Realized volatility above its 90th percentile
One-minute realized volatility over the hour is above its own trailing 90th percentile for this market.
The live read is not yet enabled. This surface reports the historical base rates below; the firing-now list turns on when the live evaluator is connected.
What has happened when it held
Historically, when Realized volatility above its 90th percentile held (50,044 occurrences in the scanned window, 48,812 with a computable 24h horizon), the market reached +5% within 24h in 15,874 of 48,812 (32.5) and drew down 10%+ in 3,905 of 48,812 (8.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
Outcome distribution
45,248 of 49,804 (90.9%) reached +0% or more within 1 hour · smallest move size with matches, picked for you
Pick a window and a move size: how often did a move at least that big follow?
🔒 Measured after every match, never by keeping winners. 49,804 of the matches have a complete 1 hour forward; the other 240 are too recent to know.
What are MFE and MAE?Why 72 hours can show fewer than 1 hour: a match too recent for a complete 72 hours is left out of that count entirely, even if it already reached the level within 1 hour. The two counts cover different sets of matches.
About 1,614 a day, on 31 of 31 days: spread through the record rather than one event.
Forward 30-minute returns across 49,031 matches: close to an even split, which is what a coin flip looks like.
How to read a distributionVersus baseline
Historically, when any eligible minute in the window (baseline) held (25,055,492 occurrences in the scanned window, 23,275,902 with a computable 24h horizon), the market reached +5% within 24h in 6,865,175 of 23,275,902 (29.5) and drew down 10%+ in 1,423,136 of 23,275,902 (6.1). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
By market
Markets where this condition occurred often enough in the window to carry its own statistically meaningful receipt:
Check our work: the key that re-runs this exact count (q#a5d2adb2)
- grammar
- research_query.v2
- normalization
- archive_normalization.v1
- feature library
- feature_defs.v1
- dataset revision
- 8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
- query hash
- a5d2adb2540a5ab73acc23fca4ce1d6aad91b671e7386f24f39a94594f9a19c8
- scanned window
- 2026-08-19T00:00:00.000Z to 2026-09-18T00:00:00.000Z (UTC)
Same key, same bytes. This receipt reruns; it does not retell. When the record grows, the dataset revision changes and says so.
Re-run this yourself, over the API or from an AI agent
This is the exact search definition behind the receipt. Send it to the key-authed research API (or the MCP research tool) and compare the bytes: a cached rerun is free.
{
"schema_version": "research_query.v2",
"normalization_version": "archive_normalization.v1",
"feature_version": "feature_defs.v1",
"target": "record_occurrences",
"where": {
"all": [
[
"feature.realized_vol_pctrank",
"gte",
0.9
],
[
"times.anchor_time",
"between",
[
"2026-08-19T00:00:00.000Z",
"2026-09-18T00:00:00.000Z"
]
]
]
},
"sort": [
"times.anchor_time",
"desc"
],
"page": {
"limit": 50,
"cursor": null
}
}curl -s https://app.edgedepth.com/api/v1/research/query \
-H 'authorization: Bearer $EDGEDEPTH_API_KEY' \
-H 'content-type: application/json' \
-d '{"schema_version":"research_query.v2","normalization_version":"archive_normalization.v1","feature_version":"feature_defs.v1","target":"record_occurrences","where":{"all":[["feature.realized_vol_pctrank","gte",0.9],["times.anchor_time","between",["2026-08-19T00:00:00.000Z","2026-09-18T00:00:00.000Z"]]]},"sort":["times.anchor_time","desc"],"page":{"limit":50,"cursor":null}}'How to read this
- Counts ship with denominators: occurrences across the eligible symbol-buckets scanned. A rate is over the occurrences with a computable horizon (present), never the total.
- Absent is absent. When a feature was not warm at a minute, that bucket leaves the denominator and is reported; it is never coerced to zero.
- This selects a setup, never its outcome. The grammar forbids filtering on what followed (OUTCOME_IN_PREDICATE), so the distribution below is over every occurrence, not a survivorship-picked subset.
Occurrences are observed examples in a selected archive, not a guarantee. Outcomes are computed forward from each anchor and are descriptive, never a filter and never a prediction. Nothing here is trading advice.