2010 was a good year for Trend Following. The year closed on a high for both Trend Following Wizards and the Trend Following index of the simple strategies I track in the State of Trend Following report.

Now that all results are in, it is a good time to look back at the results, crunch some numbers and run some analysis. Let’s see if simple strategies managed to track the Wizards performance (hint: it looks promising)…

How did the Wizards perform as whole in 2010?

In order to get an overview for 2010, I constructed a Wizard index aggregating individual fund performances. The index monthly return was simply calculated as the average of all Wizard returns.

Looking through this lens, performance in 2010 came at +18.91%, with an annualized standard deviation of 14.42% (based on monthly standard deviation of 4.16%). Below is the following monthly breakdown:

How did the Wizards compare?

Results from Trend Following Wizards are often thought to correlate with each other. Empirically, this has appeared true throughout the year, with monthly results often being “in sync”.

In order to quantify this, I calculated a correlation matrix of Wizards tracked on the blog. Of course, correlation is far from being perfect, but with this data set, it should give us a good idea of similarity in the Trend Following Wizards performance. The matrix displays the correlation (using Pearson) between the Wizards monthly returns in 2010:

Wizard Correlation Matrix: click on table to zoom in

To reduce down the information, I have also calculated an alternate correlation measure between each Wizard and the Wizard index, based on monthly returns. The table below contains correlation of monthly returns over 1, 3 and 6 years:

 
And in visual form for the 1-year correlation on 2010:
 

 
Apart from a few exceptions, correlation numbers run fairly high across the board.

How about the State of Trend Following report?

The State of Trend Following, which aggregates the performance of simple Trend-Following based-strategies, similarly had a good year in 2010.

One the drivers behind creating this report and index is the idea that a big part of Trend Follower returns are made up of style-beta, which are easier to replicate than pure alpha (see the Betafication of Alpha: towards a Commoditization of Trend Following? for more on this). This appears to be a plausible proposition when looking at the results from the correlation calculations for the Trend Following Wizards, above.

The headline return for the report index was +54.08%, which is not directly comparable to the Wizard index return: the volatility was much higher. As volatility is a function of leverage, applying a volatility-normalization to the returns will give us a better comparison. I adjusted the report index with a standard deviation-normalization (ie leveraged down the monthly returns so that their standard deviation match those of the Wizard index), which produced a return of 21.19%.

The three curves are plotted below:

Striking resemblance, don’t you find? Monthly return correlation is 0.84, which seems to support the idea of style-beta embedded in the Wizards performance.

What Next?

In the light of these results, one could come to the quick conclusion that Trend Following Wizards do not add much value compared to fairly simple Trend Following strategies. I believe this would be over-simplifying the issue.

let’s not forget that the index tracked is a theoretical model, not a real-life trading program. It does not include the impact of slippage and commissions and all other constraints associated with running a real operation. Moreover, Wizards results are net-of-fees, meaning that their gross performance is superior.

However, there is probably some truth in the concept of some style-beta associated to Trend Following, which should be encouraging to all traders/investors wanting to join the “Trend Following party”, but not able to do it through CTA offerings.

Another evolution based on this concept could be the emergence of “Trend Following tracker funds”, aiming to capture the style-beta associated with Trend Following (with tracker-like, much cheaper fees), similarly to the recently launched GTAA Trend Following ETF by Mebane Faber. It will be interesting to see how the industry looks like in 5-10 years and if changes at all.

Note on R  (for geeks)

I would have normally used Excel to do most of the correlations calculation (before I saw the “R” light). For the correlation matrix for instance, on top the fact that my version of Excel (2003) does not support color scales (to apply a specific color a the cell based on its value to generate a heat map), this would have required a fair bit of manipulation, formulas, etc. Instead I used R, and wanted to share how quick the whole operation can take. Here is the code that imports the monthly returns data, calculates the correlation table and plots it as a heat map:

x<- read.csv("2010-returns.csv", header = TRUE, as.is = TRUE, sep = ",", dec=".");
x_cor<- cor(x,,,"pearson")
x_heatmpap <- heatmap.2(x_cor, Rowv=FALSE, Colv=FALSE, dendrogram="none", col=cm.colors(256), trace="none", cellnote=x_matrix, notecol="black")

Three operations = three lines… Fanbloodytastic!

