We simply buy on a breakout tomorrow at the maximum noise level today as discovered from our earlier study for when prices are below a given moving average. When we optimized this moving average length, we found that the parameters were robust over a large range. We selected 40 as the test case. The results are shown in “Simple breakout: S&P 500” (below).
This pattern does better than other breakout methodologies we have considered for electronic markets. It works well on the E-mini S&P 500. The drawdown is low. We need to conduct further tests to determine if we can trade this strategy, but as a first step toward building a breakout system that will work in today’s markets, this is a good one.
Performance in the E-mini Nasdaq 100 is shown in “Simple breakout: Nasdaq” (below). The results are not as good as those for the E-mini S&P 500; nonetheless, they are better than classic opening-range breakout methodology.
When we consider the mirror image pattern on the short side, we get poor results. This is interesting because the period tested included significant stretches of both strength and weakness, which should eliminate an underlying market bias toward long trades. The initial conclusion is that we simply found a pattern that worked well for the long side but not the short side.
The electronic markets have changed the day-trading landscape and made the opening-range breakout null and void. However, by developing new reference points, we have furthered the relevance of this venerable technique in stock-based markets. Additional improvement could be had by employing neural networks to predict, for example, the direction of the range expansion and the noise level so we could evolve our approach on the fly. Machine-based rule induction also could be used to identify new concepts of breakouts and create rules to predict best breakouts, minimum retracements, direction of range expansion and the noise level.
Murray A. Ruggiero Jr. is the author of “Cybernetic Trading Strategies” (Wiley). E-mail him at firstname.lastname@example.org.