fund is the Cambria Global Tactical ETF (GTAA), run by Mebane Faber. Unlike CTAs it does not trade futures (it is an actively managed “ETF of ETFs”), however its core strategy uses long-term trend following principles to allocate funds between 50 to 100 underlying ETFs representing different global asset classes.

It only launched late last year and is already at the same levels of AUM as the Conquest fund, showing that there is an appetite for this type of product in an accessible format (i.e. no high minimum account size).

A fact also highlighted in the S&P press release:

“Issues like high minimums and high fees have made it difficult for many investors to gain access to global macro and managed futures strategies. We envisage that new products based on this index will give investors the ability to invest in a long/short, comprehensive set of the main futures contracts.”

“Gimmicky” Index and ETF?

Josh Brown and Abnormal Returns argue that, in a case of the tail wagging the dog giving birth to puppies, there is a plethora of index creation, in the sole aim to support “niche and gimmicky ETFs” – quickly turning into “zombie ETFs” destined to populate the ETF Deathwatch list.

It will be interesting to see if the “SGMI ETF” is one these “puppies” or whether it can create a substantial impact in the Managed Futures and Trend Following industry – as its great-grandfather the SPY did within the wider investment industry (trading volume: $80B yesterday).

What To Think of it?

It obviously depends on the actual performance of this index and its derived products, so only time will tell.

CTAs: Do you see this development as a threat to your business, or as a way to bring more awareness to the sector? Are you feeling confident about the added value that you can provide to fend-off this potential new competition?

Investors: Are you excited at the prospects of being able to invest in managed Trend Following without the big “minimum account size” hurdle? Are you skeptical of the capacity of the index to replicate the performance of Trend Followers?

Independent system developers: Does this reinforce your belief into building your own system? Would you reconsider building a system if you could invest in an ETF-like Trend Following product?

I’m keen to hear your opinions… Feel free to contribute to the discussion in the comments section below.
 
 
UPDATE: Tim Pickering from Auspice Capital has commented below but also expressed his (interesting) point of view from a CTA perspective on their blog. In essence they do not feel threatened as they “believe in the separation of the alpha and index methods” (read the post here to see why). He also pointed out that:

S&P is surely not the first to do this. In fact, their previous effort has been widely regarded as unsuccessful from a performance standpoint. The S&P DTI (and related) indices have been around for some time now.

 
 
UPDATE 2: Several Trend Following investment products (ETF, ETN, etc.) already exist actually.

Prompted by reader Pumpernickel’s comment (below), I decided to research more about the DTI index that was mentioned by Tim Pickering in his blog post. I was under the impression that a trend following ETF was a really new concept, but it appears that some products very similar already exist. Below is a summary from this “google” research:

The “Diversified Trends Indicator” (DTI) has been created by Victor Sperandeo, with a few investment products having been launched based on this index (or one of its sub-indices: the “Commodity Trends Indicator” [CTI] and the “Financial Trends Indicators” [FTI]). The DTI is a based on a single mechanical trend following/momentum strategy applied to a portfolio of 24 US futures.

From Alpha Financial Technologies (Victor Sperandeo’s firm) website:

In 2002, AFT granted Standard & Poor’s the exclusive right to sublicense the indexes to third parties, known as the S&P Diversified Trends Indicator, S&P Commodity Trends Indicator, and S&P Financial Trends Indicator. S&P launched the S&P Diversified Trends Indicator in January of 2004. Over the next two years several offshore products linked to the S&P DTI were launched, including a UCITS III Fund by Nomura International PLC. In 2007, Rydex launched the first long/short managed futures mutual fund, which tracks the S&P DTI. Direxion funds launched a mutual fund that tracks the CTI® and a mutual fund that tracks the FTI™ in 2008 and 2009, respectively. In July of 2008, Merrill Lynch structured an exchange-traded note that tracks the S&P CTI.

In November 2009, AFT commenced licensing its indexes directly to third parties, although existing S&P licenses remain in effect. On August 1, 2010 AFT launched the FX Trends Index™ (FXTI®). In January 2011, Wisdom Tree launched an exchange traded fund (ETF) which tracks the DTI®.

Investment banks and financial institutions in over 15 countries worldwide have licensed AFT’s indexes. As of April 2011, there are over $3 billion invested globally in products utilizing AFT’s indexes.

Tickers for the indices and some of their investable products:

Bloomberg Index Tickers:

• AFT Indices: DTI® TR: DTITR , CTI® TR CTITR , FTI™ TR FTITR .
• S&P Diversified Trends Indicator: Price Return: SPDTP, Total Return” SPDTT.

Investable Products:

• Rydex SGI Managed Futures: RYMFX (mutual fund based on S&P DTI and trading since 2007)
• ELEMENTS S&P CTI: LSC (ETN based on the S&P CTI and trading since 2009)
• Direxion Commodity Trends: DXCTX (mutual fund based on the AFT CTI and trading since 209)
• Direxion Financial Trends: DXFTX (mutual fund based on the AFT FTI and trading since 209)
• WisdomTree Managed Futures: WDTI (ETF based on the AFT DTI and trading since 2011)

The methodology used to calculate the index is made public by AFT – it can be found here as a pdf document.
A document for the S&P DTI can be found there as a pdf document.

I have also found this “interesting discussion” between Attain Capital and Victor Sperandeo on whether DTI-based products really can deliver Managed Futures performance.

In the first “missive” (in this article), Attain Capital argue that the DTI-based products are wrongly labeled as “Managed Futures” as they offer no exposure to the sector directly. They go on to show that the DTI index has seriously under-performed the Newedge CTA Index (and that in turn the Rydex mutual fund slightly underperformed its DTI benchmark due to expenses) since 2007 (total return of 28.85% for the Newedege index vs. 3.36% for the Rydex fund). They also chart the 12-month rolling correlation between the DTI and various CTA indexes. The figure oscillates around 0.3 and 0.9 for an average of 0.6 between 2000 and 2007.

Victor Sperandeo responds that he “developed the Diversified Trends Indicator™ as a way for investors to access the “core” returns embedded within trend-following in the futures markets” and that the DTI does indeed implement a (single) Managed Futures strategy by the way of a mechanical trend following system on a diversified portfolio of futures. He further highlights that the DTI actually out-performed the Newedge CTA Index since 2004 while providing a more practical way of investing in Managed Futures, compared to the un-investable CTA index from Newedge.

In a further reply, Attain Capital highlights that comparison between DTI and Newedge CTA Index should be done on a realistic basis (DTI Total return minus an annual 2% fee with the Newedge index already being net of fees), in which case the DTI slightly under-performs the Newedge CTA index, concluding:

  We don’t believe they will see long term success beating the managed futures indices (or if they are even trying to beat those benchmarks) because they track only a single strategy.

