XRPUSDT · historical base rate

Crowd long, leaders not on XRPUSDT

Global accounts sit at least 65% long while top traders run at least 10 points less long than the crowd.

What has happened on XRPUSDT when it held

Historically, when Crowd long, leaders not on XRPUSDT held (22 occurrences in the scanned window, 21 with a computable 24h horizon), the market reached +5% within 24h in 0 of 21 (0.0) and drew down 10%+ in 0 of 21 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 22 MATCHES

0 of 22 (0.0%) reached +5% or more within 1 hour

Pick a move size and a window: how often did a move at least that big follow?

🔒 Measured after every match, never by keeping winners. 22 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 22 matches

About 0.7 a day, on 11 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 22 measured, 0 too recent

Forward 30-minute returns across 22 matches: close to an even split, which is what a coin flip looks like.

How to read a distribution

Upside reached · 24hThe most price moved up within 24h of each occurrence: a high-water mark, never a close. 21 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 21 with a complete 24h.

Versus baseline

Historically, when any eligible XRPUSDT minute in the window (baseline) held (9,925 occurrences in the scanned window, 9,800 with a computable 24h horizon), the market reached +5% within 24h in 5 of 9,800 (0.1) and drew down 10%+ in 0 of 9,800 (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 XRPUSDT (2026-05-15 to 2026-08-08), growing daily.

Historically, when Crowd long, leaders not on XRPUSDT over the full record held (63 occurrences in the scanned window, 62 with a computable 24h horizon), the market reached +5% within 24h in 2 of 62 (3.2) and drew down 10%+ in 0 of 62 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 63 MATCHES

0 of 63 (0.0%) reached +5% or more within 1 hour

Pick a move size and a window: how often did a move at least that big follow?

🔒 Measured after every match, never by keeping winners. 63 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 63 matches

About 0.7 a day, on 28 of 86 days: spread through the record rather than one event.

Where the next 30 minutes landed - 63 measured, 0 too recent

Forward 30-minute returns across 63 matches: close to an even split, which is what a coin flip looks like.

How to read a distribution

Check our work: the key that re-runs this exact count (q#abcea9b0)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 28acee8d0f123e535c8ba6325acd308c76bd0d511704f22e105e4b7842b6cb92
query hash: abcea9b0dd1ed6ba8f658aae34a908074f3665729d02d420564bea05094ab112
scanned window: 2026-07-09T00:00:00.000Z to 2026-08-08T00:00:00.000Z (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.top_global_long_skew",
        "lte",
        -0.1
      ],
      [
        "feature.global_long_ratio",
        "gte",
        0.65
      ],
      [
        "identity.symbol",
        "eq",
        "xrpusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-09T00:00:00.000Z",
          "2026-08-08T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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.top_global_long_skew","lte",-0.1],["feature.global_long_ratio","gte",0.65],["identity.symbol","eq","xrpusdt"],["times.anchor_time","between",["2026-07-09T00:00:00.000Z","2026-08-08T00:00:00.000Z"]]]},"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