One-sided tape: sellers on CYSUSDT
Taker buy ratio over the last 15 minutes has run at 0.38 or less: a persistently seller-heavy tape.
What has happened on CYSUSDT when it held
Historically, when One-sided tape: sellers on CYSUSDT held (111 occurrences in the scanned window, 107 with a computable 24h horizon), the market reached +5% within 24h in 84 of 107 (78.5) and drew down 10%+ in 26 of 107 (24.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
Outcome distribution
103 of 111 (92.8%) 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. 111 of the matches have a complete 1 hour forward.
What are MFE and MAE?Why 24 hours can show fewer than 4 hours: a match too recent for a complete 24 hours 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.
About 3.6 a day, on 16 of 31 days: spread through the record rather than one event.
Forward 30-minute returns across 111 matches: close to an even split, which is what a coin flip looks like.
How to read a distributionVersus baseline
Historically, when any eligible CYSUSDT minute in the window (baseline) held (40,576 occurrences in the scanned window, 39,171 with a computable 24h horizon), the market reached +5% within 24h in 32,306 of 39,171 (82.5) and drew down 10%+ in 19,781 of 39,171 (50.5). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
The full record
The same setup counted over every recorded day on CYSUSDT (2026-08-05 to 2026-09-04), growing daily.
Historically, when One-sided tape: sellers on CYSUSDT over the full record held (111 occurrences in the scanned window, 107 with a computable 24h horizon), the market reached +5% within 24h in 84 of 107 (78.5) and drew down 10%+ in 26 of 107 (24.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.
103 of 111 (92.8%) 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. 111 of the matches have a complete 1 hour forward.
What are MFE and MAE?Why 24 hours can show fewer than 4 hours: a match too recent for a complete 24 hours 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.
About 3.6 a day, on 16 of 31 days: spread through the record rather than one event.
Forward 30-minute returns across 111 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#bda2f18e)
- grammar
- research_query.v2
- normalization
- archive_normalization.v1
- feature library
- feature_defs.v1
- dataset revision
- 22c65ef31c9be5c31ef90e9439d596aa036d28155c96d193417c21c89b686ff9
- query hash
- bda2f18ef372e31582b1907c15b323664e75db5ed2178748db3246d67163b92c
- scanned window
- 2026-08-05T00:00:00.000Z to 2026-09-04T00:00:00.000Z (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",
"cysusdt"
],
[
"times.anchor_time",
"between",
[
"2026-08-05T00:00:00.000Z",
"2026-09-04T00:00:00.000Z"
]
]
]
},
"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","cysusdt"],["times.anchor_time","between",["2026-08-05T00:00:00.000Z","2026-09-04T00:00:00.000Z"]]]},"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 CYSUSDT right now: the CYSUSDT research page