It seems that both have a point – and certainly a different viewpoint (and interests). It actually ties back to the alpha vs. replicated beta “debate” and what place the mechanical and investable index-based products take in the managed futures space.

As Tim Pickering was saying “I believe in the separation of the alpha and index methods. We feel it will bring awareness to CTA and offer product to investors that do not know about/understand or have access to “Accredited or QEP” products. For CTAs that generate Alpha at reasonable price, this exposure can only help you.”

It will be interesting to see – but hard to measure – whether a Starbuck effect additionally develops from these products to boost sales from classic CTAs.

These indices and products are something I had completely missed, so thanks again to the readers who offered pointers to more info.
 
 
UPDATE 3: Trader Vic Index (TVI):

Thanks to reader RB, who pointed out the Arrow Managed Futures Trend Fund, which tracks another similar index created by Victor Sperandeo: the Trader Vic Index (TVI).

The index was developed as a partnership between RBS and Enhanced Alpha Management, L.P. (EAM, LP) – Sperandeo’s CTA firm – and launched in 2009. The Arrow Funds mutual fund offering based on that index started trading in 2010, and has around $100M in AUM.

Tickers: TVICTR:IND (Bloomberg index ticker) and MFTFX (Arrow mutual fund on google finance).

I recently spent more time doing “reading research” rather than “testing research”. As result, this post resembles a collection of links on ideas seen on the web of how to trade futures with a small account – one of the topics I have been interested in.

The Issue: Diversification with Small Account

A small account size – or starting equity – can make it difficult to achieve diversification (a “free lunch” with a high “cover charge” as described in this post – you can read more on diversification and correlation from this blog here and here).

Diversification can be achieved by trading a large number of components in a portfolio, whether “components” represent:

• Instruments
• Systems
• Timeframes

Instruments Diversification

“Instruments” is usually the first aspect that comes to mind when thinking about diversification.
Including more assets/markets/instruments in a portfolio is often described as the “free lunch” – and this is one of the main reasons why large CTAs often include upwards of 100 markets in their portfolio selection.

A small account most likely cannot trade a portfolio of 100+ instrument. This is an issue that Dean Hoffman tries to address in this article: The Conundrum of Small Managed Futures Accounts.

Noting that most diversified trend follower CTAs have a minimum account size of at least $1M, Hoffman describes the advantages of trading larger accounts (able to trade many instruments including those with high margin requirements, more granular position sizing with contract scaling).

Hoffman then describes Dynamic Portfolio Selection as a potential solution for small accounts to achieve increased results from a “virtual high diversification”. The system monitors a large set of instruments but instead of taking all signals (as a diversified trend follower would most likely do), it evaluates and ranks each instrument relatively (based on each market’s potential on a risk-adjusted basis), resulting in about 90% of trading signals being filtered out. This naturally cuts down the number of positions held at the same time, and consequently the required account size.

As this is mostly a “marketing” article for Hoffman’s CTA offering (implementing this concept), there is not much more information on what sort of filtering is applied to select the “best” signals but the general idea is worth investigating (and you can check for yourself whether their performance seems to hold up against the theory).

The subject of dynamic portfolio selection has also been covered in the inevitable Trading Blox forums in this “Dynamic Portfolio Selection” post started by Dean Hoffman himself.

A couple of posts on this blog also describe potential filtering ideas based on relative market volatility and higher-level trend direction.

This idea of filtering trades is not new: the Turtles used to use the concept decades ago, as mentioned by TB forum user sluggo in this post (which contains a link to Trading Blox code implementing similar “heat limitation” mechanism).

Systems (and Timeframes) Diversification: Swarm Behaviour

Combining several systems is also a possibility to achieve diversification. With the extra advantage that it is possible – to some extent – to design systems and control their correlations to the rest of the suite of systems (as opposed to markets, which can have a furious tendency to correlate to +1 or -1 during crisis times).

And as we all know, correlation is a key element of the “diversification benefits” equation (check this thread from user sluggo on TB forums for a good presentation/discussion on the topic).

Adding a profitable mean reversion/counter-trend system to a trend following system will, in all likelihood, reduce the volatility of the combined portfolio, thanks to the negative correlation that it brings. Adding many uncorrelated systems is likely to increase this positive effect.

However, trading a diversified suite of systems has a similar constraint to trading a large portfolio: it increases the required account equity.

A comment from Pumpernickel on a recent post from Quantum Financier (who is starting a series of posts on “signal aggregation: how we form and use an ensemble of signals isolating different pieces of information to build a profitable strategy” ) pointed to a couple of documents from Fall River Capital.

The (pdf) document (part 1 and part 2 of their white paper) describe how they tackle this issue on a large scale, by trading hundreds to thousands systems simultaneously, using the concept of swarm behaviour (which can be seen throughout the natural world, such as in the mesmerising starling flights in the English Somerset Winter, pictured above).

From the white paper (other Fall River white papers and general website are also interesting to read):

An […] approach is to assign each trading system a vote. Each model is polled for its position (long, short, or out) daily, and the total is aggregated into a tally that may be thought of as a “Vox Populi,” or crowd opinion poll. Research showed that aggregating the systems by this simple tally method was a quite workable approach, allowing us to “cheat” by holding a single position per market rather than hundreds or thousands. Regardless of the number of component models, the master strategy holds a position in accordance with the majority of the crowd.

How they choose the models/systems to be included in the portfolio is mostly driven by each system’s correlation to other systems:

The portfolio of individual candidate systems consists of between several hundred and a few thou‐ sand members that share both low correlations to one another and robust returns over many years of market history. The result is a “swarm” of trading models, each attacking the market from a different direction. This process of system development, evaluation, and selection does not prioritize superior standalone system performance, but rather seeks to uncover profitable trading rules that complement one another when implemented together.

Their testing results seem to show that this approach tracks fairly well an “equal allocation” approach with hundreds/thousands of systems, which itself benefits greatly from low correlated system diversification (reduced volatility, or increased vol-adjusted returns).

This “systems voting” strategy has also been discussed on the TB forums there (again started by user sluggo…).

Other Alternatives

These are ideas to stimulate research on how to alleviate the “futures trading diversification with a small account” issue. Other ideas can also be found on other threads from the TB forum (examples 1, 2 and 3 – search the forum for more discussions), showing that the topic is a “popular” one.

