LITUSDT · historical base rate

Swept the lows and reclaimed (1h) on LITUSDT

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 LITUSDT when it held

Historically, when Swept the lows and reclaimed (1h) on LITUSDT held (43 occurrences in the scanned window, 42 with a computable 24h horizon), the market reached +5% within 24h in 17 of 42 (40.5) and drew down 10%+ in 2 of 42 (4.8). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Outcome distribution

WHAT FOLLOWED · ALL 43 MATCHES

0 of 43 (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. 43 of the matches have a complete 1 hour forward.

What are MFE and MAE?

When they happened - one bar per UTC day, all 43 matches

About 1.4 a day, on 23 of 31 days: spread through the record rather than one event.

Where the next 30 minutes landed - 43 measured, 0 too recent

Forward 30-minute returns across 43 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. 42 with a complete 24h.

Drawdown · 24hThe worst price moved against within 24h of each occurrence: the deepest point, never the close. 42 with a complete 24h.

Versus baseline

Historically, when any eligible LITUSDT minute in the window (baseline) held (37,261 occurrences in the scanned window, 35,876 with a computable 24h horizon), the market reached +5% within 24h in 11,834 of 35,876 (33.0) and drew down 10%+ in 1,421 of 35,876 (4.0). Maximum favorable (MFE) and maximum adverse (MAE) excursions, both computed forward.

Check our work: the key that re-runs this exact count (q#e5cc6c09)

grammar: research_query.v2
normalization: archive_normalization.v1
feature library: feature_defs.v1
dataset revision: 98dac62d96ea00e2e3b010d474acfd4a19e1ea7b4541e366a60e7da20c67fe00
query hash: e5cc6c0977d02293f692399a8fc5213518a3af195eebe282171b6c90524d499a
scanned window: 2026-07-24T00:00:00.000Z to 2026-08-23T00:00:00.000Z (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",
        "litusdt"
      ],
      [
        "times.anchor_time",
        "between",
        [
          "2026-07-24T00:00:00.000Z",
          "2026-08-23T00:00:00.000Z"
        ]
      ]
    ]
  },
  "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","litusdt"],["times.anchor_time","between",["2026-07-24T00:00:00.000Z","2026-08-23T00:00:00.000Z"]]]},"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 LITUSDT right now: the LITUSDT research page