Live condition · historical base rate

Liquidation intensity elevated

Normalized liquidation intensity is reading above 0.5: liquidations arriving well above this market baseline.

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 Liquidation intensity elevated held (2,212 occurrences in the scanned window, 2,156 with a computable 24h horizon), the market reached +5% within 24h in 976 of 2,156 (45.3) and drew down 10%+ in 441 of 2,156 (20.5). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 2,212 MATCHES

2,064 of 2,210 (93.4%) 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. 2,210 of the matches have a complete 1 hour forward; the other 2 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.

When they happened - one bar per UTC day, all 2,212 matches

About 71 a day, on 30 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 2,189 measured, 23 too recent

Forward 30-minute returns across 2,189 matches: 57% of them finished up.

How to read a distribution

Upside reached · 24hThe most price moved up within 24h of each occurrence: a high-water mark, never a close. 2,156 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 2,156 with a complete 24h.

Versus baseline

Historically, when any eligible minute in the window (baseline) held (30,464,101 occurrences in the scanned window, 26,640,441 with a computable 24h horizon), the market reached +5% within 24h in 7,738,908 of 26,640,441 (29.0) and drew down 10%+ in 1,639,920 of 26,640,441 (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:

1000PEPEUSDT ARBUSDT BANKUSDT BEATUSDT BLESSUSDT BTCUSDT BTWUSDT DOGEUSDT ENAUSDT ETHUSDT HYPEUSDT LITUSDT LSKUSDT NEARUSDT PROMUSDT RAYSOLUSDT SOLUSDT SUIUSDT TRUMPUSDT TUTUSDT UNIUSDT USELESSUSDT XRPUSDT ZECUSDT

Check our work: the key that re-runs this exact count (q#842fc8ec)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
query hash: 842fc8ec9154673f2a852d28bcab3eeb3e7b8a8db2441c07d42369cb65108341
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_intensity_norm",
        "gte",
        0.5
      ],
      [
        "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_intensity_norm","gte",0.5],["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.