< OI accelerating into liquidationsBTCUSDT · historical base rate

OI accelerating into liquidations on BTCUSDT

Open-interest velocity above its own 90th percentile while at least $1M of liquidation notional printed in the trailing hour.

What has happened on BTCUSDT when it held

Historically, when OI accelerating into liquidations on BTCUSDT held (142 occurrences in the scanned window, 135 with a computable 24h horizon), the market reached +5% within 24h in 3 of 135 (2.2) and drew down 10%+ in 0 of 135 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED - ALL 142 MATCHES, FROM THE PER-SCAN SUMMARY

0 OF 142 (0.0%) REACHED ≥ +5% WITHIN 1H

METRIC MFE_1H · CHIPS RE-READ THE SUMMARY, NEVER RE-QUERY

🔒 COMPUTED FORWARD, NEVER A FILTER · DENOMINATOR 142 = MATCHES WITH A COMPLETE 1H HORIZON

OCCURRENCES PER DAY · UTC · SUM 142 - ALL 142, NOT THE PAGE

About 4.6 a day, on 28 of 31 days: spread through the record rather than one event.

FWD_RET_30M DISTRIBUTION · 142 PRESENT · 0 ABSENT

Forward 30-minute returns across 142 matches: close to an even split, which is what a coin flip looks like.

Versus baseline

Historically, when any eligible BTCUSDT minute in the window (baseline) held (40,440 occurrences in the scanned window, 39,019 with a computable 24h horizon), the market reached +5% within 24h in 162 of 39,019 (0.4) and drew down 10%+ in 0 of 39,019 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on BTCUSDT (2026-05-15 to 2026-07-31), growing daily.

Historically, when OI accelerating into liquidations on BTCUSDT over the full record held (430 occurrences in the scanned window, 423 with a computable 24h horizon), the market reached +5% within 24h in 4 of 423 (0.9) and drew down 10%+ in 0 of 423 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED - ALL 430 MATCHES, FROM THE PER-SCAN SUMMARY

0 OF 430 (0.0%) REACHED ≥ +5% WITHIN 1H

METRIC MFE_1H · CHIPS RE-READ THE SUMMARY, NEVER RE-QUERY

🔒 COMPUTED FORWARD, NEVER A FILTER · DENOMINATOR 430 = MATCHES WITH A COMPLETE 1H HORIZON

OCCURRENCES PER DAY · UTC · SUM 430 - ALL 430, NOT THE PAGE

About 5.5 a day, on 74 of 78 days: spread through the record rather than one event.

FWD_RET_30M DISTRIBUTION · 430 PRESENT · 0 ABSENT

Forward 30-minute returns across 430 matches: close to an even split, which is what a coin flip looks like.

Reproducibility key (q#8dccb649)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 52a43a418a97fbe3a324f7c372fa4d53967163acaa0ed33c56b78ddd897331a4
query hash: 8dccb649595210178729e477c43f56ed3e32ef4dcb358551a81f4fddabbf6e84
scanned window: 2026-07-01T00:00:00.000Z to 2026-07-31T00:00:00.000Z (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 over the API or MCP

This is the exact research_query.v2 definition behind the receipt. Send it to the key-authed research API (or the MCP research tool) and diff 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.oi_velocity_pctrank",
        "gte",
        0.9
      ],
      [
        "feature.liq_notional_usd_1h",
        "gte",
        1000000
      ],
      [
        "identity.symbol",
        "eq",
        "btcusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-01T00:00:00.000Z",
          "2026-07-31T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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.oi_velocity_pctrank","gte",0.9],["feature.liq_notional_usd_1h","gte",1000000],["identity.symbol","eq","btcusdt"],["times.anchor_time","between",["2026-07-01T00:00:00.000Z","2026-07-31T00:00:00.000Z"]]]},"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.

Everything measured on BTCUSDT right now: the BTCUSDT research page