Trend Following for Individual Investors – A Manageable Futures Strategy

A traditional long-only portfolio consisting of globally diversified asset classes can be greatly enhanced by a truly diversifying strategy, namely trend-following managed futures. We have over 40 years of real return data *,*** available from different CTAs running such strategies – there is even research testing the validity of the concept going back 800 years in time****. This data shows trend-following strategies to be uncorrelated to other asset classes and to provide considerable crisis protection, while at the same time producing long-term positive returns in line with (or better than) the global portfolio on a risk-adjusted basis. Details on the behavior of a trend strategy over time – how and why it works – are analyzed here.
Mixing together a traditional portfolio and a managed futures trend-following strategy in equal parts results in a much more efficient portfolio with higher returns and less volatility. Because of it´s positively skewed return distribution, a trend approach behaves especially complimentary to a negatively skewed strategy e.g. a short volatility approach. This diversifying quality is especially desirable in times when major asset classes show very high valuations (signaling low future expected returns) simultaneously – as equities and bonds do at the moment.
Because these properties are rare and very valuable, I have spent time researching ways to implement a managed futures strategy for an individual investor with an account size that makes using a strategy, that requires a lot of work, worthwhile, but falls short of the large amount of capital required to run a classic managed futures strategy with an acceptable degree of diversification and volatility.


Minimum Account Size and Alternatives
Because futures contracts are really the only instruments cost effective enough, while providing the necessary leverage and flexibility, to run such a strategy, an account size of about US$1 Million* is required to run it across a reasonably large investment universe due to the large size of individual futures contracts. When using other instruments like levered ETF, CFD/Spread bets or options the resulting cost will likely eat up any excess returns above simply accessing the strategy through a high-fee mutual fund.


Until now I used such funds and recently created ETF that replicate managed futures strategies (I am uncertain as yet what they will actually deliver). A long-only trend approach using ETF is easily implemented with any amount of capital and very little time and effort***** – I use a trend overlay independently in all areas of my portfolio specifically to avoid the large drawdowns of buy-and-hold investments.


But the major diversification benefit comes from a long/short strategy and with a certain account size it starts to make sense to think about an individual implementation using futures contracts simply because the expected return (historically about 10% per year targeting 12% volatility – managed futures strategies are easily scalable to run at different volatility targets) makes the effort worthwhile over the long run.
On the other hand, judging from the strategy as described below, to reach a tolerable level of volatility and to be able to trade enough futures contracts to be diversified across the full spectrum (at least 25 to 50 different assets), considerable capital is required. Combining these two estimates, I have come to the conclusion, that with a minimum account of about $/€200.000 it is possible to consider running an individual trend-following managed futures strategy responsibly – that´s only 1/5th the usual guideline for required capital.


Investment Universe
Futures contracts cover these main asset classes:
  1. Equities
  2. Interest rates
  3. Agricultural commodities
  4. Non-agricultural commodities
  5. Currencies
From each category we should use at least 3 to 10 different futures that are as non-correlated as possible to be well diversified. Other categories, for example, the new cryptocurrency futures, can be integrated easily.


A Basic Strategy
Trend-following strategies are really very simple in their basic structure and I list the best books I read on the subject at the end of the article – they all contain complete strategies.
Any method used to determine trend – be it by comparing two price points, using breakouts or moving averages – will catch the movement (if it materializes) in a very similar fashion. In my opinion, it really doesn’t matter much which method is used – differences in outcome will largely be due to chance. I choose the method that intuitively makes most sense to me, because being able to trust the signals is very important when actually running the strategy.
Two factors will impact strategy performance the most:
  • The look-back period: short-, medium- or long-term trend-following
  • The sensitivity of the trend signal: how frequently we look at prices (e.g. daily, monthly)


Trend strategies are very robust across wide sets of parameters, but the shorter the time-frame used, the noisier the trend signal gets. Also, because they have become more popular, the effectiveness of short-term strategies has deteriorated in recent decades*. Any information coming from look-back periods lower than 2 weeks and prices more frequent than daily, I consider largely useless random noise.


An example for a classic medium term strategy is a 10×100 daily moving average crossover which gives us a good benchmark. It is always in the market: if 10 MA > 100 MA we are long and if 10 MA < 100 MA we are short. Across 50 volatility-scaled futures positions this simple strategy has produced a Sharpe ratio of 0,7 to 0,9 historically (as we can scale the strategy to any volatility target, stating absolute return numbers is quite meaningless – a 0,8 Sharpe ratio would translate into approximately 10% return at 12% volatility annually).*
Because it always holds positions in 50 futures this strategy would require more than $1 Million to scale to 12% volatility.


