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Follow that Trend

May 21, 2012


We mentioned in our post last week titled Here’s hoping the markets "Go to Zero"  that we half-jokingly expected stocks and crude oil to be up 3% this week because we had the audacity to say we wanted them to go lower, and wouldn’t you know it – we finish today with gains of over 1.50% across stocks and Crude.  

While we still hope the markets go to zero, we don’t think it will happen without the bulls trying to push it back to the upside a few times.  We just don’t want the moves higher to be significant enough to reverse the trend lower. 

You see, even though managed futures growth over the past two decades has seen the dawn of other strategy types within the asset class– like options, agriculture, currency, specialty (fixed income, stock index, metals, etc), spread, discretionary, and multi-strategy approaches– trend following is still the bread and butter of the world of managed futures. In fact, in our recent breakdown of the CTA industry, trend following was far and away the dominant strategy. However, not all trend followers necessarily cut from the same cloth. We’ve mentioned more than a few times that there are numerous ways to skin the trend following cat  (sorry cat lovers).

Foundation Breakdown

What, exactly, do we mean by that? While trend following, by definition, is the process of recognizing and trading along with an up or down trend, there are multiple mechanisms for identifying both when a trend starts and when it ends, with technical indicators such as Bollinger Bands, Donchian Channels, and Moving Average Cross Overs. 

The different types of trend following methods are essentially broken up into two types: those that believe a new trend is triggered by a breakout of prices above/below a certain level, and strategies which use the relative movement of prices to determine whether a new trend has started. 

Breakout Models

Perhaps most  easily recognizable of the breakout trend following model methodologies is the Bollinger Band method, which we discussed rather extensively in our post series on crude oil last year, “Anatomy of a Trend Following Trade” (here, here, here, here, and here). In this method, a program looks for a breakout above or below “bands” surrounding market prices at one standard deviation above and below the 60 to 100-day moving average to initiate a trade, and closes the trade when the market moves back to the 60 to 100-day moving average. 

A variation of the Bollinger Band method is to create bands around market prices using the Average True Range (or ATR) of prices instead of the standard deviation of prices, seeting the bands 2 ATRs above/below the current price, for example.  

Another breakout model is the Donchian method, named after Richard Donchian (and before you ask, yes, we do have a picture of him tacked up on the bulletin board in our kitchen– we’ve told you before we’re nerds for this stuff). The so-called "Father of Commodities Trading" developed what would become known as Donchian trading channels, which are simple channels surrounding recent price action with the top channel equal to the highest price of the last n days and the bottom channel the lowest price of the same period.  You buy when prices break above the top channel (making a new 20 day high, for example), and sell when the market breaks below the bottom channel (a new 20 day low, for example). A typical period may be 20 to 50 days. This is a method similar to the famous turtle method.

Richard Donchian 

Relative Price Models

Relative price models are less concerned with if a market has broken out of a range, and more concerned with whether recent prices are stronger or weaker than past prices. 

The Simple Moving Average Cross Over method (which is used more frequently in the stock market, in our experience) is the classic example of this, and it entails buying or selling when two moving averages of differing time periods (such as the 20-day and 100-day simple moving average) cross over one another.  The shorter term moving average is used as the trigger, signaling a buy when it crosses above the longer term average, and a sell when crossing back below the average. CNBC and the twitter stream go all a flutter when the S&P 500’s 50 day moving average crosses over its 200 day moving average to the upside, calling the move the "Golden Cross." 

A variation of the moving average cross over is the Triple Moving Average method which uses three moving averages instead of two (such as 10, 20, and 50-day simple moving average), where the smallest period crossing over the longest period acts as an early indicator of a trend, and the middle period crossing over the longest period acts as confirmation of the trend. 

After that, there are a few lesser known methods which use singular indicators (which themselves are based on relative price movements) to divine whether a trend has begun or not. These include an ADX (average directional index) method where the non directional indicator which measures the strength of a trend must be above a certain level in conjunction with a cross over of the positive/negative directional indicators; and an RSI (relative strength index) method where a move of the RSI above certain levels signals an up trend and below certain levels a down trend.  