Another alternative would be to move away from trading actual futures but instead focus on “proxy” instruments such as ETFs (see this post for a quantification of how ETFs can track futures) or spread betting (they usually offer lower minimum trading lots, allowing for lower required trading equity, but can have other disadvantages, such as counterparty risk, less instruments available or cost of funding/leverage). Another trade-off to make in system/strategy design..
 

I have already covered the idea of using ETFs in place of Futures. Today, I wanted to run a quantitative comparison between the two instrument types

The ETF sector has been growing at an impressive rate, with new offerings popping up every month and providing a wider choice in selecting a portfolio to trade. One of the drawbacks of using ETFs for mechanical trading strategies is the relative short history of these instruments, making it hard or impossible to run to back-tests looking back far in the past – which I believe is critical to understand how a strategy works over different types of market conditions.

In A Practical Guide to ETF Trading Systems, author Anthony Garner uses futures contracts as a proxy for testing strategies on ETFs. If futures are good proxies for ETFs, the back-test results from a long run with futures can be extrapolated to the ETF world.

This post is attempting to look at one way of quantifying how well futures can act as a proxy.

Quantification by Correlation

The main “tool” used in this post, to test whether futures are good proxies to ETFs, is that of correlation between prices as well as returns. The assumption being that correlations between an ETF and a good candidate futures proxy must be as close as possible to 1.0. The closer to 1, the better the proxy relationship.

I picked a few ETFs and associated futures and calculated the correlation (using Pearson product-moment) between several of the data attributes:

• Open
• High
• Low
• Close
• t-1 Return
• t-5 Return
• t-30 Return

The data considered in each case was the list of all dates where both futures and ETF had data. The futures contract were proportionally back-adjusted (to keep price ratio at correct values, as discussed in this post).

The table below shows the number of records for each comparison, the start date and the various correlation coefficients.

Note: please make sure you view this post with your “browser zoom” at 100% for the numbers to be readable.

Most of the numbers are pretty close to 1, which seems to validate the idea of using the future contract as a proxy for ETF.

Some underlyings can be traded through different ETFs (or ETNs). Crude Oil, for example, has several ETFs aiming to capture its performance (I picked DBO, OIL and USO for this test). We can see that there are some differences between the correlation figures of these three ETFs. Not all ETFs seem to track the underlying as well….

A more obvious case of divergence is between the two Cotton ETFs: BAL and COTN (traded in London). The COTN ETF has a low correlation reading on previous-day returns (0.319). Obviously, the fact that the two instruments are trading in different timezones (London vs. NY) will cause their daily performance returns to differ as the market still moves after the London close. This would be something to also keep in mind when choosing an ETF/future pair.

Note on Correlation: Pearson vs. Spearman

I have chosen the Pearson correlation method for these calculations, as the linear relationship between the variables under comparison was what I felt needed to be looked at. However there are several “limitations” of using this correlation calculation. One of them is the assumption of the data being normally distributed, resulting in the calculation being very sensitive to outliers.

This is best illustrated with the correlation of “t-1 Returns” between the COTN ETF and its corresponding Cotton future contract. Back in September 08, the ETF price dropped by 60% before gaining 75% the next day. I seem to remember this was related to a scare around AIG-backed ETFs/ETNs, when the insurance giant was rumoured to go bankrupt.

For the purpose of a test, I simply removed these 2 extreme returns from the file and re-ran the correlation calculation. The figure came in at 0.518. A large increase, obtained by simply removing two outliers. This highlights the point about Pearson correlation not being the most robust with regards to outlier data points.

There are other methods for calculating correlation, and Spearman’s rank method does not make any assumptions on the distribution of the data. It can also detect some non-linear relationship (which we were not interested here). Nevertheless, I re-ran the whole set using this alternative method. The figures were approximately identical – except for the case of COTN.

The Spearman correlation coefficient for COTN v CT using both original and modified files were near-identical at around 0.61. No big jump by removing two outliers.

Note on ETFs

ETPs (Exchange Traded Products), be they called ETF, ETN or ETC all come in different flavours. They can be structured in different ways and track different benchmarks, even when they are associated to the same market. They also add an extra layer and therefore additional counterparty risk on the intermediaries, costs/fees and tracking error.

A good example of the differences between some products can be found in this ETFdb article: USO vs. OIL: A Better Crude Oil ETF?

Finally, it is not (yet?) possible to trade the equivalent of a fully diversified futures portfolio with ETFs, as all markets are not represented in the ETF universe.

Appendix: R Code

Once again, I used R to generate the various correlation calculations. The idea being that one it is coded up it can be applied very quickly to any data.

The code takes a parameter file containing the files to be compared (1st column = description, 2nd and 3rd columns = file names) and the correlation calculation type. See sample here.

Here is the code below. Hopefully the comments are clear enough to help you understand the logic. This might not be the most perfect way to implement this but it does the job:

First, it defines a function to perform the calculations on two files (ie ETF vs. future):

# Define correlETFFut function
correlETFFut <- function(params, i, cor.method,...) {
 
  colsFut <- c("date", "O", "H", "L", "C", "Vol", "OI", "Con", "UC")
 
  # Adding headers
  cols1 <- colsFut
  cols2 <- colsFut
 
  # Read in data sets
  data.1 <- read.csv(toString(params[i,2]), col.names=cols1)
  data.2 <- read.csv(toString(params[i,3]), col.names=cols2)
 
  # Join both data sets
  data.merged <- merge(data.1, data.2, by.x="date", by.y="date")
 
  #Calculate correlation on Open, High, Low and Close
  cor.O <- cor.test(data.merged$O.x, data.merged$O.y,method=cor.method)
  cor.H <- cor.test(data.merged$H.x, data.merged$H.y,method=cor.method)
  cor.L <- cor.test(data.merged$L.x, data.merged$L.y,method=cor.method)
  cor.C <- cor.test(data.merged$C.x, data.merged$C.y,method=cor.method)
 
  #Calculate correlation on t-1 Rtn
  log.diffs1 <- log( data.merged$C.x[2:sum ( !is.na ( data.merged$C.x ) )]/data.merged$C.x[1:(sum ( !is.na ( data.merged$C.x ) ) - 1)])
  log.diffs2 <- log( data.merged$C.y[2:sum ( !is.na ( data.merged$C.y ) )]/data.merged$C.y[1:(sum ( !is.na ( data.merged$C.y ) ) - 1)])
 
  cor.Rtn <- cor.test(log.diffs1, log.diffs2,method=cor.method)
 
