Live condition · historical base rate

Whale prints carrying a third of volume

Large trades have accounted for at least a third of traded volume over the last 15 minutes.

What is order flow?

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 Whale prints carrying a third of volume held (10,768 occurrences in the scanned window, 10,381 with a computable 24h horizon), the market reached +5% within 24h in 1,963 of 10,381 (18.9) and drew down 10%+ in 297 of 10,381 (2.9). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 10,768 MATCHES

7,958 of 10,737 (74.1%) 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. 10,737 of the matches have a complete 1 hour forward; the other 31 are too recent to know.

What are MFE and MAE?

Why 7 days can show fewer than 4 hours: a match too recent for a complete 7 days is left out of that count entirely, even if it already reached the level within 4 hours. The two counts cover different sets of matches.

When they happened - one bar per UTC day, all 10,768 matches
4180209MATCHES PER UTC DAY · MAX 418 ON 2026-09-0441533336127936533733438535037531231236637135939641830432234037138840636230540637636238237608-2408-3109-0709-142026-08-19 TO 2026-09-18 · 31 UTC DAYS · ZERO DAYS KEEP THEIR SLOT

About 347 a day, on 30 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 10,634 measured, 134 too recent
≥ +0: 3552 OF 106341325629772513949971718188910041375734339811531≤−100−15−10−5−2−1−0.5−0.2−0.1<00++0.1+0.2+0.5+1+2+5+10+30≥+400

Forward 30-minute returns across 10,634 matches: close to an even split, which is what a coin flip looks like.

How to read a distribution
Upside reached · 24hUpside reached (MFE, 24h)The most price moved up within 24h of each occurrence: a high-water mark, never a close. 10,381 with a complete 24h.
Drawdown · 24hDrawdown (MAE, 24h)The worst price moved against within 24h of each occurrence: the deepest point, never the close. 10,381 with a complete 24h.

Versus baseline

Historically, when any eligible minute in the window (baseline) held (29,997,927 occurrences in the scanned window, 26,399,798 with a computable 24h horizon), the market reached +5% within 24h in 7,706,964 of 26,399,798 (29.2) and drew down 10%+ in 1,637,927 of 26,399,798 (6.2). 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:

Check our work: the key that re-runs this exact count (q#11c89659)
grammar
research_query.v2
normalization
archive_normalization.v1
feature library
feature_defs.v1
dataset revision
8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
query hash
11c896593ce7de332fae7d4832b388feb152ccb00c97584fd31ce2ddb537bd5d
scanned window
2026-08-19T00:00:00.000Z to 2026-09-18T00:00:00.000Z (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.large_trade_share_15m",
        "gte",
        0.33
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-08-19T00:00:00.000Z",
          "2026-09-18T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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.large_trade_share_15m","gte",0.33],["times.anchor_time","between",["2026-08-19T00:00:00.000Z","2026-09-18T00:00:00.000Z"]]]},"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.