Crowd long, leaders not on XMRUSDT
Global accounts sit at least 65% long while top traders run at least 10 points less long than the crowd.
What has happened on XMRUSDT when it held
Historically, when Crowd long, leaders not on XMRUSDT held (20 occurrences in the scanned window, 19 with a computable 24h horizon), the market reached +5% within 24h in 7 of 19 (36.8) and drew down 10%+ in 0 of 19 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
Outcome distribution
16 of 20 (80.0%) 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?
🔒 Measured after every match, never by keeping winners. 20 of the matches have a complete 1 hour forward.
What are MFE and MAE?Why 7 days can show fewer than 1 hour: a match too recent for a complete 7 days is left out of that count entirely, even if it already reached the level within 1 hour. The two counts cover different sets of matches.
About 0.6 a day over 31 days, but 2026-08-29 alone holds 55% of them: this is close to a single episode.
Forward 30-minute returns across 20 matches: 60% of them finished up.
How to read a distributionVersus baseline
Historically, when any eligible XMRUSDT minute in the window (baseline) held (42,216 occurrences in the scanned window, 40,811 with a computable 24h horizon), the market reached +5% within 24h in 12,462 of 40,811 (30.5) and drew down 10%+ in 91 of 40,811 (0.2). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
The full record
The same setup counted over every recorded day on XMRUSDT (2026-08-08 to 2026-09-07), growing daily.
Historically, when Crowd long, leaders not on XMRUSDT over the full record held (20 occurrences in the scanned window, 19 with a computable 24h horizon), the market reached +5% within 24h in 7 of 19 (36.8) and drew down 10%+ in 0 of 19 (0.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
16 of 20 (80.0%) 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?
🔒 Measured after every match, never by keeping winners. 20 of the matches have a complete 1 hour forward.
What are MFE and MAE?Why 7 days can show fewer than 1 hour: a match too recent for a complete 7 days is left out of that count entirely, even if it already reached the level within 1 hour. The two counts cover different sets of matches.
About 0.6 a day over 31 days, but 2026-08-29 alone holds 55% of them: this is close to a single episode.
Forward 30-minute returns across 20 matches: 60% of them finished up.
How to read a distributionCheck our work: the key that re-runs this exact count (q#aa185c4e)
- grammar
- research_query.v2
- normalization
- archive_normalization.v1
- feature library
- feature_defs.v1
- dataset revision
- c5654b7b634bee0cdfb2e8766a962014a6487b56594e8bce014ec5c87d905ae0
- query hash
- aa185c4eebcb672b1082d989e2058888cf41413dae475adde3405fa41dc5fefa
- scanned window
- 2026-08-08T00:00:00.000Z to 2026-09-07T00:00:00.000Z (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",
"xmrusdt"
],
[
"times.anchor_time",
"between",
[
"2026-08-08T00:00:00.000Z",
"2026-09-07T00:00:00.000Z"
]
]
]
},
"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","xmrusdt"],["times.anchor_time","between",["2026-08-08T00:00:00.000Z","2026-09-07T00:00:00.000Z"]]]},"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 XMRUSDT right now: the XMRUSDT research page