  #Calculate correlation on t-5 Rtn
  log.diffs1W <- log( data.merged$C.x[6:sum ( !is.na ( data.merged$C.x ) )]/data.merged$C.x[1:(sum ( !is.na ( data.merged$C.x ) ) - 5)])
  log.diffs2W <- log( data.merged$C.y[6:sum ( !is.na ( data.merged$C.y ) )]/data.merged$C.y[1:(sum ( !is.na ( data.merged$C.y ) ) - 5)])
 
  cor.RtnW <- cor.test(log.diffs1W, log.diffs2W,method=cor.method)
 
  #Calculate correlation on t-30 Rtn
  log.diffs1M <- log( data.merged$C.x[31:sum ( !is.na ( data.merged$C.x ) )]/data.merged$C.x[1:(sum ( !is.na ( data.merged$C.x ) ) - 30)])
  log.diffs2M <- log( data.merged$C.y[31:sum ( !is.na ( data.merged$C.y ) )]/data.merged$C.y[1:(sum ( !is.na ( data.merged$C.y ) ) - 30)])
 
  cor.RtnM <- cor.test(log.diffs1M, log.diffs2M,method=cor.method)
 
  #Put it all into cor.all
  cor.all <- c(sum ( !is.na ( data.merged$C.x )), data.merged$date[1], signif(cor.O$estimate,5), signif(cor.H$estimate,5), signif(cor.L$estimate,5), signif(cor.C$estimate,5), signif(cor.Rtn$estimate,5), signif(cor.RtnW$estimate,5), signif(cor.RtnM$estimate,5))
  return(cor.all)
 
}

Second, a function reads the parameter file of all files to be compared, iterates through it and call the first function for each pair of files, building an array of all results:

# Define fullCorrel function
fullCorrel <- function(file, cor.method,...) {
 
  # read input file
  comps <- read.csv(file, header=F)
  for (i in 1:dim(comps[1])[1]){
    cor.tmp <- correlETFFut(comps,i,cor.method)
    if (i==1) cor.full <- cor.tmp else cor.full <- cbind(cor.full, cor.tmp)
  }
 
  rownames(cor.full) <- c("Record Count", " Start Date", " cor Open", " cor High", " cor Low", " cor Close", " cor Rtn", " cor RtnW", " cor RtnM")
  colnames(cor.full) <- t(comps[1])
 return(t(cor.full))
}

Finally, set your working directory (where all the files are) and call the second function with the relevant parameters (here the function is called twice with the same parameter file and two different correlation methods (make sure to update the working dir to your own path):

# Set Working Dir - Replace with your own path!
setwd("D:/ATS/Blog/Posts/ETF v Futures/Data")
 
# Run Pearson correlations
fullCorrel("ETFvFut.txt", "pearson")
 
# Run Spearman correlations
fullCorrel("ETFvFut.txt", "spearman")

Below are included some sample files to run the code above:
Sample_Data

Futures have been the main focus on the Au.Tra.Sy blog (in terms of instruments) but the ETF side of things has grown quickly very over the last decade and they do offer an alternative worth looking into.

Smaller Trading Size = Greater Diversification

I am a great believer in diversification. I think it is an “easy” way to improve performance and some even think that:

The only free lunch available in financial markets is diversification.

Diversification presents the trader with the advantage of having to handle less downside heat, or alternatively, if he can handle the heat, he can upsize his portfolio and capture more profit for the same heat.

This last quote is an extract from Ed Seykota’s page on diversification, with a simple demonstration of how diversification improves the results of 2 systems when combining them.

One problem with trading a diversified futures portfolio (50+ instruments) is the relatively high minimum account size because of the high notional size of the contracts. This is just for minimum account size. A larger account is usually more desirable, to enable more granular position sizing options (ie choosing between 1 to 10 contracts for example, instead of 1 or 0 or for scaling in/out of positions…).

But diversification does not only involve the choice and number of instruments in the portfolio. You can diversify even more by trading multiple systems and multiple timeframes. This renders the required starting account size problem even worse… Diversification is a “free lunch” only if you can afford the “cover charge”.

As a side note, I believe this is one of the reasons why managers like BlueTrend, who trade as a big fund (AUM in billions) are able to achieve their level of performance (18.71% CAGR with 12.56% MaxDD).

A perfect illustration of the minimum lot difference can be found when looking at the S&P 500 index:

• The E-mini S&P 500 futures contract (ES) trades at around $55,000 (US$50 times the value of the S&P 500 stock index) for a margin requirement over $5,000.
• The corresponding “Spyder” ETF (SPY) is trading at just over $100

Trading ETFs can drastically lower the required account size for a system, allowing for a wide diversification by combining multiple systems on multiple timeframes on a large portfolio.

Range of instruments

There are now literally hundreds of ETFs, which would make trading a diversified portfolio using ETFs a real possibility. I have not yet taken the time to try and map a list of futures to their equivalent ETFs, but I suspect that a fair proportion of them would be available (and potentially more as illustrated by Lithium or Water ETFs). A good starting point for this would be the ETF database or similar (numerous) websites.

Of course the “explosive growth” of ETFs, including obscure ones, can create liquidity issues for the ETFs trading in thin markets. Not every ETF trades at $20B average daily volume (as is the case for SPY).

Another downside of trading with ETFs is their relative recency and lack of trading history (and therefore historical data available for back-test). This can be mitigated in most cases by using proxies – in the form of corresponding instruments such as futures contracts – but is obviously not as accurate as using the instruments themselves.

Operational Complexity / Roll-Overs

One of the characteristic of futures trading is the roll-over and choice between different available contracts. This is more complicated to handle (than your average stock for example) but allows for more fancy/interesting strategies.

ETFs, on the other hand, usually use futures as the underlying source of exposure. The mechanism for roll-over is handled by the ETF, which wrap the whole operation in one continuous security. The operational management of the roll-over is done for the investor. This however comes at a cost: direct (ETF fees) and indirect (lack of flexibility in approach).

There are also other issues with ETFs potentially impacting the futures markets (contango and backwardation rates) and suffering from their own market impact during roll-over periods (discussed here and here)

An ETF is a bit like outsourcing the futures roll-over process: it will not be as good as if you did it yourself but somebody else will save you the hassle of having to manage the process.

Lack of leverage and a Word on Volatility Decay

An obvious advantage of futures trading is the inherent leverage that it offers: for an initial margin of less than $10,000, you could control 1,000 barrels of crude oil (about $75,000 worth at the current price levels).

On the other hand ETFs trade very similarly to stocks and do not offer any sort of embedded leverage. Of course, as for any stock, you can trade “on margin” to add some leverage but this is never as high as the leverage on offer with futures and comes at a cost (broker margin rate).

There are of course leveraged ETF offerings (2x or 3x usually), but these suffer from another type of cost in the form of volatility decay, which make them less than ideal for longer holdings.

