< All conditionsLive condition · historical base rate

Crowd long, leaders not

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

Firing now (provisional, current bucket)

The live read is not yet enabled. This surface reports the historical base rates below; the firing-now list turns on when the live evaluator is connected.

What has happened when it held

Historically, when Crowd long, leaders not held (6,739 occurrences in the scanned window, 5,459 with a computable 24h horizon), the market reached +5% within 24h in 1,232 of 5,459 (22.6) and drew down 10%+ in 434 of 5,459 (8.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED - ALL 6,739 MATCHES, FROM THE PER-SCAN SUMMARY

120 OF 5,708 (2.1%) REACHED ≥ +5% WITHIN 1H

METRIC MFE_1H · CHIPS RE-READ THE SUMMARY, NEVER RE-QUERY

🔒 COMPUTED FORWARD, NEVER A FILTER · DENOMINATOR 5,708 = MATCHES WITH A COMPLETE 1H HORIZON (1,031 ABSENT: TOO CLOSE TO THE RECORD EDGE FOR 1H - NEVER TRUNCATED, NEVER AN IMPLIED ZERO)

OCCURRENCES PER DAY · UTC · SUM 6739 - ALL 6,739, NOT THE PAGE

About 217 a day, on 29 of 31 days: spread through the record rather than one event.

FWD_RET_30M DISTRIBUTION · 4,992 PRESENT · 1,747 ABSENT

Forward 30-minute returns across 4,992 matches: close to an even split, which is what a coin flip looks like.

Versus baseline

Historically, when any eligible minute in the window (baseline) held (20,460,611 occurrences in the scanned window, 18,323,605 with a computable 24h horizon), the market reached +5% within 24h in 4,215,038 of 18,323,605 (23.0) and drew down 10%+ in 1,055,783 of 18,323,605 (5.8). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

By market

Markets where this condition occurred often enough in the window to carry its own statistically meaningful receipt:

1000PEPEUSDT ARBUSDT SOLUSDT

Reproducibility key (q#c4128560)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 52a43a418a97fbe3a324f7c372fa4d53967163acaa0ed33c56b78ddd897331a4
query hash: c4128560d6fcbc33eeb5f777af5d24cbd7a65b8f5395e7c0735dc3f592a990e1
scanned window: 2026-07-01T00:00:00.000Z to 2026-07-31T00:00:00.000Z (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 over the API or MCP

This is the exact research_query.v2 definition behind the receipt. Send it to the key-authed research API (or the MCP research tool) and diff 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
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-01T00:00:00.000Z",
          "2026-07-31T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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],["times.anchor_time","between",["2026-07-01T00:00:00.000Z","2026-07-31T00:00:00.000Z"]]]},"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.