Note that the function heatmap.2 (improves on standard heatmap by, amongst other things, allowing values to appear on cells of heat map) requires an additional R package: gplots. A sample returns file can be found here for you to test this out. Go on and download R (Windows, Mac, or Linux) to give it a try if you do not use it already. You will not regret it.
 
 
Update as per Josh’s comment below: to use the gplots package you need to install it and load it (as it is not standard). For this, you can either use the Packages menu “Install Packages…” option and pick your CRAN mirror and select the gplots package (if you’re using RGui). Or you can do it from the console with the “install.packages” command as per code below, which installs and then loads the package (required before running the previous code):

#install package from CRAN repository mirror
install.packages("gplots")
 
#load package
library(gplots)

Dennis did so to set up an old argument with fellow trader Bill Eckhardt on whether trading could be taught or not (not unlike the story in classic movie Trading Places).

The experience was succesful in proving Dennis right (trading could be taught), with his turtle traders making him $100 million.

Why turtles?

The Turtles were nicknamed as such because of an analogy with how Richard Dennis expected to “grow” traders in the same way Singapore farms grow turtles. Dennis taught his students a mechanical Trend Following system and let them trade with his own capital. After being kept secret for more than a decade, the rules were revealed and floated on the internet for a while. Two enjoyable books have now been published on the topic (Complete Turtle Trader – featuring the actual turtle rules and The Way of the Turtle written by Curtis Faith, a former Turtle) if you are interested in learning more about it.

The turtle system

The Turtle system did not contain “magical” components. It was basically a combination of 2 different breakout systems with specific rules for money management, including position sizing, pyramiding, correlation limits and cutting down position size during drawdowns (a quick “Turtle trading rules” google search should yield some results for the exact rules).

System kaput?

Now that the rules have been made public, it is possible to backtest them and see how they would have performed on the recent markets. Such test result can be found on the Trading Blox forum.

click to zoom in

It basically shows that the CAGR drops from 216% (!!) from 1970 to 1986 (when Dennis and Eckhardt were developing the system, and also when the students traded the system with real money) to barely double digits (10.5%) in the last 23 years (1986 to 2009), with a completely flat period from 1996 to 2009.

As a side note check the crazy amount that compounding generates at that rate of 216%!

One could argue that Dennis & co were just lucky to trade the system during what appears to be its golden period.

System overheating warning

During the Turtle experiment, Dennis came to the realisation that their position sizing rules were such that:

you have been trading as much as twice as big as we thought

Here is a snapshot of the memo that Dennis sent to all traders asking them to cut their position size in half.

As Dennis said:

We must be living right

Another way of saying “We have been very lucky”?…

What does this mean?

Well, some say that the Turtle performance was a fluke – that the Turtles were actually the proverbial monkeys writing Hamlet (see the Infinite Monkey Theorem). I guess these people would be in the EMH camp (Efficient Market Hypothesis).

Some say that Trend Following is dying/dead and the Turtle system under-performance is an illustration of that. Problem is: these people seem to celebrate the demise of “simpleton” Trend Following strategy every so often (during major drawdown peiods), only for Trend Following to come back roaring again (think 2008).

Some might also say that market conditions are changing, and systems need to adapt to these changing conditions.

Although, some also say that this argument is specious, citing Bill Dunn as an example of a CTA claiming to use the same rules as when he started in the 70′s.

Reconciling it all

Instead of concluding this post quickly here – on what it does or does not means – I thought it might be more interesting to expand and spend more time on the possible interpretations above in a post of its own. Part 2 will take this discussion further.

