Live condition · historical base rate

At least $5M liquidated in one hour

Observed liquidation notional over the trailing one-hour window is at least $5 million.

What is a liquidation?

Firing now (provisional, current bucket)

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 At least $5M liquidated in one hour held (137 occurrences in the scanned window, 135 with a computable 24h horizon), the market reached +5% within 24h in 38 of 135 (28.1) and drew down 10%+ in 6 of 135 (4.4). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 137 MATCHES

121 of 137 (88.3%) 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?

WINDOWMOVE SIZE

🔒 Measured after every match, never by keeping winners. 137 of the matches have a complete 1 hour forward.

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.

When they happened - one bar per UTC day, all 137 matches
1206MATCHES PER UTC DAY · MAX 12 ON 2026-08-1912111111567245613176224484274208-2408-3109-0709-142026-08-19 TO 2026-09-18 · 31 UTC DAYS · ZERO DAYS KEEP THEIR SLOT

About 4.4 a day, on 26 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 137 measured, 0 too recent
≥ +0: 62 OF 137112413121412115620201042≤−100−50−20−5−2−1−0.5−0.2−0.1<00++0.1+0.2+0.5+1+2+5≥+400

Forward 30-minute returns across 137 matches: close to an even split, which is what a coin flip looks like.

How to read a distribution
Upside reached · 24hUpside reached (MFE, 24h)The most price moved up within 24h of each occurrence: a high-water mark, never a close. 135 with a complete 24h.
Drawdown · 24hDrawdown (MAE, 24h)The worst price moved against within 24h of each occurrence: the deepest point, never the close. 135 with a complete 24h.

Versus baseline

Historically, when any eligible minute in the window (baseline) held (30,471,289 occurrences in the scanned window, 26,641,977 with a computable 24h horizon), the market reached +5% within 24h in 7,739,581 of 26,641,977 (29.1) and drew down 10%+ in 1,641,323 of 26,641,977 (6.2). 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#0ce9f01c)
grammar
research_query.v2
normalization
archive_normalization.v1
feature library
feature_defs.v1
dataset revision
8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
query hash
0ce9f01cb72466df26bb7fdb65b9f1e242945316d7583234bf0faa8203a87aeb
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.liq_notional_usd_1h",
        "gte",
        5000000
      ],
      [
        "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.liq_notional_usd_1h","gte",5000000],["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.