I was recently reading a blog post discussing “trading the equity curve” of a system. This usually entails adapting trading size based on whether the equity curve of the system is below or above its equity curve.
A similar concept is described in The Way of the Turtle, by ex-Turtle Curtis Faith:
The Turtles were instructed to decrease the size of the notional account by 20 percent each time we went down 10 percent of the original account.
The idea seems to make sense in a way: the further the notional account size is reduced during a drawdown, the lower the maximum drawdown amount figure should be. Similar to a racing driver “hitting the brakes” as and when their vehicle starts going off-course, to avoid going “into the ditch”.
In periods of prolonged losing periods, the result should be a lesser impact on the equity curve, and a lower drawdown figure.
Testing The Concept
Trading Blox has this functionality built-in, so it is easy to test. You can set parameters for this Drawdown Reduction technique: the Threshold dictates at which point the notional account size is reduced (10% drawdown in the example above) and the Amount dictates by how much the size is reduced (20% in the example above). The reduction is cumulative, meaning that every time a new 10% decrease is observed, a further reduction of trading size by 20% is applied.
I ran a 20-day Donchian breakout system with “classic” volatility-adjusted position sizing (risk per trade = 0.75% of equity = 2 ATR) as a starting point. The results obtained were as follows:
The next run applied the Drawdown Reduction logic to the exact same system. As expected, the Max Drawdown figure does decrease by a fair amount, however note that the return also decreases by a greater amount, actually hurting the MAR ratio. The Sharpe ratio does not improve either:
In hindsight, it simply looks that reducing the overall leverage of the original system might achieve the same results (reduction in both drawdown and return). Here are the results of the first system with reduced leverage (0.64%) so that the Max Drawdown amount matches that of the second system:
Sharpe and MAR are closer to the first run results, with the CAGR being higher than in the second run, implying that a simple leverage reduction could be a better option.
Post Update: Of course, this is a single one-off test, from which you can hardly draw any conclusions, so do not go and dismiss it solely based on this post/test. A proper and more complete test with a large number of system variations – as kindly pointed out by Pumpernickel in the comments – would be a good start to evaluate the impact of the Drawdown Reduction technique. Something to play with in your own system development or to follow-up in a later post.
Managing the Unexpected
This comparison test benefits from hindsight. This money management technique might still bring a way to deal with extra-ordinary negative periods (where drawdowns would exceed the expected figures from the back-test). This way, the system could start trading with the “optimal” leverage derived from the back-test, with an extra safeguard (possibly with higher threshold triggers) only for cases when the system starts diverging substantially from the back-tested results (whether the system is “broken”, or experiences its worst period to date).
Another point to note is that the downside of reducing leverage during drawdowns usually increases the time required to get out of the drawdown.
Another possible use of this technique might be to use it to dynamically allocate to several systems in a suite: reduce the allocation of poorly-performing systems and shift the allocation to better performing systems (“starve the dogs and feed the stars”). This way, if a system starts becoming “broken”, its allocation is automatically decreased, which should reduce its (negative) impact on the overall suite performance.