Stay tuned, I will try to touch on whether or not Trend Following should, and can, adapt to changing market conditions – update: post here.

I just have to get something out on this 1st beginning of March. I could keep polishing this report until Christmas… But instead here is a first draft. The State of Trend Following report will be published at the beginning of each month (expect it to be evolving for the first few months).

The Concept

The principle was inspired to me by Michael Stokes from MarketSci with his very smart State of the Market report describing how the markets are behaving.

The idea is to track several classic Trend Following systems over a wide range of instruments and different timeframes. This should, in theory be a good complement to tracking the Trend Following Wizards to get an idea of how well Trend Following is doing as a strategy.

For this first report, I picked (arbitrarily) 3 standard Trend Following systems that ship with Trading Blox:

• Moving Average Crossover system with ATR-based stops
• Bollinger Band Breakout
• Triple Exponential Moving Average

Each system is tested over a short, medium and longer timeframe. Details of the system rules, parameters used and instruments traded can be found at the end of the post.

The Background Picture

As an initial run – to give a long-term idea of how each of the 9 systems performed (3 systems x 3 timeframes) – the whole futures data available in Trading Blox (provided free by CSI) was used for the backtest simulation.

One of the main interest of the study is how trends evolve over time and different systems. Therefore, the simulation parameters include no trade friction (ie no commissions and slippage at all). The equity curves below are therefore over-optimistic but they should really be used for relative comparisons.

Chart showing Systems Equity curves and Drawdowns. Each column represents a system (MA=MA Crossover; TMA=Triple Moving Average; BBBO=Bollinger Band Breakout) , with each row representing a timeframe (short, medium, long from top to bottom) - Click to zoom in

If there is one trend that seems to be shared across all 9 systems, it is the increased volatility – expressed by the larger drawdowns having appeared in the last 4-5 years in all systems.

Imagine starting trading the Golden Crossover system (50-200 MA Crossover) on January 1st 2003 – after good backtesting results (CAGR>20% and worst drawdown around 10% – a MAR of 2), only to see drawdown figures quadruple in the next 5 years!

This could be down to the fact that most instruments have become more correlated in the last few years, in effect negating some benefits provided by diversification.

Zoom in to 2009 and 2010

We are also interested in the most recent picture. Below are 2 charts comparing our 9 systems on the 2009-2010 and 2010-only time horizons (each system adjusted back to 100 at the start of the period). Each type of system is represented in one colour and each timeframe in one line style:

Nothing very exciting in 2009, and with addition of trade friction, it seems to correlate with the (negative) results witnessed on the Trend Following Wizards. Also note that most systems follow the same path (apart from the short-term Bollinger system, all over the place with extreme volatility – the chart does not show it, but it closed February 2010 at around 60).

2010 seems to be a continuation of 2009 so far: with a slight downward trend emerging across all systems.

Improvements to the report

There are quite a few things that could be added to this report, starting with integrating more systems and parameters (probably aggregated as an average per timeframe to avoid the data deluge syndrome). It might also be interesting to be able to break down the performance by type of markets/sectors.

But I am also open to your suggestions and requests. So please let me know in the comments below and I will integrate the best ideas.

Until next month…

Appendix: System Details

System Rules and Parameters

All the systems were tested with the same simplistic position sizing rules of 1% per new trade. No other Money/Risk Management rules were used.

The system rules are detailed on the Trading Blox online documentation.
The MA Crossover system was used with moving average pairs of 10-20, 20-50 and 50-200 days. The stops are set at 5 x ATR(39).
The Bollinger Band system is the classic use of the Bollinger Bands with entries taking place at Breakouts. The parameters used were 20, 50 and 200 days with 2 standard deviations.
The Triple moving Average system was used with moving average triplets of 10-20-50, 20-50-200 and 50-200-800 days. The stops are set at 5 x ATR(39).

