HFTUSDT · historical base rate

Swept the lows and reclaimed (1h) on HFTUSDT

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 HFTUSDT when it held

Historically, when Swept the lows and reclaimed (1h) on HFTUSDT held (31 occurrences in the scanned window, 31 with a computable 24h horizon), the market reached +5% within 24h in 8 of 31 (25.8) and drew down 10%+ in 1 of 31 (3.2). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 31 MATCHES

0 of 31 (0.0%) reached +5% or more within 1 hour

Pick a move size and a window: how often did a move at least that big follow?

🔒 Measured after every match, never by keeping winners. 31 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 31 matches

About 1 a day, on 17 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 30 measured, 1 too recent

Forward 30-minute returns across 30 matches: 73% of them finished up.

How to read a distribution

Upside reached · 24hThe most price moved up within 24h of each occurrence: a high-water mark, never a close. 31 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 31 with a complete 24h.

Versus baseline

Historically, when any eligible HFTUSDT minute in the window (baseline) held (38,221 occurrences in the scanned window, 35,726 with a computable 24h horizon), the market reached +5% within 24h in 10,710 of 35,726 (30.0) and drew down 10%+ in 1,686 of 35,726 (4.7). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

The full record

The same setup counted over every recorded day on HFTUSDT (2026-05-15 to 2026-08-07), growing daily.

Historically, when Swept the lows and reclaimed (1h) on HFTUSDT over the full record held (78 occurrences in the scanned window, 77 with a computable 24h horizon), the market reached +5% within 24h in 20 of 77 (26.0) and drew down 10%+ in 3 of 77 (3.9). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

WHAT FOLLOWED · ALL 78 MATCHES

1 of 77 (1.3%) reached +5% or more within 1 hour

Pick a move size and a window: how often did a move at least that big follow?

🔒 Measured after every match, never by keeping winners. 77 of the matches have a complete 1 hour forward; the other 1 are too recent to know.

What are MFE and MAE?

When they happened - one bar per UTC day, all 78 matches

About 0.9 a day, on 42 of 85 days: spread through the record rather than one event.

Where the next 30 minutes landed - 76 measured, 2 too recent

Forward 30-minute returns across 76 matches: 59% of them finished up.

How to read a distribution

Check our work: the key that re-runs this exact count (q#dc0db039)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 2f157bbdb91d341d09d9341ea73c8ceeeeee8acbe51e910f3b77fb76954ba6be
query hash: dc0db03938fe0791c37535f3679e7a6302c92c6c0693c5dd2cd259ff48d8f0a5
scanned window: 2026-07-08T00:00:00.000Z to 2026-08-07T00:00:00.000Z (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",
        "hftusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-08T00:00:00.000Z",
          "2026-08-07T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","hftusdt"],["times.anchor_time","between",["2026-07-08T00:00:00.000Z","2026-08-07T00:00:00.000Z"]]]},"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 HFTUSDT right now: the HFTUSDT research page