This is mainly due to the way the ETFs are rebalanced every day to replicate double or triple the rate of daily return. This can be illustrated by a very simple comparison example between an ETF and its leveraged counterpart.

Consider 2 daily returns of -10% and +11.11%. The standard ETF will end up unchanged ([1-0.1] x [1+0.1111] = 1) whereas a 3x leveraged ETF would end up losing 6.67% (([1 - 0.1x3] x [1 + 0.1111x3] = 0.93331).

This is a concept that has already been covered in this leverage post.

Closing Bell

I have noticed that the blogosphere has embraced ETFs in a very similar explosive fashion to the ETFs themselves. They do indeed offer an interesting alternative to futures trading and I will be looking more into it. I believe the main deciding point will be weighing up the additional diversification options from a much smaller minimum lot size versus their main downside: reduced leverage. Although this last point has been covered in A practical Guide to ETF Trading Systems.

I’d be curious to hear what your experiences are on trading ETFs, so please share them via the comments section.

I have been going on about roll yield and term structure for a few posts, and through two very concrete examples we’ll see how it can affect your trading and system development

A reader recently mentioned a paper (pdf by Sloyer and Tolkin) presenting a theoretical trading strategy which improves the risk-return profile of standard equity-bond portfolio by adding allocation to equity volatility represented by the VIX index. The idea sounds good on paper (no pun intended), but a “small” assumption might render the strategy impossible to implement practically:

VIX futures can realistically be included as an asset in a passively managed portfolio as the futures can be rolled relatively cheaply from one contract to the next as each contract expires.

The Current VIX Situation

Taking a look at the current VIX futures curve clearly invalidates the assumption above:

VIX futures curve - click to zoom in

At the current levels, the contango rate is over 100% annualized – definitely no “relatively cheap” roll yield. As we’ve seen with the contango exhibited in Crude Oil in 2009, futures performance failed to match the spot price – and with such a high contango rate in the VIX futures, the same would happen: spot price returns would be “eaten away” by the negative roll yield. Indeed, prices would have to raise by an annualized 100% just to counter the contango.

Sometimes a good theoretical idea fails at the practical implementation stage…

Mexican Peso: Same Concept, Opposite Effect

Coincidently, another reader was offering me a friendly warning regarding the use of spot market to drive signals for a futures trading strategy – as was described in Better Trend Following through improved Roll Yield (note: for practical reasons, the test in that post was done using front-month contract as a proxy for the spot market).

In effect, as is the case in the VIX futures, the roll yield part of the total return sometimes trumps the spot price moves. In these cases, looking solely at the spot market can be flawed.

This is very well illustrated by this Peso chart sent from our reader:

The black curve almost continuously going down is the spot price, whereas the blue curve is a continuous futures contract (back-adjusted using front-month contracts) - click to zoom in

The strong divergence between both series meant that an investor/trader going short on the Peso, would have been “right” (ie. the Peso unarguably went down), yet would have lost money if using futures to implement her trade (as highlighted by the futures continuous contract going up).

This is another case where the roll yield has a stronger impact than the spot price move.

Two Conclusions

Despite the spot price usually grabbing most of the attention, the roll yield can be the driving factor to a futures market’s total return. This can seem counter-intuitive but on long-term timeframes, roll yield explains most of the market’s performance (as discussed in this separating the wheat from the chaff paper I have linked to previously).

When looking at potential trades (in futures, or ETFs, which are sometimes no more than futures “wrappers” or even forex with cost of carry), one should weigh the spot price return potential against the term structure implied return. From a system development point of view, an idea might be to add a portfolio ranking/filter based on the implied yield vs. ATR (or other measure of “volatility”).

The second conclusion is that the market seems to reward those who trade against conventional wisdom. In that period covered by the Peso chart, most people were worried about further devaluations and were paying dear to hedge against this outcome. The hedge ended up costing them more than the cost of holding on to a devalued Peso. Alpha is finite and flowed to the minority being long the falling Peso…

Maybe time to short the VIX futures?

DB Optimal Yield Index

This idea of optimising roll yield is not a brand new approach, however I have never seen it applied to an active trading strategy.

In fact, I have only seen it applied in the Deutsche Bank Commodity Index (exact name is a mouthful: Deutsche Bank Liquid Commodity Index – Optimum Yield Diversified Excess Return – which I suspect has really only been devised to underlie their ETF fund tracking it).

Deutsche Bank seems to have taken on-board the fact that roll yield represents a non-negligible aspect of futures/commodity investing. From the index/fund website:

The Index is a rules-based index composed of futures contracts on 14 of the most heavily-traded and important physical commodities in the world.

Optimum Yield describes the process by which expiring futures contracts in the Index are replaced with new futures contracts. The Optimum Yield process seeks to pick the futures contract expiring in the next thirteen months that has the highest implied roll yield.

In effect, since the fund is always long, it tries to buy the contract which offers the highest rate of backwardation, or at least the lowest rate of contango.

DB do seem to produce some excess return through that process, as displayed by this comparative chart, taken from their marketing material:

Optimal Roll Yield Trend Following

I wanted to check how a similar concept would perform on an active trading strategy such as a Trend Following system. Typically, in mechanical futures trading, one usually uses the front-month contract — makes it easier to backtest (only one back-adjusted continuous time series to handle) and simpler to trade (only one contract to monitor and trade).

However, in a new optimal roll yield approach, for each trading signal to buy or sell, one could have a theoritical choice to trade any available contracts and their associated maturities. For any given date where a trading signal occurs, one could check the futures contracts yield curve and determine the contract which will optimise the roll yield (highest rate of backwardation, or at least the lowest rate of contango for a BUY signal and the opposite for a SELL signal).

The Methodology: MA Cross-over 50/20 with Optimal Roll Yield

The two components of Trend Following return we are dealing with here are the returns from the spot price beta moves and the roll yields from the futures contracts (TF returns breakdown here).

The idea is to generate the Trend Following signals based off the spot price movements and for each new signal, compute the yield curve to identify the contract which offers the most attractive roll yield (depending on the signal direction). For this example, I picked a very standard cross-over system using 50-day and 20-day MAs.

The process sequence looks like this:

1. Generate the Trend Following strategy signals based off the spot price movements (ie crossovers between the spot price 50-day and 20-day MAs); and for each new signal:
2. Compute the yield curve to identify the contract which offers the most attractive roll yield (depending on the signal direction).
3. Buy/Sell that contract
4. Hold the position until either: 1) the contract expires (roll-over) or 2) the position is reversed (new signal + yield curve computation to pick the best yielding contract)

The lookup for the “best” contract is limited to 12 months in the future.

