GWEIUSDT · historical base rate

Crowd long, leaders not on GWEIUSDT

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

What has happened on GWEIUSDT when it held

Historically, when Crowd long, leaders not on GWEIUSDT held (22 occurrences in the scanned window, 22 with a computable 24h horizon), the market reached +5% within 24h in 9 of 22 (40.9) and drew down 10%+ in 13 of 22 (59.1). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 22 MATCHES

1 of 22 (4.5%) 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 over 31 days, but 2026-08-11 alone holds 41% of them: this is close to a single episode.

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

Forward 30-minute returns across 22 matches: 64% of them finished down.

How to read a distribution

Upside reached · 24hThe most price moved up within 24h of each occurrence: a high-water mark, never a close. 22 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 22 with a complete 24h.

Versus baseline

Historically, when any eligible GWEIUSDT minute in the window (baseline) held (40,641 occurrences in the scanned window, 39,185 with a computable 24h horizon), the market reached +5% within 24h in 21,627 of 39,185 (55.2) and drew down 10%+ in 17,075 of 39,185 (43.6). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on GWEIUSDT (2026-05-15 to 2026-08-14), growing daily.

Historically, when Crowd long, leaders not on GWEIUSDT over the full record held (22 occurrences in the scanned window, 22 with a computable 24h horizon), the market reached +5% within 24h in 9 of 22 (40.9) and drew down 10%+ in 13 of 22 (59.1). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 22 MATCHES

1 of 22 (4.5%) 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 0.2 a day over 92 days, but 2026-08-11 alone holds 41% of them: this is close to a single episode.

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

Forward 30-minute returns across 22 matches: 64% of them finished down.

How to read a distribution

Check our work: the key that re-runs this exact count (q#b8bc100e)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 3f9230e34c0a14e16a7bb3035842c9f073de8c4cb679de6f70e64bbb2153d03a
query hash: b8bc100e78362029b289270a3d74cab083f4e78a8b83e06e472e95c17ca0df8f
scanned window: 2026-07-15T00:00:00.000Z to 2026-08-14T00:00:00.000Z (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",
        "gweiusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-15T00:00:00.000Z",
          "2026-08-14T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","gweiusdt"],["times.anchor_time","between",["2026-07-15T00:00:00.000Z","2026-08-14T00:00:00.000Z"]]]},"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 GWEIUSDT right now: the GWEIUSDT research page