One of the characteristics that deter traders from using Trend Following is the typical lower winning percentage rate (i.e. ratio of winning trades vs. losing trades) of such systems. It goes against the natural instinct of “wanting to be right most of the time” as trades end up in a loss more often than not.
Psychologically, it is harder to trade a system that produces more losing trades. Despite this, Trend Following is a profitable strategy. Could there be a sort of “psychological premium” received by traders willing to use low winning percentage system?
When recently looking at Ralph Vince’s Leverage Space Model, I could see a potential reason why low winning percentage systems might be more robust.
An aspect of robust systems is the volatility in the system results and its equity curve. Similarly to low winning percentage, volatility is also a system feature that traders/investors nearly always want to avoid – at least from a psychological point of view.
Here is a quote from David Druz – a recent addition to the Trend Following Wizards report – which explains the link between robustness and volatility:
The robustness of a trading system is proportional to its volatility. This is the no-free-lunch part. A robust system is one which works and is stable over many types of market conditions and over many timeframes. It works in German Bund futures and it works in Wheat. It works when tested over 1950-1960 or over 1990-2000. Robust systems tend to be designed around successful trading tactics, classical money management techniques, and universal principles of market behavior. These systems are not designed around specific types of markets or market action. And here is the amazing thing about robust systems: The more robust a system, the more volatile it tends to be! This is because robust systems are not optimized to particular markets or market conditions. The converse is also true. You can design systems with excellent returns and low volatility on historical testing, but which work only for given periods in given markets. These systems tend to be curve-fit or market-fit and are not robust. For a system to have the highest odds of profitability over time and markets, the inescapable tradeoff is volatility. Diversification can be used of course, but it will only dampen the volatility so much.
A typical illustration of low-volatility, high-performance equity curve is this one below:
LTCM: smooth equity curve… for a while. No robustness there.
Money Management is a pivotal part of a trading system. It can make or break any system, however good it is. Over-trading a really good system will still lose you money.
Indeed, Vince affirms, in the Leverage Space Trading Model intro, that Money Management represents 100% of a trading system (leaving 0% for signal generation, etc.). This is probably a hyperbole, but the impact of money management should not be understated.
By looking at the impact of the the system’s winning rate on the money management part of it, there might be a reason as to why a system might be more robust when it produces more losers than winners.
Depending on preferences, there exists an optimal f which maximizes growth and which can take risk factors into account. The optimum approach is obviously to be as close as possible to the optimal f. However, this is not simple: markets change, systems go through different phases of performance and as a result, optimal f moves as well. It is not a fixed value.
From the start, we know that optimal f is bounded between 0 and 1 and that the further we are from it, the less optimal performance will be. Vince had an amusing analogy, comparing the optimal f to a roaming tiger on a football field. The tiger could be anywhere on the field; the hunting trader needs to find its location to place the tiger cage. Any error in locating the tiger would result in sub-optimal performance: the further the tiger is from the cage, the worse the system would perform.
However, there is a corollary from the Optimal f calculations: the optimal f value is bounded by the winning rate of the trading system. F can take values between 0 and 1 but if the system has a win rate of 30%, optimal f will always be between 0 and 0.3.
Going back to the tiger-on-the-football-field analogy, it greatly reduces the task of the “hunting” trader if we know that the tiger never ventures beyond the 30-yard line. And as a result we’ll never be too far off the target. From a trading point of view, this means that there is less room for error in the location of the optimal leverage and therefore less impact of a sub-optimal leverage.
In terms of robustness, this means that as markets change, so will the optimal f. A trading system with a low winning percentage will reduce the possible variation in the optimal f (e.g. [0,0.3] instead of [0,1]) and therefore the error between the actual f value used by a trader and the optimal f. Reducing the error should also reduce its negative impact on the system performance and make the system less sensitive to underlying market changes. In a word: more robust.
This is just a thought. I do not have any hard evidence or theory for it . As a young kid I always dreamt of having a tiger and riding it to school. I’d now like to think that it was an early subconscious call to be a good trader by focusing on “catching the tiger” of good position sizing/money management…