GWEIUSDT · historical base rate

One-sided tape: sellers on GWEIUSDT

Taker buy ratio over the last 15 minutes has run at 0.38 or less: a persistently seller-heavy tape.

What has happened on GWEIUSDT when it held

Historically, when One-sided tape: sellers on GWEIUSDT held (355 occurrences in the scanned window, 353 with a computable 24h horizon), the market reached +5% within 24h in 191 of 353 (54.1) and drew down 10%+ in 160 of 353 (45.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 355 MATCHES

15 of 355 (4.2%) 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. 355 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 355 matches

About 11 a day, on 28 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 355 measured, 0 too recent

Forward 30-minute returns across 355 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. 353 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 353 with a complete 24h.

Versus baseline

Historically, when any eligible GWEIUSDT minute in the window (baseline) held (36,401 occurrences in the scanned window, 34,959 with a computable 24h horizon), the market reached +5% within 24h in 19,275 of 34,959 (55.1) and drew down 10%+ in 16,809 of 34,959 (48.1). 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 2026-08-10), growing daily.

Historically, when One-sided tape: sellers on GWEIUSDT over the full record held (752 occurrences in the scanned window, 750 with a computable 24h horizon), the market reached +5% within 24h in 434 of 750 (57.9) and drew down 10%+ in 294 of 750 (39.2). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 752 MATCHES

30 of 752 (4.0%) 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. 752 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 752 matches

About 8.5 a day, on 64 of 88 days: spread through the record rather than one event.

Where the next 30 minutes landed - 751 measured, 1 too recent

Forward 30-minute returns across 751 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#05fd507c)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 6e71a2c7c51599fabbd8af31ef93b828abccae884d3765632ce11c434ef0d496
query hash: 05fd507cd80778a65e6aef937293cbbea3b2decf03d341fd7992b0a4f30a1203
scanned window: 2026-07-11T00:00:00.000Z to 2026-08-10T00:00:00.000Z (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.taker_buy_ratio_15m",
        "lte",
        0.38
      ],
      [
        "identity.symbol",
        "eq",
        "gweiusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-11T00:00:00.000Z",
          "2026-08-10T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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.taker_buy_ratio_15m","lte",0.38],["identity.symbol","eq","gweiusdt"],["times.anchor_time","between",["2026-07-11T00:00:00.000Z","2026-08-10T00:00:00.000Z"]]]},"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