XRPUSDT · historical base rate

OI accelerating into liquidations on XRPUSDT

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

What has happened on XRPUSDT when it held

Historically, when OI accelerating into liquidations on XRPUSDT held (27 occurrences in the scanned window, 19 with a computable 24h horizon), the market reached +5% within 24h in 13 of 19 (68.4) and drew down 10%+ in 8 of 19 (42.1). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 27 MATCHES

2 of 27 (7.4%) reached +5% or more within 1 hour

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. 27 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 27 matches

About 0.9 a day, on 10 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 27 measured, 0 too recent

Forward 30-minute returns across 27 matches: 74% 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. 19 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 19 with a complete 24h.

Versus baseline

Historically, when any eligible XRPUSDT minute in the window (baseline) held (41,504 occurrences in the scanned window, 40,085 with a computable 24h horizon), the market reached +5% within 24h in 4,636 of 40,085 (11.6) and drew down 10%+ in 2,730 of 40,085 (6.8). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on XRPUSDT (2025-07-15 to 2026-08-24), growing daily.

Historically, when OI accelerating into liquidations on XRPUSDT over the full record held (144 occurrences in the scanned window, 136 with a computable 24h horizon), the market reached +5% within 24h in 41 of 136 (30.1) and drew down 10%+ in 12 of 136 (8.8). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 144 MATCHES

4 of 144 (2.8%) reached +5% or more within 1 hour

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. 144 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 144 matches

About 0.4 a day, on 85 of 406 days: spread through the record rather than one event.

Where the next 30 minutes landed - 144 measured, 0 too recent

Forward 30-minute returns across 144 matches: 60% of them finished up.

How to read a distribution

Check our work: the key that re-runs this exact count (q#825c9c7f)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: c9e0579296efd94a7ed22f263b9b7549097c44019cff4da395a5cf6ac557aceb
query hash: 825c9c7fd3f8746b5d8c91a9623f0dcdc3bfc5b3813b9a1c7b59a6d2dc9b292a
scanned window: 2026-07-25T00:00:00.000Z to 2026-08-24T00:00:00.000Z (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.oi_velocity_pctrank",
        "gte",
        0.9
      ],
      [
        "feature.liq_notional_usd_1h",
        "gte",
        1000000
      ],
      [
        "identity.symbol",
        "eq",
        "xrpusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-25T00:00:00.000Z",
          "2026-08-24T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","xrpusdt"],["times.anchor_time","between",["2026-07-25T00:00:00.000Z","2026-08-24T00:00:00.000Z"]]]},"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 XRPUSDT right now: the XRPUSDT research page