GWEIUSDT · historical base rate

Swept the lows and reclaimed (1h) on GWEIUSDT

The last completed one-hour candle swept prior lows and closed back above them: the "bullish" sweep and reclaim formation on the hourly.

What has happened on GWEIUSDT when it held

Historically, when Swept the lows and reclaimed (1h) on GWEIUSDT held (48 occurrences in the scanned window, 48 with a computable 24h horizon), the market reached +5% within 24h in 24 of 48 (50.0) and drew down 10%+ in 21 of 48 (43.8). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 48 MATCHES

8 of 48 (16.7%) 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. 48 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 48 matches

About 1.5 a day, on 23 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 48 measured, 0 too recent

Forward 30-minute returns across 48 matches: 58% 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. 48 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 48 with a complete 24h.

Versus baseline

Historically, when any eligible GWEIUSDT minute in the window (baseline) held (38,281 occurrences in the scanned window, 36,824 with a computable 24h horizon), the market reached +5% within 24h in 20,695 of 36,824 (56.2) and drew down 10%+ in 17,510 of 36,824 (47.6). 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 2026-08-10), growing daily.

Historically, when Swept the lows and reclaimed (1h) on GWEIUSDT over the full record held (101 occurrences in the scanned window, 101 with a computable 24h horizon), the market reached +5% within 24h in 57 of 101 (56.4) and drew down 10%+ in 42 of 101 (41.6). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 101 MATCHES

14 of 101 (13.9%) 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. 101 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 101 matches

About 1.1 a day, on 47 of 88 days: spread through the record rather than one event.

Where the next 30 minutes landed - 101 measured, 0 too recent

Forward 30-minute returns across 101 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#15aa0899)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 6e71a2c7c51599fabbd8af31ef93b828abccae884d3765632ce11c434ef0d496
query hash: 15aa0899f9a836fed1573743076ed2d78952e89b059277d5990de8e8485830c9
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.candle_sweep_reclaim_1h",
        "eq",
        "bullish"
      ],
      [
        "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.candle_sweep_reclaim_1h","eq","bullish"],["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