TACUSDT · historical base rate

Swept the lows and reclaimed (1h) on TACUSDT

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 TACUSDT when it held

Historically, when Swept the lows and reclaimed (1h) on TACUSDT held (39 occurrences in the scanned window, 39 with a computable 24h horizon), the market reached +5% within 24h in 11 of 39 (28.2) and drew down 10%+ in 2 of 39 (5.1). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 39 MATCHES

34 of 39 (87.2%) 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?

WINDOWMOVE SIZE

🔒 Measured after every match, never by keeping winners. 39 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 39 matches

About 1.3 a day, on 20 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 39 measured, 0 too recent

Forward 30-minute returns across 39 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. 39 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 39 with a complete 24h.

Versus baseline

Historically, when any eligible TACUSDT minute in the window (baseline) held (38,941 occurrences in the scanned window, 37,303 with a computable 24h horizon), the market reached +5% within 24h in 11,606 of 37,303 (31.1) and drew down 10%+ in 3,684 of 37,303 (9.9). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Check our work: the key that re-runs this exact count (q#567ae0c9)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 896a4727caf667cfa34cd73a7db512a14144336387da6c22d2f716650335af30
query hash: 567ae0c9a956eac84c13d136454a56f79357895ce5249cc12b9bc68f785ce2fc
scanned window: 2026-07-28T00:00:00.000Z to 2026-08-27T00:00:00.000Z (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",
        "tacusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-28T00:00:00.000Z",
          "2026-08-27T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","tacusdt"],["times.anchor_time","between",["2026-07-28T00:00:00.000Z","2026-08-27T00:00:00.000Z"]]]},"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 TACUSDT right now: the TACUSDT research page