There you go – a few paragraphs to explain 7 models which form the basic building blocks behind strategies used to manage $100s of Billions of dollars across the world.  Fun stuff, if you're into technical analysis. 

Is managed futures getting "short"?

All of this is a lead up to the seemingly simple question we’ve been getting from clients, blog readers, and so on over the past week:  is managed futures getting "short" amidst the coordinated sell off in stocks, metals, foreign currencies? The general response from us is that, yes, these are the types of moves which can trigger new entries for managed futures programs- this time on the short side.

 But the more analytical side of us wanted to know the answer with a little more sophistication.  Problem is, we don’t have access to the positions of the entire managed futures universe. We know the positions of dozens of managers and can extrapolate from there (really, if it works for media covering elections, we can do it, right?), but are always searching for a more technical approach to the question of when and where "managed futures" went long or short . 

Enter a blog we follow with a knack for trend following analysis: Au.Tra.Sys. Written by Jez Liberty, it tracks some of the largest trend following programs out there, as well as providing the rules for some of the “basic” trend following methods listed above. What’s more, they do a monthly series in which they apply 3 sets of different parameters to each of the four base models, resulting in 12 different trend following “programs,” for which they then report the test results across 50 markets using some basic money management and risk per trade techniques (which many would argue are way more important than the model you are using). 

We took that concept a step further by coding in the 4 base models on their blog,  plus an additional 3 models which are considered trend following in one way or another (the ATR channel, RSI, and ADX models outlined above) to put a more technical spin on the question of whether managed futures (or more technically – trend following) is short the current move. 

We ran these 7 trend following models against a portfolio of 48 markets spanning all of the major sectors (grains, energies, meats, softs, metals, currencies, stock indices, and interest rates), and then looked at each model position by position to see if they were long or short each of the 48 markets for every day over the last 11 years back to 2002. 

We then got the net position per market by summing the net position for each model in each market, and divided that net position by the total possible net position across all models to arrive at a net trend following exposure number. 

 So, if all 7 models are short all 4 energy markets in our portfolio, we would have 28 short positions across 28 possible short positions (7 models times 4 markets), where we calculated net trend following exposure as -100% (all short). Similarly, if just 4 models were net short just 3 of the energy markets, we calculated net trend following exposure as (4*3)/28 = -43%. Negative numbers represent short exposure and positive numbers represent long exposure. 

Finally, in a nod to the risk on/risk off environment of the past few years, we calculated the long/short exposure across all sectors  by summing the total net position of each market across each sector.  And to make sure the risk on/risk off characteristics remained intact for this analysis, we treated risk off markets inversely, so a long position in the US dollar, Japanese Yen, or any of the US or foreign bond markets we track (Euro, Aussie, and Japanese) was flipped to represent a short position (and vice versa) for our analysis to see total risk on/off exposure. 

The Results are In

Our findings were interesting – and did show what our experience led us to believe before running the data – that managed futures are indeed getting short "the market" right now,  with the market in this case meaning the risk on set including stocks, foreign currencies, and commodities.  

As of close Friday – we’re looking at our trend following proxy (the 7 models outlined above) short 30 out of the 48 markets in our test portfolio  (many of the grain markets are noticeably absent),  for a total net exposure across all markets on all 7 trend following models (again, with risk off markets like the US Dollar, bonds, and Yen treated as the inverse) of -49% . 

What’s interesting to us is that the trend following net exposure flipped to negative about a full month ago (April 12th), well before the move lower really started to pick up speed, and that the bounce higher today occurred right as trend following was at its most negative net exposure of the year and lowest since October of last year (thanks a lot…). Interestingly, that negative net exposure you see below in the first part of the year was the shorts initiated in October of 2011 being unwound as risk on markets rallied to start the year. 

[The following graphs show the net trend following direction as a percentage of the total possible positions in that direction across 7 separate trend following models, and include a single market overlay on a separate axis for comparison with a related market’s price movement over that time. Past performance is not necessarily indicative of future results.]

