4USDT · historical base rate

Crowd long, leaders not on 4USDT

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

What has happened on 4USDT when it held

Historically, when Crowd long, leaders not on 4USDT held (30 occurrences in the scanned window, 30 with a computable 24h horizon), the market reached +5% within 24h in 19 of 30 (63.3) and drew down 10%+ in 9 of 30 (30.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 30 MATCHES

29 of 30 (96.7%) reached +0% or more within 1 hour · smallest move size with matches, picked for you

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

What are MFE and MAE?

When they happened - one bar per UTC day, all 30 matches

About 1 a day, on 11 of 31 days: spread through the record rather than one event.

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

Forward 30-minute returns across 30 matches: 60% 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. 30 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 30 with a complete 24h.

Versus baseline

Historically, when any eligible 4USDT minute in the window (baseline) held (14,286 occurrences in the scanned window, 12,844 with a computable 24h horizon), the market reached +5% within 24h in 7,130 of 12,844 (55.5) and drew down 10%+ in 5,049 of 12,844 (39.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on 4USDT (2026-08-02 to 2026-09-01), growing daily.

Historically, when Crowd long, leaders not on 4USDT over the full record held (30 occurrences in the scanned window, 30 with a computable 24h horizon), the market reached +5% within 24h in 19 of 30 (63.3) and drew down 10%+ in 9 of 30 (30.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 30 MATCHES

29 of 30 (96.7%) reached +0% or more within 1 hour · smallest move size with matches, picked for you

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

What are MFE and MAE?

When they happened - one bar per UTC day, all 30 matches

About 1 a day, on 11 of 31 days: spread through the record rather than one event.

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

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

How to read a distribution

Check our work: the key that re-runs this exact count (q#58de5952)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 9bf4ae595eb428a9fafa01232b309eefc814fb6c5e92d9f5fb113f72f9fdc836
query hash: 58de5952dcdbe83368cea7e773f54ff9b1958b6c02a1bf974bfaf5c344b6b75d
scanned window: 2026-08-02T00:00:00.000Z to 2026-09-01T00:00:00.000Z (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",
        "4usdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-08-02T00:00:00.000Z",
          "2026-09-01T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","4usdt"],["times.anchor_time","between",["2026-08-02T00:00:00.000Z","2026-09-01T00:00:00.000Z"]]]},"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 4USDT right now: the 4USDT research page