Note that roll-overs should happen less frequently than with a standard approach (because you might buy a contract maturing in 12 months and hold it for the full 12 months – as opposed to rolling over to the front-month contract every month).

The Results for Crude Oil

Because I coded some of the test algorithm outside of Trading Blox (see p.s. below for more details), I decided to keep it simple to start with, and ran the test on one instrument only, keeping working with Crude Oil (since it instigated this series on roll yield).

As a reference point, the performance of the 20-50 MA cross-over system on front-month contracts (“standard” approach) returned a CAGR of 10.25% with a MaxDD of 46.85% and an annualized Sharpe ratio of 0.37 (no trade costs or slippage included in the test).

OK – enough introduction, here are the comparison results:

The chart shows it pretty clearly and the summary table confirms it:

 
The optimal roll yield approach seems to improve the overall system significantly, whatever metrics you wish to pick for comparison. Pretty pleasing results…

Volume and Slippage Considerations

However, there is an important aspect about trading in the front-month only: liquidity. And with liquidity come better fills and lower slippage — which can greatly impact trading system results.

My initial assumption was that if the optimal yield concept was viable for a large player like DB to run a fund with, I should not worry about liquidity for a similar approach with Trend Following. By checking the actual volume figures for each contract bought/sold with the strategy, I quickly realised that some trades had been made on days with very low volume (ie <50) and "only" 83% of trades on a daily volume over 1,000. Oops, was I just chasing an elusive unicorn? A theoritical result impossible to to apply in practical real-life trading...

Adding a liquidity filter to the roll yield algorithm would allow to reject contracts for which daily volume is too low and avoid liquidity problems. How much would it affect performance, though?

Not too much actually:

The filter is pretty simple: when it computes the yield curve and checks for the contract with the best roll yield, it only considers contract months for which daily volume is over 5,000.

And for completeness, the table summarizing the three tests undertaken:

Note that even with the liquidity filter, slippage might still be a bit better in the front-month contract, as this is where a big chunk of the trading is concentrated. However, real-life testing is the only way to verify and quantify this difference.

To get an idea of how slippage would affect the system performance in general, I ran the standard approach system as a backtest in Trading Blox, with slippage set at a pessimistic 25%. Under these conditions, the system performance (CAGR) dropped “only” by 2.5 percentage points.

Conclusion

One of my main concerns regarding this strategy was the potential loss in “raw price moves” (ie the fact that price trends would not propagate as well in alternative contract months), but the strong correlation between the standard and optimized approach seems to indicate that improved roll yield return does not come at the cost of beta spot price moves return, therefore providing a direct bonus.

It is quite evident that liquidity can become an issue and that a liquidity filter should be employed at a minimum. Moreover Crude Oil, used for this example, is one of the largest traded physical commodity. Other products might not offer enough liquidity depth, far in the yield curve. DB, however, can implement its optimal yield approach over 14 different instruments, which indicates that there is scope for this approach to be employed on additional products to Crude Oil. I believe such approach could have its place in a fully diversified Trend Following system – but only applied to the most liquid instruments.

Finally the optimal approach might generate some additional slippage compared to the traditional approach. However, this extra slippage cost should still be outweighed by the extra roll yield return, as evidenced in the Trading Blox slippage impact test.

Epilogue: Techie’s corner

In terms of techical implementation, this is slightly more complicated than standard back-testing because each instrument must use multiple price streams (for each individual contract) and cannot be handled by standard back-testing packages (that I know of, or without heavy customisation).

To avoid re-developing a back-testing package from scratch, I used my trusted copy of Trading Blox to generate the “standard/non-optimal roll yield” MA cross-over system output for a single instrument (Crude Oil), which output several files providing the dates of the signals as well as other useful computations such as position sizing with number of contracts and running Total Equity values. Using this information, I ran a second pass of processing, by reading the signals and other info generated by Trading Blox, and looping through the individual contract data in order to pick, for each entry signal, the best contract on the yield curve (this second part was coded outside of Trading Blox).

 
Credits: Thanks to the Trading Blox forum members to help discuss the subject of this post on this thread, and especially svquant for pointing out the DB Optimum Yield commodity index.
 
 
*Note: the Modified Sharpe ratio is as per Jack Schwager’s definition in Managed Futures, Myths and Truths, which introduces interesting performance metrics. The Modified Sharpe ratio is simply a Sharpe ratio where Rf (risk-free rate of return) is set to 0 (makes it independent of leverage). mSR = E[R] / sd.

We have seen previously that backwardation and/or contango can induce a fairly large drift between the performance of an instrument’s spot market and its corresponding futures market.

This phenomenon can be described as roll yield of futures trading and I suggested it was one of the four components in Trend Following returns. As per that last post’s conclusion, breaking down the returns should allow to focus, study and improve a trading system’s individual component – and this is what we are doing here with the roll yield, by looking into the Commodity Yield Curve.

The roll yield had a negative impact in the Crude Oil example, however let’s explore how it could be turned into an extra source of profit…

Term Structure – or Yield Curve

Term structure best applies to interest rates analysis. If we take the example of US Treasuries; they cover a wide range of maturities, from T-Bills (one year or less maturity) , T-Notes (one year to ten years) to T-Bonds (up to thirty years). For any given date, each treasury is priced depending on its maturity and an implied yield can be derived for each maturity. Charting these yields for each maturity gives the yield curve, also called term structure of interest rates.

Below can be found a “normal” (upward sloping) yield curve for US Treasuries (from last Friday) showing that yields went slightly down in the middle of the curve whereas the short and long ends of the curve did not change much:

The yield curve, or term structures of interest rates is a major economic indicator, closely watched by many traders to gain insights in the market. You can read more about it at investopedia and wikipedia.

Application in Futures Trading

Term structure can be similarly transposed to futures trading, which deals with the same concept of trading the same instrument (i.e. where a commodity like Corn replaces money used in the case of the classic yield curve) with different maturities (all different contracts and their various expiry/delivery months).

Note that the term Commodity Yield Curve is a bit of misnomer as the concept could be applied to any instrument traded in different maturities (i.e. all futures for instance).

Commodity/Futures Yield Curve: How to Build it?

The futures yield curve is a representation of the backwardation/contango rate for the different maturities of futures contracts. The contango/backwardation rates are calculated by taking the price difference between the spot/cash market price and the futures contract price. This difference can be expressed as a percentage (for x number of days: time to expiration of the futures contract) and then annualised to calculate an annual contango or backwardation rate, representing an implied yield for the specific maturity.