This peek into how trend following models position themselves piqued our interest enough that we ran the test back to Jan of 2002 to see just what the trend following landscape (and, by extension, managed futures as a whole) has looked like over the past 11 years. Questions such as the following suddenly came to mind: How short were trend followers in 2008? And just when did they go short? Are there any clues there as to why it has been a struggle since? 

Managed Futures Net Exposure Since 2002
Our first thought on viewing the above chart was something along the lines of…”wow, 2008 was a crazy time”, with net exposure nearly 3 times as short as it had been in the 7 years prior as depicted in the large red blob in our graph looking like the underwater portion of the iceberg that hit the titanic. We were also struck by just how large of a net position that was in 2008, with no other time period seeing more than 50% or less than -50% of markets in the same direction.  

Another interesting insight relates to the risk on/risk off trade, which can be seen graphically in the chart above in the way the long and short net positions are "larger" than they were before 2008. This is just direction, and has nothing to do with the size of subsequent moves – so those larger green and red areas in the graph post 2008 tell us trend following models saw more markets going long or short at the same time. How much larger- the absolute value of the net position percentage- was just 17% long or short from 2002 through 2007, yet has been nearly 1.5 times higher since 2008 with a value of 26%.

Our next thought was that it sure is nice to be in a dynamic strategy which adjusts position automatically from long to short (no rebalancing/rethinking asset exposure required). We were also struck by how consistent the net direction is during different periods. The data in the chart is daily, yet there is very little in the way of red/green, green/red, bars in the chart denoting trend following  flipping back and forth over several weeks. 

And finally, this view of trend following’s net direction really lets one see how much of a lag there can be in trend following. It is a reactive strategy, not predictive – meaning it isn’t going to be selling tops or buying bottoms, which is why you don’t see the net direction moving significantly into the red until mid 2008 even though the markets had started to sell off in 2007. Likewise, it stayed "red" for most of 2009 before the rally triggered moves into long positions. 

Sector Analysis

Once we saw the net direction across all markets, we couldn ‘t stop there, wanting to know what the net direction has been over the years (and is now) in energies, bonds, and more. 

First up, the bond (interest rate futures) trade in managed futures:

Managed Futures Bond Exposure 

If you’re thinking that’s a lot of green, you hit the nail on the head. But would you believe trend following (per our test) has been long bonds on 1,955 out of 2,587 days since January 2002, or 75% of the time? Wow. Maybe we shouldn’t be so excited about the inevitable down trend to unwind the 30yr up trend in bonds... or should we congratulate the models on correctly identifying and being involved with this decades long trend? (Disclaimer: Past performance is not necessarily indicative of future results.)

Next up – the energy sector:

Managed Futures Energy Exposure 

You can just barely make out in this chart the net position flipping to short (red) on the far right, but the bigger item for us was that there is a whole lot more red on this energy chart than the "ban speculators"proponents would have you believe. We dug in a bit deeper and found that the average net exposure across all 2,600 days was just 5%, and the total of all days was just 125% (compared with 89,000% across all days in bonds). You would expect this chart to look more like the bond chart if speculators such as managed futures programs were driving energy prices higher, but it contains quite a bit of net short positioning - yet speculators still fail to get the credit when energy prices fall. 

PS - If those backadjusted Crude oil prices look odd to you with the ‘08 low nowhere near its real low, that’s the effect of paying the roll yield month after month to roll the futures to a more expensive contract (contango – ask the USO fund about it).

What about the currency trade in trend following?

Managed Futures Currency Exposure 

No surprise here – with trend following having generally been long currencies (short the US Dollar and Yen) as the US Dollar has trended steadily lower over the past decade. This chart is one of the ones which has recently (last week) flipped from net long to net short, despite not being able to see it on the far right of the graph above. In this instance, managed futures are hoping the short currency trade (long US Dollar) looks more like the 2008 red period above instead of the spiky 2010 period where the net direction change didn’t last long enough for managed futures to capitalize on it. 

Finally, for good measure, we include the Grain and Metals sectors below, where are thoughts are: 1. We don’t remember many models successfully trading metals on the long side between 2002 and 2005 despite the apparent correct positioning with all the green in our graph, and 2. The grain sector is a sort of a classic trend following sector with its rather regular oscillations between up and down trends translating into rather regular switches in the net positioning between long and short for trend followers.

