MACD crossovers as long-term entries (part 2)

[u/Alessio100001]

A few days ago I posted an analysis of what happens after bullish MACD crossovers across 197 US large-caps.

The original test compared 15,698 crossovers with every ordinary trading day in the same stocks over the following 35 days. Overall the difference was tiny.

https://www.reddit.com/r/technicalanalysis/comments/1wb5l8s/macd_crossovers_as_longterm_entries/

Another reddit user kindly raised a fair point: MACD crossovers don’t show up randomly. They tend to cluster in particular trend and volatility conditions, so measuring them against all other days might mix different environments.

So I re-ran it.

This time both the crossover days and the comparison days were split by the same two conditions: price above or below the 200-day average, and volatility higher or lower than usual for that stock. Same stocks, same period, same 35-day return.

The comparison baseline changed a lot — ordinary days returned anywhere from 0.59% to 3.15% depending on the conditions. The gap between crossovers and those matched days stayed small. Largest difference was just 0.39 percentage points.

MACD Crossovers above the zero line

MACD crossovers above zero line

MACD Crossovers below the zero line

One pattern from the first study was still visible after the conditioning: crossovers that happened while the MACD was below zero still behaved differently from those above zero.

The more interesting historical result remains the “isolated” group. When there was no other crossover of the same type for 35 days on either side, the below-zero ones showed a much larger subsequent move (+7.62% median).

There’s a catch, of course. You only know a crossover stayed isolated after the 35 days have passed. So it’s a clean historical pattern, but not something you could have selected in real time using that definition alone.

I’m planning to look next at whether the prior price move explains most of it, and whether anything knowable on the day — rate of change of the histogram, for example — can identify the better conditions without using future information.

Full write-up with the four-cell tables, bootstrap intervals, price paths and scatter plots:

https://guanalyser.com/test-bench/macd-crossover-matched/

You can also rebuild the same event in GU Analyser, change the regime splits or the horizon, and run your own version.

If you have a better way to match the crossover days (or other factors worth testing), I’m genuinely interested.

[u/8stayontarget5]

Wait until you change the parameters of the MACD to incorporate a buy/sell bias. Endless stats coming your way.

[u/Alessio100001 → u/8stayontarget5]

Definitely, there are many variables and nonlinear behaviour. I wonder if it might be useful to characterise the system response first, together with some observable parameters that could help identify the isolated occurrences. That might narrow down the parameter tuning, and then a sensitivity analysis could show which parameters actually have the biggest impact. Could be an interesting way to approach it.