For example, if the spot market trades at 70 and the October contract (expiring in 90 days) trades at 71, the yield can be calculated as follows:

1. Price difference = 71 – 70 = 1
2. Percentage price difference = 1/70 = 1.43% (for 90 days)
3. Annualised contango rate (yield) = (1.43% + 1)^(365/90) – 1 = 5.92%

Performing this calculation for each contract/maturity would allow to chart a futures yield curve.

Volatility of the Yield Curve

I calculated and charted the Crude Oil yield curve in order to analyse and visualise the evolution of the contango/backwardation rates, both across the historical timeline and the range of contract maturities. This was done as discussed above for all contracts available for a given date.

Here is a sample chart using data from December 03 to January 04 where Crude Oil was trading in backwardation:

Each “ribbon” on the chart represents the yields for all 18 different maturities (1=short, 18=long) for a given date: each ribbon represents the yield curve for that day. All yields are negative because of the backwardated aspect of Crude Oil at the time. Backwardation rates have reasonable values (-15%/+5%) and the associated yield curves vary slightly across time, albeit maintaining a similar pattern.

However this snapshot was taken at relatively quiet times in the market. Fast forward to 2009 and you can see that the market now exhibits contango, with rates spiking to much higher values:

Adding any more dates to this chart would make it hardly readable, but to give an idea of how the term structure of Crude Oil can vary over a longer period of time, I have simply plotted the shortest end of the curve (i.e. yields for the contract with closest maturity), which you can imagine as a “vertical slice” of the 3-D chart alongside the date axis, where maturity = 1. Here is the chart:

There are spikes, alternation between contango and backwardation. It clearly displays some volatility and, for lack of a better term, a character of its own.

Note that because the yield is calculated as an implied value based solely on the price difference between contracts, some discrepancies in these contract prices (for whatever reason) would impact the curve in a more volatile and “artificial” way than if it did purely represent the fundamental yield factors (cost of carry, supply/demand and shortage, convenience yield, etc.)

Some Observations

The main observations are:

• Yield curves and their associated contango and backwardation rates evolve in a fairly volatile fashion across time.
• For any given date, different contracts and their associated maturities offer different yields, possibly within a wide range of values.

This can add an extra dimension to your trading decisions. The yield curve offers other opportunities (than the traditional front-month contract) when wanting to buy or sell any instrument via futures trading. These extra opportunities can be turned into an extra source of profit.

This is really an introduction to a next post, in which we will look into how we can use this information and apply it to a trading system to improve its roll yield component and enhance its overall performance.
Stay tuned…

Petroleum has unfortunately been in the news lately. Nevertheless, Crude Oil performance last year gave us a good illustration of the impact that contango/backwardation can have.

Crude Oil – 2009

Crude Oil’s had a fantastic year in 2009. The spot price bottomed around 35 and topped 80 to finish on a near +100% performance

Many would assume that quick and easy way to double their money was to invest in Crude Oil in 2009 (assuming you could time the top and bottom perfectly). This is without counting the strong effect of contango that would have eaten into the return.

This can be illustrated by the fact that the USO ETF – supposed to reflect the performance, less expenses, of the spot price of West Texas Intermediate (WTI) light, sweet crude oil – did not manage to emulate the levels of performance seen in the Crude Oil spot price in 2009:

A mere +34% performance over 2009 pales in comparison with spot price performance

This is, of course, because the ETF managers invest in Crude Oil futures and are subject to the same contango, which eats into their returns.

Below is a chart of several prices for Crude Oil in 2009:

• The spot price is the headline price.
• The rolled contract price represents a back-adjusted contract, trading in the front-month contract and rolling over to the next contract before expiry.
• Two other single contracts, with different maturities are also plotted.

All prices are rebased to start 2009 at the same level as the spot price.

Whereas you cannot trade the spot price, you can trade using the 3 other contracts. Note how they all underperform the spot price performance: this is contango in action.

Most people call this a negative roll yield (long positions in a market in contango or short positions in a market in backwardation) because the drift is more apparent at time of rolling over to the new contract (which is priced dearer than the current contract), however the decay induced by the contango is gradual and erodes the price regularly – as can be seen in the further-dated contracts (the premium priced in the future-dated contract deflates gradually to zero at time of expiry).

The commodity yield curve is clearly an incidental impact on an overall trading strategy results, but it can also be used to form the basis of the strategy itself

Term Structure Trading Strategy

One such example of a strategy using term structure (aka. yield curve) as a trading signal is described in the paper
Tactical Allocation in Commodity Futures Markets: Combining Momentum and Term Structure Signals.

ABSTRACT: This paper examines the combined role of momentum and term structure signals for the design of profitable trading strategies in commodity futures markets. With significant annualized alphas of 10.14% and 12.66% respectively, the momentum and term structure strategies appear profitable when implemented individually. With an abnormal return of 21.02%, a novel double-sort strategy that exploits both momentum and term structure signals clearly outperforms the single-sort strategies.

The authors calculate the roll return for various instruments and select the most backwardated and contangoed markets. Only backwardated markets are allowed to be long and contangoed markets to be short. The increase in performance compared to a standard momentum strategy appears interesting.

Term Structure as a Strategy Filter

Another idea to explore is using term structure as a filter to a Trend Following strategy. Similarly to the concept explained above, one could prevent going short strongly backwardated contracts or long strongly contangoed contracts – basically avoid the markets where the odds are stacked against them.

I have not come across any such published test but found this paper:

Separating the Wheat from the Chaff: Backwardation as the Long-Term Driver of Commodity Futures Performance; Evidence from Soy, Corn and Wheat Futures from 1950 to 2004

It gives interesting fundamental insights as to why markets might be in contango or backwardation and studies the impact or prediction power of the roll yield on a passive long investment strategy. One of their conclusions is that yield return rates start having predictive power when considered on a long-term basis (multi-year) as opposed to monthly measurements.

I wanted to test the MMDI as a follow-up to Moving Median: a better indicator than Moving Average?. This also provided a good opportunity to test Trading Blox (which I am thinking of buying).

MMDI: What is it?

In short, this is an indicator very similar to the MACD, except that the short moving average of the MACD is replaced by a moving median.

Portfolio Filter: Trade with the trend

One concept often used to improve the edge of a trading system is to look at 2 or more timeframes. The main timeframe (shorter one) is used for triggering trading signals (eg Donchian Channel breakouts), and the longer timeframe is used to determine the direction of the main trend. The filter rules prevent any trade signal to be taken if it goes against the main trend.