Managed Futures Metals Exposure
Managed Futures Grains Exposure 


We’re (collectively) always so concerned about performance that we sometimes forget to look at things from another angle – such as thinking about trend following in terms of how it positions its portfolio of markets, instead of just how it performs on those markets.  

We’re only showing the net direction of these base trend following models, and completely ignoring performance here – but somehow feel looking at trend following as an imperfect proxy for the asset class as a whole from this angle will open some eyes as to what exactly drives trend following performance that looking at compound returns and annual drawdowns just can’t provide. 

The graphs above show us that trend followers are indeed dynamic, moving from long positions to short positions and back again depending on market conditions. They show us that they are reactive, with the net position percentages usually topping or bottoming out well after the market has bottomed or topped out. They show us that these seven different trend following models are in agreement on the direction of the trend more often than not (if the models were in disagreement most of the time, the net position percentages would be clumped around the zero line). And they show us that these publically available trend following models aren’t all that different when it comes to identifying trend direction than professional trend followers running managed futures programs. 

Wait… if these free, publically available models essentially signal the same long and short positions in these markets as professional managed futures programs – why pay a 2% annual management fee and 20% of the profits for the professional access? Can't you just do it on your own? There's a very important, one word response to this query.


You see, identifying the trend, as it turns out,  is pretty much the easy part. Knowing how much of the account to risk on that trend, how many markets- in that sector and overall-  to enter in the same direction, when and how to filter such signals, and when to get out of the trade, just to name a few concerns, are the hard parts. In effect, anyone can get long or short a whole bunch of markets using these base models, but not everyone can contain drawdowns when the trend reverses. 

But these models will serve a purpose for us moving forward.  We’ll be saving these models on our servers, and using them as a proxy for managed futures positioning moving forward, relying on them as a benchmark of sorts to see how a manager lines up with the positioning the rest of the industry should have and sharing insight when we see a certain sector gaining significant net exposure one way or the other. 

What can we say? We're numbers nerds at heart.



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Feature | Week In Review: Down and down we go…

In what’s become rather familiar lately, it was another down week for the indices; the Dow lost -3.54%, the S&P 500 fell -4.39%, the Nasdaq fell -5.46%, the S&P Mid-Cap 400 E-mini lost -6.43%, and the Russel 2000 E-mini was down -5.56%. Bonds continued to flirt with new highs, as US 10-year notes rose 0.53%, and US 30-year bonds rose 2.20%. In currencies, the US Dollar gained 1.27% to reach its highest level since January, while the Euro lost -1.43% to reach its lowest level since January. The Japanese Yen gained 1.01%, the British Pound fell -1.66%, and the Swiss Franc lost -1.41%.

In metals, Gold was up 0.50%, Silver fell -0.61%, Copper fell -4.92%, Platinum lost -0.82%, and Palladium gained 0.03%. In energies, Crude hit a 5-month low after falling -4.50%, Heating Oil was down -4.51%, RBOB Gasoline was down -3.71%, and Natural Gas rose 9.29%.

In grains, Corn and Wheat both saw huge rallies, erasing the previous week’s sharp declines, as Corn rose 9.38% and Wheat gained 16.46%. Soy, on the other hand, fell -0.07%. In meats, Live Cattle gained 3.80%, and Live Hogs rose 2.49%. In softs, Orange Juice and Cotton both reached their lowest levels since July of 2010, as Orange Juice fell -16.61% and Cotton lost -1.24%. Cocoa lost -1.98%, Coffee gained 1.13%, and Sugar gained 1.24%.

Trading Systems

The lone day trading system this week, PSI! TF lost -$606.50 on one trade. In swing trading systems, Jaws US 400 US was up 1313.75 on one trade; Moneybeans S gained on one trade, but lost another to finish the week down -$1010; MoneyMaker ES lost on two trades for a total of -$2022.50; finally, Strategic ES had an even tougher time, losing on four trades to finish the week down -$2620.


