AINUSDT · historical base rate

Swept the lows and reclaimed (1h) on AINUSDT

The last completed one-hour candle swept prior lows and closed back above them: the "bullish" sweep and reclaim formation on the hourly.

What has happened on AINUSDT when it held

Historically, when Swept the lows and reclaimed (1h) on AINUSDT held (54 occurrences in the scanned window, 48 with a computable 24h horizon), the market reached +5% within 24h in 11 of 48 (22.9) and drew down 10%+ in 4 of 48 (8.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 54 MATCHES

43 of 51 (84.3%) 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. 51 of the matches have a complete 1 hour forward; the other 3 are too recent to know.

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.

When they happened - one bar per UTC day, all 54 matches

About 1.7 a day, on 22 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 50 measured, 4 too recent

Forward 30-minute returns across 50 matches: close to an even split, which is what a coin flip looks like.

How to read a distribution

Upside reached · 24hThe most price moved up within 24h of each occurrence: a high-water mark, never a close. 48 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 48 with a complete 24h.

Versus baseline

Historically, when any eligible AINUSDT minute in the window (baseline) held (39,061 occurrences in the scanned window, 37,580 with a computable 24h horizon), the market reached +5% within 24h in 11,733 of 37,580 (31.2) and drew down 10%+ in 4,403 of 37,580 (11.7). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on AINUSDT (2026-08-19 to 2026-09-18), growing daily.

Historically, when Swept the lows and reclaimed (1h) on AINUSDT over the full record held (54 occurrences in the scanned window, 48 with a computable 24h horizon), the market reached +5% within 24h in 11 of 48 (22.9) and drew down 10%+ in 4 of 48 (8.3). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 54 MATCHES

43 of 51 (84.3%) 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. 51 of the matches have a complete 1 hour forward; the other 3 are too recent to know.

What are MFE and MAE?

When they happened - one bar per UTC day, all 54 matches

About 1.7 a day, on 22 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 50 measured, 4 too recent

Forward 30-minute returns across 50 matches: close to an even split, which is what a coin flip looks like.

How to read a distribution

Check our work: the key that re-runs this exact count (q#0b5dbbea)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 8904ef0442c6f623ec772df2b59d7baf1e49aaa808b5b11ac31546605549e3bb
query hash: 0b5dbbea93561170bd7564e92f4a76a91fe3ae24e127964391a6d74c63a2ebc9
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.candle_sweep_reclaim_1h",
        "eq",
        "bullish"
      ],
      [
        "identity.symbol",
        "eq",
        "ainusdt"
      ],
      [
        "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.candle_sweep_reclaim_1h","eq","bullish"],["identity.symbol","eq","ainusdt"],["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 AINUSDT right now: the AINUSDT research page