Tweaking the Strategy
The benchmark strategy has several issues we can address to construct a better strategy which requires less capital.
  1. Diversification across parameters: It would be better to combine short-, medium- and long-term signals. Alas that tends to increase capital requirements as often several futures contracts would be held per asset.
  2. Non-binary entries and exits by scaling in and out of positions would be preferable in practice resulting in a smoother equity curve. Using different time frames could achieve that.
  3. Taking correlations into account for diversification: the benefit of adding an uncorrelated asset far outweighs adding another closely correlated one. We can use broad categories (e.g. for equities only US, Developed and Emerging markets) and cap closely correlated assets to achieve good diversification with a lower number of positions.
  4. The basic strategy is common knowledge and a lot of the underlying market inefficiencies are likely to be arbitraged away by increasing participation when many trend-followers enter and exit at the same time. A useful idea would be to implement a counter-trend element that uses that insight by entering near common trend-following exit points.
  5. Investing in smoothly trending markets is best in practice, but that is hard to quantify and backtest as well as it is unpredictable when a smooth trend phase is likely to begin.
  6. Carry (the cost of holding a position) could add useful information on expected returns for different assets and add an additional dimension especially for commodities and currencies.**


Strategy Implementation
It is possible to implement a trend-following strategy manually, investing time continually, or fully/partly automated, investing time and money programming the strategy to run independently.
I, for the moment, will start to run it manually to be aware of and learn from all the nuances of daily implementation and because I have decided to employ a discretionary visual filter to determine trend quality.
I personally like to use moving averages and moving average crossovers of daily prices as I tend to distrust breakouts to new highs or lows intuitively.
These are the parameters I use – not optimized, but arbitrarily chosen to lie somewhere in the middle of all the parameters that have worked in the past and to be somewhat different from commonly used values:
  • 9-day moving average: 9MA
  • 60-day moving average: 60MA
  • 275-day moving average: 275MA
  • new long-term high or low


Rules and Process
Basic Filters determine if a futures contract qualifies for investment and in which general direction it should be traded:
  • Long-term trend component: if 60MA is above 275MA enter only long positions; if 60MA is below 275MA enter only short positions.
  • Discretionary trend quality: I visually determine if an asset currently shows smooth, clear up- or downtrends or follows a choppy path with lots of gaps to whittle down the number of investable assets: I assume here that trend quality exhibits persistence which will result in more profitable trades, because less initial stops are triggered by random movements, but, as it isn´t easily testable, who knows? I base my assumption on the observable phenomenon of volatility clustering.
  • Carry: Holding an asset should cost less than 1% per quarter, positive carry positions are preferred. Especially for commodities this is an essential screen, futures for most other assets show very small cost of carry that we can ignore.
Entry
  • Counter-trend, medium term component: after a counter-trend that causes the 9MA to cross below the 60MA enter an initial position in the direction of the long-term trend when the 9MA re-crosses above the 60MA. This is a high probability entry that buys outsized dips below recent highs (or the reverse for short positions), but only enters in the aligned direction of the long-term and medium-term trends.
  • Long-term trend component: enter a second position at the breakout to a new high or low. The setup of the strategy will usually cause a new high/low to be several weeks from the last significant high/low. The initial position must have moved well into profitable territory at the second entry point or we don´t take it.
Exit
  • Short-term trend component: exit one position at a daily close below (for longs) or above (for shorts) the 9MA. This exit determines the actual capital at risk for each position and it will closely follow a price move in the right direction.
  • Medium-term trend component (only when two positions were entered): exit remaining position when the 9MA crosses below (for longs) or above (for shorts) the 60MA.
Money Management & Position Size
There are several good methods to calculate volatility scaled position sizes using standard deviation**, average true range* or the margin to equity* that your broker determines for each contract, which all target the same average daily volatility for each position.
I like to use the average true range (ATR) over the last 20 days as it is very simple to use and shows the average of the actual range of recent daily moves.

The key is, that the combination of the first counter-trend entry and short-term exit on the other side of the 9MA has a high reward-to-risk ratio. It has a high likelihood to show an immediate profit and has an automatic tight trailing stop loss (the 9MA) – a potential loss that can be predetermined before putting the position on. This enables a relatively high initial position size and only once that first position shows a healthy profit will the entry for a second position be triggered by a trend confirmation and I initiate a second wider exit point on my initial position to allow profits to run for longer.
While many positions are stopped out, losses are small and winning positions show high multiples of the average loss as profits. Any trend-following system will show a relatively low win probability for each trade (between 30% and 40%) – it is the hallmark of a positively skewed return distribution that we aim for with this strategy.