Trading Blox: a componentized testing framework

A great feature of Trading Blox is that it provides you with a skeleton workflow that forms the framework for the backtesting process. What this means is that Trading Blox implements and runs its logical workflow in the simulation loop (ie read data, update indicators, check entry signals, check exit signals, post-simulation scripts, etc.) but provides you with hooks at every step (about 35 hooks per simulation loop) where you can write your own code for customisation (with access to Trading Blox internal objects).

Next is the concept of blocks, which represent the different components of a trading systems (Entry signals, Money Management, Risk Management, Portfolio Filter, etc.). These blocks are easily reusable in any system and implement the functionality required via the code contained in their scripts.

One such block we are interested in for today is the MACD Portfolio Filter:

MACD Portfolio Filter Blox

This block stops the system from opening new trades in the opposite direction to the trend (the direction of the trend is derived from the MACD value).

MMDI Portfolio Filter

It was easy to use the standard Donchian channel system that ships with Trading Blox and replace its MACD Portfolio filter block by an implementation of the MMDI Portfolio filter. All it took was a copy of that block and an update of some of the scripts to implement the MMDI indicator and the filtering based on its value.

Indicator calculation:

mmdiIndicator=Median(ohlcDiv4,mmdiShort)-emaIndicator

Filtering code (long side):

[...]
' If positive, then allow long trades
IF ( mmdiIndicator > 0 ) THEN
	instrument.AllowLongTrades
ENDIF
[...]

And applying the new block (MMDI Portfolio Filter) to the system in the system editor screen:

System Editor

Test Scenario

The test is a comparison of the standard Donchian Channel breakout Tend Following system with MACD Portfolio Filter against its variation using the MMDI Portfolio Filter.

In order to get more data points (and to test Trading Blox parameter stepping), the comparison was run over a combination of system parameters:
- Long MMDI Moving Average: 200, 250 and 300
- Short MMDI Moving Median: 50, 62 and 74
- Donchian Channel Length (Entry): 20, 30 and 40
- Donchian Channel Length (Exit): 15
- Stop level: 2 x ATR(40)
- Slippage: 15% of ATR (+3% at rollover)
- Commissions: $12.50 per contract
- Dates: 01/01/2001 to 09/30/2010
- Instruments: 28 liquid futures (currencies, commodities, financials)

Test Results

Here are the two results tables produced by Trading Blox, showing a few stats for each system tested:

MACD Portfolio Filter Results - click to expand

MMDI Portfolio Filter results - click to expand

Looking at the CAGR and Sharpe ratio on aggregate, here is the how the systems compare:

Conclusions

First on Trading Blox: it was fairly straight-forward to code up this new indicator and system. The stepped parameter tests were also really quick to run (<1 min). Still pretty pleased and feeling at ease with it.

The test results show a small improvement in the MMDI favour. A possible explanation might be that the more volatile nature of the moving median (as illustrated in the moving median indicator post) allows it to pick up changes in trend faster (and get in them at an earlier, better price).

The whipsawing produced in the Moving Median crossover run would normally take place during range-bound markets, where few Donchain breakouts would happen, therefore cancelling the extra noise and losses associated with them.

Might be worth investigating further…
 
 
PS: David’s code for MMDI on TradeStation is available for free on his dvindicators.com website

PS2: As a comparison, and to illustrate the impact of a portfolio filter, here are the results for the same system without any Portfolio Filter:

Only 3 sets of results as the MACD/MMDI stepped parameters have been removed - click to expand

Without the trend filter applied to the portfolio, the Donchian channel breakout system now exhibits a negative performance (-2.68% on average across the 3 backtests). The trend is definitely your friend!

A research paper (summarised below) was recently published by AQR, explaining some concepts of trend following.

Clifford Asness, AQR Managing & Founding Principal, was also invited to speak about it with his good friends at CNBC (he is also an ex-Goldman, so he surely has lots of connections with the media and government). The video is not that interesting but here it is below, anyway. If you’re short of time (aren’t we all?), I recommend you skip to the paper (8 pages) or the summary, which yield more interesting insights.

Summary of Paper: Understanding Managed Futures

Click to download paper

- They start with a chart displaying the Managed Futures “smile”, basically a scatter plot of Trend Following strategy return vs. S&P 500 total return – making the point that Trend following performs best in case of extreme stock market moves (one of the main points they want to drive home throughout the paper is that Trend Following is a great portfolio diversificator with low correlation to other assets). They further empirically demonstrate this by pointing out Trend Following’s performance in Q4 2008 (strongly positive) in contrast to the global crash.

- The paper uses a hypothetical Trend following strategy for comparison and analysis. This strategy trades 60 liquid futures markets divided in the four asset classes defined by AQR (equities, commodities, bonds and currencies). To determine the trend, the strategy considers the excess return over cash of each instrument for the prior 12 months (a positive return results in a long position and a negative return results in a short position). The portfolio is equal-risk-weighted (i.e. normalised for annualised volatility) across the instruments and rebalanced every month.

- The second part breaks down the three parts of a trend and some behavioral biases or technical explanations for them:

• Start of the trend and under-reaction, due to anchor and insufficient adjustment to new conditions (i.e. news, supply shock, etc.), disposition effect (selling winners too early) and market particpants fighting trends (central banks or commodity hedgers)
• Trend continuation and over-reaction, due to herding and feedback trading as well as confirmation bias (similar to the reflexivity concept explained by George Soros in The Alchemy of Finance) and Risk Management practices (stop losses being triggered generate more losses, etc.)
• End of the trend where the market comes to the realisation that prices have gone too far

- The analysis of the strategy looks at the performance of each market and compares it to the overall strategy performance – noting the effect of the free-lunch that is diversification: The Sharpe ratio of the overall strategy is 1.3, higher than any of the individual market Sharpe ratio (all between 0 and 1).

- Another observation is the low correlation between the individual markets (average pair-wise correlation of 0.08) as well as between the overall strategy and various asset classes (Equities, Bonds and Commodities).

- Finally, they compare a 60/40 portfolio performance (60% Equities, 40% Bonds) with a hybrid portfolio (80% 60/40 portfolio and 20% Managed Futures) and show that return, standard deviation, Sharpe ratio, worst month and worst drawdown are all improved under the second scenario. I believe this is how they intend to market their new trend following funds: as a portfolio diversificator improving its overall variance-adjusted return

- In conclusion they highlight some of the risks (range-bound periods, high turnover and trading costs as well as manager fees) and finally (it is a marketing paper after all!) some of the value-add that a fund like theirs can provide (advanced strategies using rigorous quantitative methods over different time horizons, sophisticated risk management systems, portfolio optimisation and smart order execution algorithms, etc.)

The trend following space is just getting a bit more crowded…