Live condition · historical base rate

Liquidations arriving in cascades

The cascade clustering index is above 0.6: liquidations are arriving in bursts rather than independently.

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 Liquidations arriving in cascades held (840 occurrences in the scanned window, 822 with a computable 24h horizon), the market reached +5% within 24h in 419 of 822 (51.0) and drew down 10%+ in 238 of 822 (29.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 840 MATCHES

786 of 840 (93.6%) 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. 840 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 840 matches
66033MATCHES PER UTC DAY · MAX 66 ON 2026-08-2228283966292828331930134423192933212016212641352517501524221808-2408-3109-0709-142026-08-19 TO 2026-09-18 · 31 UTC DAYS · ZERO DAYS KEEP THEIR SLOT

About 27 a day, on 30 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 834 measured, 6 too recent
≥ +0: 428 OF 8341139276774677522273332928587823411131≤−100−30−20−15−10−5−2−1−0.5−0.2−0.1<00++0.1+0.2+0.5+1+2+5+10+15+20+30≥+400

Forward 30-minute returns across 834 matches: 55% of them finished up.

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. 822 with a complete 24h.
Drawdown · 24hDrawdown (MAE, 24h)The worst price moved against within 24h of each occurrence: the deepest point, never the close. 822 with a complete 24h.

Versus baseline

Historically, when any eligible minute in the window (baseline) held (23,109,929 occurrences in the scanned window, 20,038,886 with a computable 24h horizon), the market reached +5% within 24h in 5,845,457 of 20,038,886 (29.2) and drew down 10%+ in 1,123,252 of 20,038,886 (5.6). 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#55e2d169)
grammar
research_query.v2
normalization
archive_normalization.v1
feature library
feature_defs.v1
dataset revision
8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
query hash
55e2d169c9294e8d6a653ec51f3aa14f91b2587abf8d3c67dd7d04c9aa1a0b12
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.cascade_clustering_index",
        "gte",
        0.6
      ],
      [
        "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.cascade_clustering_index","gte",0.6],["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.