I determine my position size as follows:
  • The exit point on the other side of the 9MA initially determines my average potential loss.
  • I volatility weight each position by calculating: 0,33% of capital (this percentage is determined by my risk preference and portfolio structure) divided by the current 20-day ATR multiplied by the futures contract multiplier which gives me the number of futures contracts to buy or sell:
    (0,33% NAV) : ((1 ATR) x contract multiplier) = number of contracts.
    I then cross check, if my 9MA exit at the time of entry is approximately 1 ATR away from my entry point, as this will determine my average loss in case the position goes against me right away – 0,33% of capital are at risk for each position using the sizing formula. If the distance between entry and exit is further than 1,5 ATR, I don´t take the entry, because the initial risk is too high.
  • When I get a second breakout entry signal I follow the same procedure for the second position while putting a mental breakeven stop loss below my first position, so I have never more than an average 0,33% of capital at risk per asset.
  • At the same time I have an eye on the capital at risk by all open positions simultaneously. This should be around 5% at a maximum. When many assets show entry signals simultaneously, I implement a correlation cap – e.g. after three positions I stop adding any more equity markets as they are very closely correlated.


This process implies a few things: 
  • Each position exhibits the same average day to day volatility (an average daily change in value of 0,33% NAV) – portfolio risk is diversified across uncorrelated assets.
  • Whether a position will be a loser is determined quickly because of the tight stop loss. This is likely the weakest point of the strategy: if too many positions are stopped out by random noise, it will not be profitable.
  • Profits will be taken quickly initially and only the positions that are profitable and have their trend confirmed by a breakout to a new high or low will be converted into medium-term holdings through the second entry and exit points. Holding on to these profitable positions long enough is the most challenging part in running the system for me, but it is where the real profits are generated.
  • Because of the tight initial exit and the strict entry criteria, that weed out a lot of assets, we will likely hold only a small number of open positions at any one time (4 to 12) each at a larger size than a traditional CTA would scale it (they would use about 0,1% to 0,2% NAV in the above formula*), which allows the strategy to be implemented with less capital then is usually required.
  • The volatility of profitable positions may be quite high as position sizes can be comparatively large due to the tight exit and pyramiding mechanism. A high upside volatility is desirable, but considerable book profits can also revert quickly and be stopped out.
  • The size of the positions makes the use of leveraged instruments necessary – only a handful of positions will use up all available capital, if used unlevered. Leverage levels vary according to volatility (short term interest rates will require most leverage), but can easily reach and exceed 10 times overall.



Managed Futures in a portfolio context
It is not easy to judge how much capital to allocate to the strategy within a portfolio as in practice futures contracts only use a part of your margin allocation (usually between 10% and 20%) and no capital. The actual risk allocation in the portfolio is determined by the combined position volatility which is determined by the position sizing formula.
I arrived at the position sizing algorithm above by combining backtest data, comparing margin requirements of futures positions with the margin used by my short options strategy and the exposure to the underlying assets (where managed futures inherently use a higher degree of leverage). I now watch the daily fluctuations within my portfolio closely as well as monitoring capital at risk in different portfolio parts independently to fine-tune the allocation. Updates are listed below.
My default allocation in the current positive market environment is equal weighting exposure to the three basic strategies in my portfolio:


global asset allocation – short volatility – trend-following managed futures


The concept of adaptive portfolio allocation will change the weight with changing market regimes, quickly reducing exposure to short volatility strategies in a beginning bear market, more slowly reducing exposure to and reallocating within the global asset allocation, while raising cash and increasing the managed futures allocation.


Adjustments
January 26th 2018: After running the strategy for two months with good results, I reduced the daily average volatility per position from 0,5% of capital to 0,33% of capital. I found myself holding around 10 positions on average and, while usually the assets move uncorrelated to each other, occasionally everything co-moves. This has caused daily fluctuations in the portfolio value of almost 5%, which I find too high.


I also reduced the ATR look-back from 60 days to 20 days to react more quickly to changes in volatility. Starting in February 2018 volatility shot up across many assets and open / new position sizes should have been adjusted faster.


August 2018: February´s regime change introduced increased whipsaw losses to the strategy and I looked at smoothing and enhancing returns by combining the trend strategy with some mean-reversion ideas based on carry. These can be found in a new post.



My favorite books on strategic trend-following:
*In “Following the Trend” Andreas Clenow shows how a simple trend strategy explains the majority of returns from major CTA funds and how it behaved historically over the years in great detail with straightforward ideas on Investment Universe, Position Sizing, Leverage etc..
** Robert Carver lays out a complete trading system design process using trend and carry rules as an example in „Systematic Trading“.
*** Michael Covel gives a thorough overview over the industry and concepts in „Trend Following”
**** In “Trend Following with Managed Futures“ Alex Greyserman and Kathryn Kaminski take a very long-term look at trend-following and make the academic case for viewing it as an alternative asset class.
***** Gary Antonacci details a method using trend and relative momentum in a long-only ETF portfolio with very few moving parts in „Dual Momentum Investing”.




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