It was the week of the short term strategy, with all three short term trading programs we track posting greater than 7% MTD returns as of last week’s end. The specialty programs we track are also having a good month. On the other hand, the options programs are really struggling this month, rounding out the bottom of the heat map. Agriculture programs are also struggling as they continue to cool off following last month’s great performance. Trend followers were all over the board, from the top to (near) the bottom. Check out the full heat map below:



Max DD*

Strategy Type

Clarke Capital Management, Inc. Worldwide




Quantum Leap Capital (QEP Only)



Short Term

Bouchard Capital, LLC Short Term Multi Commodity



Short Term

Dominion Capital Management (QEP Only)



Short Term

Integrated Managed Futures Corp. IMFC Global Concentrated




P/E Investments FX Strategy - Standard




Clarke Capital Management, Inc. Global Basic




Emil Van Essen, LLC Commodity Only (Low Min)



Spread Trading

2100 Xenon Fixed Income Program:



Fixed Income

Emil Van Essen, LLC Combined (Low Min)



Spread Trading

Bluenose Capital Management LLC - BNC EI




HB Capital




Rosetta (QEP Only)




Futures Truth MS4 (QEP Only)




Bluenose Capital Management LLC - BNC BI




Hoffman Asset Management, INC. Managed Account




Briarwood Capital Management Diversified Trading Program




Reynoso Capital Management - Small Accounts




Attain Portfolio Advisors - Strategic Diversification Program




Crescent Bay BVP




NDX Abedengo




NDX Shadrach




Mesirow Absolute Return




Tanyard Creek Capital (QEP Only)




James River Capital Corp. - Navigator




Bel Air Capital Asset Management




Cervino Gold




Cervino Diversified Options




Auctos Capital Management




2100 Xenon Managed Futures (2x) Program:




GT Capital




Covenant Capital Management Aggressive




White River Group Diversified Option Writing




Global Ag (QEP Only)




Cervino Diversified 2x













*Max DD= A drawdown is the “pain” experienced by an investor in a specific investment. As an example, an investor starting out with a $100,000 account who sees it fall down to $80,000 before it runs back up to $110,000 saw a $20,000 loss ($100K – $80K), which would equal a -20% ($20K/$100K) drawdown. The so called Maximum Drawdown (Max DD) is the worst such peak to valley down period for an investment.


**Disclaimer: Past performance is not necessarily indicative of future results.  These performance numbers are calculated using the liquidating value of a single client at Attain trading the listed program, and are believed to be representative of all similar clients invested in the program.  A 20% incentive fee and 2% annual management fee are deducted from all profitable months, regardless of whether the program is at a new equity high.  These numbers may vary from the actual performance numbers presented by the CTA upon completing their accounting for the month gone by, and should not be considered apart from the performance numbers listed in the disclosure document for the program listed.


Futures based investments are often complex and can carry the risk of substantial losses. They are intended for sophisticated investors and are not suitable for everyone. The ability to withstand losses and to adhere to a particular trading program in spite of trading losses are material points which can adversely affect investor returns.

Past performance is not necessarily indicative of future results. The performance data for the various Commodity Trading Advisor ("CTA") and Managed Forex programs listed above are compiled from various sources, including Barclay Hedge, Attain Capital Management, LLC's ("Attain") own estimates of performance based on account managed by advisors on its books, and reports directly from the advisors. These performance figures should not be relied on independent of the individual advisor's disclosure document, which has important information regarding the method of calculation used, whether or not the performance includes proprietary results, and other important footnotes on the advisor's track record.

The dollar based performance data for the various trading systems listed above represent the actual profits and losses achieved on a single contract basis in client accounts, and are inclusive of a $50 per round turn commission ($30 per e-mini contracts). Except where noted, the gains/losses are for closed out trades. The actual percentage gains/losses experienced by investors will vary depending on many factors, including, but not limited to: starting account balances, market behavior, the duration and extent of investor's participation (whether or not all signals are taken) in the specified system and money management techniques. Because of this, actual percentage gains/losses experienced by investors may be materially different than the percentage gains/losses as presented on this website.

Please read carefully the CFTC required disclaimer regarding hypothetical results below.