One-sided tape: sellers on APTUSDT
Taker buy ratio over the last 15 minutes has run at 0.38 or less: a persistently seller-heavy tape.
What has happened on APTUSDT when it held
Historically, when One-sided tape: sellers on APTUSDT held (422 occurrences in the scanned window, 408 with a computable 24h horizon), the market reached +5% within 24h in 133 of 408 (32.6) and drew down 10%+ in 6 of 408 (1.5). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
Outcome distribution
384 of 421 (91.2%) 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. 421 of the matches have a complete 1 hour forward; the other 1 are too recent to know.
What are MFE and MAE?Why 72 hours can show fewer than 1 hour: a match too recent for a complete 72 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.
About 14 a day, on 30 of 31 days: spread through the record rather than one event.
Forward 30-minute returns across 421 matches: close to an even split, which is what a coin flip looks like.
How to read a distributionVersus baseline
Historically, when any eligible APTUSDT minute in the window (baseline) held (37,160 occurrences in the scanned window, 35,868 with a computable 24h horizon), the market reached +5% within 24h in 12,647 of 35,868 (35.3) and drew down 10%+ in 614 of 35,868 (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 APTUSDT (2026-08-19 to 2026-09-18), growing daily.
Historically, when One-sided tape: sellers on APTUSDT over the full record held (422 occurrences in the scanned window, 408 with a computable 24h horizon), the market reached +5% within 24h in 133 of 408 (32.6) and drew down 10%+ in 6 of 408 (1.5). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
384 of 421 (91.2%) 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. 421 of the matches have a complete 1 hour forward; the other 1 are too recent to know.
What are MFE and MAE?Why 72 hours can show fewer than 1 hour: a match too recent for a complete 72 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.
About 14 a day, on 30 of 31 days: spread through the record rather than one event.
Forward 30-minute returns across 421 matches: close to an even split, which is what a coin flip looks like.
How to read a distributionCheck our work: the key that re-runs this exact count (q#ba294f1c)
- grammar
- research_query.v2
- normalization
- archive_normalization.v1
- feature library
- feature_defs.v1
- dataset revision
- 8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
- query hash
- ba294f1c2a97dde2a2f12aa516161f3db82f10b6fc4c3941dcda175ba48fc0af
- 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.taker_buy_ratio_15m",
"lte",
0.38
],
[
"identity.symbol",
"eq",
"aptusdt"
],
[
"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.taker_buy_ratio_15m","lte",0.38],["identity.symbol","eq","aptusdt"],["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.
Everything measured on APTUSDT right now: the APTUSDT research page