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I’m convinced that one of the biggest lies of traditional technical analysis (TA) is that “you can apply any tool to any market or any timeframe with the same results.” These claims are usually “backed up” with a few examples of an indicator slapped on some different markets and some vague appeals to the concept of fractal markets.

The concept of fractal markets is interesting and deserves some attention. (The (Mis)behavior of Markets by Mandelbrot is a good read and a very accessible primer.) To my (possibly misguided) thinking, the key element of fractal markets is that markets move in response to investors who work on many timeframes, each focusing on a specific kind of information. As long as these groups are essentially in conflict, markets are stable in their instability–chaos and instability bring a type of regularity to markets. When these investors on different timeframes turn their attention to the same factors and herd together, markets become unstable.

This is a powerful idea with far-reaching implications, but we do it a disservice to assume that this also means we can simply slap any indicator on any chart and go to town. In fact, it’s easy to demonstrate that there might be significant differences in the way different asset classes trade. The less structure we put on the markets (i.e., indicators or other calculations) the closer we are to the data, so let’s take a look at a simple channel breakout system, also often called a Donchian channel.

The use of channel breakout systems is well-established in trend following circles. We now know that the Turtles owed much of their success to the disciplined application of a rather simple system that used breakouts of N day highs and lows to signal entries. For trend following, it’s a good concept to start with because a market has to take out previous highs to go higher and lows to go lower. Visually, such a system might look like this:

100 day channel breakout system, with "flip only" entries marked.

100 day channel breakout system, with “flip only” entries marked.

The red (short) and blue (buy) dots on the price bars show the points where a simple breakout system would have initiated trades at the previous 100 bar high or low. One issue with a system like this is that it will mark multiple entries in the same direction, often on consecutive bars. We have to have some way to deal with that in actual trading, and I’m going to show you some tests here that use a “flip only” approach, meaning that you can only take a short after a long entry and vice versa. (In actual trading, there are better ways, but this will suffice for a simple test.)

So, what happens when we execute this? Here are some results, on a basket of commodity futures:

Results of a channel breakout test on commodity futures.

Results of a channel breakout test on commodity futures.

There’s a lot of information in that table, but, for this discussion, it’s sufficient to focus on the lines with the diamonds superimposed. These lines show the “excess return” over the baseline for the sample, for each of 20 trading days following a buy or sell signal. The buys (left column) show a signal which appears to get stronger further from the entry, and the shots (right column) appear to be roughly the same–a strong, stable signal.

Based on this information, we might conclude that we could build a system on this breakout concept, and we’d be correct. However, let’s look at exactly the same system applied to a basked of individual stocks:

The same breakout system applied to a basket of stocks.

The same breakout system applied to a basket of stocks.

Again, just focusing on the lines below the tables, we see a very different story: both lines seem to hover around the zero line with no clear signal. Compared to the futures sample, this is a dramatic difference. (If we run the same test on currencies it looks a lot more like the commodities than the stock sample.)

What can we conclude from this? Well, it’s just a rough test, but it is an indication that something might be different in these groups of markets. My work has found this over and over, and I believe that the force of mean reversion is much stronger in stocks than in currencies or commodities. Anecdotally, we know that there are many trend following systems for commodities and funds that run these systems, but we do not have so many examples of trend followers in stocks. Furthermore, a casual look at the TA literature shows many examples of authors saying that commodities and currencies “trend better” than stocks, which might be a way to say that the balance of momentum and mean reversion might be different in those asset classes.

I’ll show you a few examples in coming days of this concept applied to some indicators and other tools. Time and again, we will see evidence that all asset classes do not “trade the same”, and evidence that seriously calls into question the accepted wisdom that you can apply any tool to any market. As always, this is a call to do your own work and research, to fully understand the tools you use, and to work hard to understand the implications of risk and trading in your chosen markets.