Portfolio Instruments

The instruments included in the simulation are a semi-diversified list of futures, contained in the All Futures Trading Blox set:

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!

Robustness in trading is a tough beast to tame and understand. The more “robust” the research and development process, the better (read: robust) the results ought to be, right? With this in mind, I decided to test robust “tools” within the actual mechanical trading strategy itself.

The moving average indicator is so ubiquitous in trading that most folks (me included) use it without second thoughts. Its legacy probably dates from the era of expensive and complicated computing (it is relatively inexpensive to compute), so I wanted to revisit its hegemony – and give it a run for its money: by pitching it against a moving median indicator (on the basis of better statistical robustness for the latter).

Could it be that a moving median is actually a better indicator than the moving average?…

The experiment

To find out I used a basic and simple mechanical trading strategy: the Moving Average Crossover. This trading systems is always in the market, buys when the fast moving average crosses over the slow moving average and sells short when the fast average crosses under the slow average.

The second system would be a Moving Median Crossover. You guessed it: the same system, but replacing the average by the median.

The markets tested were a random collection of 17 Futures daily prices (proportionally back-adjusted contracts) – all going back as far as CSI history goes (1920′s for Wheat!):

 
The money management for both systems is to trade each instrument in a separate independent sub-account, fully funded (i.e. no leverage used) with profit re-invested. All commissions or slippage are ignored.

The main interest of the experiment is the robustness of each indicator. To quantify this, each system is run over 9 combinations of parameters for the Golden Cross (fast indicator values: 45, 50 and 55 days; slow indicator values: 180, 200 and 220 days). A measure of the robustness of the indicator is the uniformity of the results over the 9 combinations.

The results

Below are the total returns for both systems over each parameter set:

 
At first glance, it appears that the Moving Median indicator significantly under-performs the standard Moving Average indicator for these crossover systems.

Let’s look a bit deeper at the results with some basic statistical analysis:

 
The calculation confirms the under-performance of the Moving Median Crossover system. What about robustness you ask? Well, the Moving Median still scores worse than the Moving Average on both measures of uniformity/dispersion: the standard Coefficient of Variation (0.64 v 0.68) and its alternative cousin based on Median and Median Absolute Deviation (0.36 v 0.45): The Moving Average System produces more uniform (robust?) results!

Below is also a histogram of all 288 individual returns (per market per parameter combination, i.e. Wheat 180/45, Wheat 200/50, Silver 200/50, etc.):

There is clearly more blue presence on the left side of the chart and more red one on the right side…

A potential explanation

Using some inductive logic (warning: this might be dangerous when dealing with data from Extremistan^*), I started eye-balling the charts in search for some clues as to why the Median under-performs the Average. Below is an example of what I found:

Crossovers for Cocoa 2004-2010

The chart above only shows Moving Averages and Moving Medians (the prices have been removed to make the picture clearer). Average and Median seem to closely follow each other both on slow and fast sides. Indeed the bulk of the trades take place at roughly the same time (i.e. they both detect large trends fairly similarly).

However, if we zoom in over that red-circled congested area:

Zoomed-in portion of Cocoa chart

We can see that the Median Crossover system generates more signals than the Moving Average one (9 v 5). Trend following systems notoriously make big bucks in large moves but lose money in trend-less, range-bound markets – like the one being zoomed into. If the Moving Median Crossover system is more active in these sort of markets it will generate more losing trades while capturing similar big winners to the Moving Average Crossover system.

Intuitively, it could be hypothesized that the Moving Average evolves in a smoother manner and will generate smoother curves with less erratic moves and consequently less losing trades during trend-less markets – while the Moving Median does not generate a significant edge in detecting large trends.

One test is hardly enough to provide siginificant evidence, however this should give us some insights in the nature of the Moving Median indicator. The first insights being no increase in robustness and a drop in performance (when comparing total returns).

