FARTCOINUSDT · historical base rate

Crowd long, leaders not on FARTCOINUSDT

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

What has happened on FARTCOINUSDT when it held

Historically, when Crowd long, leaders not on FARTCOINUSDT held (24 occurrences in the scanned window, 18 with a computable 24h horizon), the market reached +5% within 24h in 8 of 18 (44.4) and drew down 10%+ in 6 of 18 (33.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 24 MATCHES

22 of 24 (91.7%) 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. 24 of the matches have a complete 1 hour forward.

What are MFE and MAE?

Why 24 hours can show fewer than 1 hour: a match too recent for a complete 24 hours 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.

When they happened - one bar per UTC day, all 24 matches

About 0.8 a day, on 9 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 24 measured, 0 too recent

Forward 30-minute returns across 24 matches: 67% 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. 18 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 18 with a complete 24h.

Versus baseline

Historically, when any eligible FARTCOINUSDT minute in the window (baseline) held (23,751 occurrences in the scanned window, 23,309 with a computable 24h horizon), the market reached +5% within 24h in 9,813 of 23,309 (42.1) and drew down 10%+ in 385 of 23,309 (1.7). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on FARTCOINUSDT (2026-05-15 to 2026-08-14), growing daily.

Historically, when Crowd long, leaders not on FARTCOINUSDT over the full record held (26 occurrences in the scanned window, 25 with a computable 24h horizon), the market reached +5% within 24h in 7 of 25 (28.0) and drew down 10%+ in 4 of 25 (16.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 26 MATCHES

10 of 26 (38.5%) reached +1% 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. 26 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 26 matches

About 0.3 a day, on 10 of 92 days: spread through the record rather than one event.

Where the next 30 minutes landed - 26 measured, 0 too recent

Forward 30-minute returns across 26 matches: 58% of them finished up.

How to read a distribution

Check our work: the key that re-runs this exact count (q#160f09fa)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 4d13215e558bbcbb2f787b91f7e61d8eba747c3454741cb9a9ed917fa9f745bc
query hash: 160f09fa8526ca3d7ffb9bdeac09e4e941d055fd28f4534f715c1ba117e18117
scanned window: 2026-07-30T00:00:00.000Z to 2026-08-29T00:00:00.000Z (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",
        "fartcoinusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-30T00:00:00.000Z",
          "2026-08-29T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","fartcoinusdt"],["times.anchor_time","between",["2026-07-30T00:00:00.000Z","2026-08-29T00:00:00.000Z"]]]},"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 FARTCOINUSDT right now: the FARTCOINUSDT research page