The Hindenburg Omen Manual

Research note. Every figure computed at render from our daily breadth dataset (2010–present, ~4,750 US common stocks) — the same file behind the live Hindenburg Omen tracker. Methods in How we checked it; editorial standards in How we use AI.

What is the Hindenburg Omen?

A breadth checklist from technician Jim Miekka, popularized in the 1990s and named — by a colleague of his, for maximum menace — after the 1937 airship disaster. Its idea is simple under the branding: a healthy rally lifts most boats, so a market making many new 52-week highs and many new 52-week lows at the same time, while the index itself is still rising, is internally split in a way that has shown up near some serious tops. It measures disagreement, not doom. As of August 2026's close the conditions are not met (the last signal day was June 2026), with 4.7% of issues at new highs and 0.9% at new lows.

What triggers a signal? The recipe

Four conditions, all on the same day: new 52-week highs and new lows each at least 2.2% of issues traded; the index above its 50-day average (the split has to happen inside an uptrend — that's the whole point); the McClellan Oscillator negative (breadth momentum already deteriorating); and new highs no more than twice new lows. We compute it across our own universe of roughly 4,750 US common stocks rather than the classic NYSE-composite lists, and single days that qualify get grouped into clusters when they land within 30 sessions of one another. The tracker page documents every departure from Miekka's original spec in a comparison table. One consequence worth knowing: counts differ between sites because universes differ — ours has shifted its own history slightly as delisted stocks were pruned, and we say so rather than pretending the record is carved in stone.

How accurate is it? The honest record

Since 2010, our variant has produced 146 signal days, grouping into 33 clusters — roughly two per year. In the 63 sessions after a signal day, SPY averaged -0.3% against a +3.1% baseline — a drag of more than three percentage points. And yet 60% of those windows still finished positive. Both facts matter. The Omen marks conditions under which forward returns have been much worse than normal, and most individual signals still went nowhere. Anyone selling everything on a single trigger has been run over repeatedly; anyone ignoring a cluster entirely has met a few famous accidents.

Famous fires, famous fizzles

Seven clusters from our record — four that preceded real trouble, three that preceded nothing. SPY's next-quarter return is computed from each cluster's first signal day.

The pattern across all 33 clusters: the Omen's hits cluster in years when breadth was already splitting — 2015, early 2018, early 2020 — and its misses cluster in one-way bull years like 2013 and 2017, when the recipe technically fired into markets too strong to care. The indicator is a symptom check, and symptoms occur without disease.

Where this gauge will mislead you

• The name oversells it. "Omen" promises crashes; the data delivers below-average forward returns with a 60% positive rate. It is a caution flag with excellent marketing.
• Every implementation disagrees. Thresholds, trend filters and universes vary by source, so "the Omen fired" means different things on different sites. Ours is documented line by line on the tracker precisely because of this.
• It repeats what other gauges already say. When we audited our Swing Score, the Omen's ingredients were already counted by its breadth checks — so we demoted it to an unscored alert rather than let it vote twice. Treat it the same way in your own process: confirmation, never headline.
• History here starts in 2010. Claims about 1987 or 2008 come from other datasets and other recipes; our record covers 33 clusters across sixteen years and can't validate the older folklore either way.

How we checked it

Signal days come straight from our daily breadth dataset — the same computation the tracker charts — with clusters formed by a stated rule (a new cluster requires a 30-session gap between qualifying days). Forward returns are SPY averages from each signal day; windows overlap heavily across days inside a cluster, so effective sample sizes are far smaller than the day counts. The named-cluster table computes each next-quarter return at render, so revisions to the underlying data flow through automatically.

Frequently asked questions