 
 
^*Extremistan: concept popularised by Nassim Taleb to describe the “province” where the total can be conceivably impacted by a single observation (e.g. financial data, wealth distribution). The opposite is Mediocristan: the province dominated by the mediocre, with few extreme successes or failures. No single observation can meaningfully affect the aggregate (e.g. human height and weight distribution). The bell curve is grounded in Mediocristan. There is a qualitative difference between Gaussians and scalable laws, much like gas and water.

For more info on Taleb’s terms, check his Fooled by Randomness glossary (PDF).

What this book is not

If you are looking for the Holy Grail of trading systems and/or a complete “turn-key” trading system: look elsewhere as this is not what this book is set to achieve. It will not reveal any trading secrets; however reading this book will make you realise that this is not what is important…

Trend Following: essentials of a trading philosophy

It looks like Michael Covel appreciated the concept of Market Wizards from Jack Schwager and drew inspiration from it. In a clever mix, Covel managed to overlay and enrich all the foundations of trend following with great trend follower insights.

The book covers the principles of the trend following trading philosophy and its multiple aspects: discipline, drawdowns, risk and money management, behavioral biases, prediction vs. reaction, etc. It makes for a very interesting read – both instructive and interactive.

Even with an automated trading system, you have to be in agreement with the strategy philosophy. This book helps you understand the philosophy of trend following in greater detail – you can then decide whether it suits you. Additionally it can help you strengthen your belief during the inevitable low points such as drawdowns while trading a trend following system.

Inside Trend Wizard minds: 10 years of research

The world of trend followers is relatively discreet: barely anybody has heard of Ed Seykota or Bill Dunn – but John Meriwether opening up his third hedge fund (after blowing up his previous 2 – including LTCM!) makes it to the first page of Bloomberg and the FT.

Michael Covel has spent 10 years researching and interviewing for this book. This allowed him to get in the head of the greatest trend following traders. This results in extensive quotes/pearls of wisdon from Ed Seykota, Dave Harding, Bill Dunn, John W Henry, all the greatest Trend Following Wizards. It really gives you great insights on the way they think and how they apply their market philosophy to trend following. And this is information that is not readily available anywhere else.

New Edition addition

I have read both earlier and newest (post-2008) editions and I think the later version is worth it. Many references to the major events of 2008 help to put the text in the present context and add some relevancy (especially with trend followers outstanding performance during these times).

Additonally there is a new appendix covering in detail Trend Following for Stocks, which is of interest.

Controversies

One of the main message of Trend Following (the trading philosophy and the book) is that we cannot predict. It forces society to admit that we are no as sophisticated as we think. It also points to the fact that chance might have much more to do with success of so-called “experts” rather than their “expert prediction abilities”.
One such “expert trader” Victor Niederhoffer slams the book:

I get the same sort of value from these books [Trend Following] as I do from studying the Keech cult, supernatural operators such as Uri Geller and horosope readers

You can tell that Covel takes some pleasure describing the latest blow-up from Niederhoffer’s fund in 2007 in his “Big Events” chapter.
Some people have also raised the question as to why Michael Covel primarily seems to be marketing his books and courses rather than trading for himself. Whether this is a valid point or not, this does not remove any quality from the book.

You can check and follow Michael Covel on his trend following blog to make your opinion.

In closing

I find this book very inspiring and motivating.
If you are new to trend following, this is a must-read: it will introduce you to all the basic concepts.
If you already believe in trend following, this is a must-read: it will reinforce your beliefs and motivation.
If you do not believe in trend following: this is a must-read: it is not too late to change your mind and this is the best book for it!

I have read Trend Following about 3 times now; this is a book that lives on my trading bookshelf – I know that every time I need a boost for motivation, a re-read will provide me just that.

As Larry Hite says:

The way I see it, you have two choices – you can do what I did and work for 30-plus years, cobbling together scraps of information, seeking to create a money-making strategy, or you can spend a few days reading Covel’s book and skip that three-decade learning curve.

