Discussing the article: "Three MACD Filters on US_TECH100: Five Years of Broker Data"

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Check out the new article: Three MACD Filters on US_TECH100: Five Years of Broker Data.

This article tests three common filters on a standard MACD crossover for US_TECH100 H1 using five years of broker-native data. Filters are layered incrementally: regime, higher timeframe (HTF) alignment, and US session timing, to isolate each one's marginal impact. Results show session timing contributes far more than indicator refinements, while regime and HTF add little on their own. Includes a reproducible MQL5 regime classifier.

I have been looking at charts for more than twenty years, and I have been programming in MQL5 for about ten. It took me almost a decade of programming to feel that I understood what I was doing, rather than just copying patterns from other people's code. Two years ago something clicked, and since then I have been building my own tools instead of buying them.

One thing that bothered me for years is how often vendors sell "improved MACD" indicators by stacking filters on top of the classic crossover logic. They stack regime filters, higher-timeframe alignment, and session windows. The pitch is always the same: these filters transform a noisy MACD into a reliable system. I have bought several of these over the years and studied their results. My suspicion was that most of the improvement was either cherry-picked or the filters were doing less than the pitch suggested.

So I used real broker H1 data for US_TECH100, ran the raw MACD crossover strategy, and then added each filter one at a time to measure the impact on the results. There are no opinions and no narratives, just closed trades, win rates, and expectancy over five years.

The result surprised me.

Author: Marcelo Alejandro Borasi