Or are they you?…

Let’s explore the impact of fees charged by Fund Managers on the overall long-term return on your money. This is a question you might want to ask yourself before venturing in building automated trading systems (i.e. from a business point of view: would I spend a large number of man-days building a system that might barely achieve a better return than an established and succesful fund manager).

The assumption is that Trend Followers exhibit similar performance (for similar leverage). This can be verified when looking at the historical performance of the wizards. That can be explained by the fact that there is no “rocket science” approach to trend following. It boils down to getting on the trend as it starts and riding it until the end (when it bends…). For a given timeframe, any indicator you use would most likely identify the same trends.

To simplify, we will assume that a trend following fund manager achieves 20% per year and charges 1/20 fees (1% of AUM and 20% of performance).
Is it worth investing many hours in building a system yourself – or are you better off sending a cheque to John Henry, Dave Harding or Ed Seykota?

A gross return of 20%, after fees of 1% and 20% gives a net return of 0.99 x (20% x 0.8) = 14.84%
Turns out to be quite a difference!

By extrapolating and running a year by year comparison of gross return vs. net return we can see how the difference stacks up:

Net vs. Gross Return Comparison with starting capital of 100

Assuming that you have managed to build a system that produces the gross return above, we can see that keeping the fees for yourself (ie running the system) has quite an effect on the long-term return on your capital (compared to getting a fund to manage your capital):
After 3 years, you would already have gained one year compared to the managed fund approach (i.e. same capital but a year earlier). After 13 years, you are 4 years ahead!

And this is without considering you now have the option of raising external money to manage and earn your own fees (which you can only do if you run your own system)!

The example above illustrates the power of compounding and how small differences can end up making big numbers – which justifies putting the efforts to manage your own capital.

Trend followers position themselves to profit from and capture the “fat tails” exhibited in market returns distribution. In a fat-tail distribution (Power law, Levy or Mandelbrotian distributions), extreme occurrences occur with a probability greater than normal.

Fat-tail vs. normal distribution: notice the thickness of both extremes on the Levy distribution.

If you put in stops and run your profits and trade randomly you make money; and if you put in targets and no stops, and you trade randomly you lose money. So the old saw about cutting losses and running profits has some truth to it.

The basics of trend following is to ride the trend until the end (when it bends) and to protect yourself on the downside by cutting your losses.

This ensures that the location of your trades in the returns distribution will:

• Never venture on the left fat-tail (i.e. no extreme negative return)
• Not be bounded on the right-hand side of the distribution (i.e. allow for extreme positive returns)

As the markets are mostly random, most of the trades will end up in the centre of the distribution curve either side of the horizontal axis – and their return should cancel each other out.

Trend Following’s alpha (the actual strategy return) is generated by extreme movements: By letting trades run on the right-hand side fat-tail and stopping them from “wandering” on the left-hand side one, an overall positive return is generated. This outlines the fact that Trend Following relies on rare extreme returns (outliers) whereas the bulk of trades cancel each other out.

Note that this post simplifies matters to illustrate the fundamental point. Other parameters such as trading costs, etc. obviously need to be considered.

UPDATE: For those readers wanting to investigate this concept a bit further, a later post presents a research paper investigating the effects of the 4 first moments of the price distributions on the return of a Trend Following system. Please read here

The main message of the song is to “stick to the rules” – which is a fundamental aspect of Automated Trading Systems whatever stratgy the system uses. Please enjoy the video below, the music is not bad either… ;-)

The 6 rules are:

• Ride your Winners
• Cut your Losses
• Manage your Risk
• Use Stops
• Stick to the System
• File the News

Sounds like universal Trading Truths, doesn’t it?

Note: Ed Seykota has been trading automatic trend following systems for over 30 years. His model account grew by over 10,000%. You can find more information on Seykota from his website and in the books Market Wizards by Schwager and Trend Following by Michael Covel.