STRASMORE/EXPLORE 3,293 QUERIES

Inside the widest 2025 SPY sweep day: fifteen-minute high, low and volume

Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-09-28, from What Is a Liquidity Sweep in Trading?.

as of series 26×4read in context →
Inside the widest 2025 SPY sweep day: fifteen-minute high, low and volume — 26 rows by 4 columns, computed from US exchange, SIP and OPRA data.
et_timeslice_highslice_lowvolume_millions
09:30674.15672.478.05
09:45675.02672.964.51
10:00675.41672.794.18
10:15675.02672.823.44
10:30675.56674.312.48
10:45675.32672.743.63
11:00672.82669.854.59
11:15669.97667.427.76
11:30668.17664.137.29
11:45665.79659.399.6
12:00662.38658.039.44
12:15659.48656.69.03
12:30659.54655.456.84
12:45657.88655.495.41
13:00661.85656.726.48
13:15661.97658.734.31
13:30660656.243.38
13:45656.4654.124.63
14:00655.85653.865.14
14:15657.54654.852.81
14:30656.99655.213.74
14:45658.25655.34.28
15:00658.13655.724.22
15:15657.53655.354.54
15:30656.56653.136.47
15:45653.96651.8914.73
Rows × columns
26 × 4
Computed
Completeness
No missing values
Source
US exchange, SIP and OPRA market data
Licence
Strasmore terms · free, no signup
Formats
JSON · CSV · the SQL below

What each column holds

Column definitions for Inside the widest 2025 SPY sweep day: fifteen-minute high, low and volume, derived from the stored result.
ColumnTypeRangeNotes
et_time text 26 distinct values (09:30, 09:45, 10:00…)
slice_high number 653.96 to 675.56 US dollars
slice_low number 651.89 to 674.31 US dollars
volume_millions number 2.48 to 14.73 count

Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on , and the query below is what returned them.

Run it yourself

This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.

WITH
  (
      SELECT toDate(date)
      FROM
      (
          SELECT
              date,
              hi,
              cl,
              lagInFrame(hi) OVER (ORDER BY date ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prior_hi
          FROM
          (
              SELECT
                  date,
                  argMax(high,  _ingest_time) AS hi,
                  argMax(close, _ingest_time) AS cl
              FROM global_markets.stocks_daily_aggs
              WHERE ticker = 'SPY'
                AND date >= '2024-12-31'
                AND date <  '2026-01-01'
              GROUP BY date
          )
      )
      WHERE date >= '2025-01-01'
        AND prior_hi > 0
        AND hi > prior_hi
        AND cl < prior_hi
      ORDER BY (toFloat64(hi) - toFloat64(prior_hi)) / toFloat64(prior_hi) DESC, date DESC
      LIMIT 1
  ) AS sweep_day,
  (
      SELECT toFloat64(maxIf(size, has(conditions, 8)))
      FROM global_markets.stocks_trades
      WHERE ticker = 'SPY'
        AND sip_timestamp >= toDateTime(sweep_day, 'UTC') + 68400
        AND sip_timestamp <  toDateTime(sweep_day, 'UTC') + 79200
  ) AS closing_print
SELECT
    formatDateTime(toStartOfFifteenMinutes(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS et_time,
    round(toFloat64(max(high)), 2)          AS slice_high,
    round(toFloat64(min(low)), 2)           AS slice_low,
    round((toFloat64(sum(volume)) + if(et_time = '15:45', closing_print, 0)) / 1e6, 2)  AS volume_millions
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
  AND window_start >= '2025-01-01'
  AND window_start <  '2026-01-02'
  AND toDate(toTimeZone(window_start, 'America/New_York')) = sweep_day
  AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
       + toMinute(toTimeZone(window_start, 'America/New_York'))) >= 570
  AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
       + toMinute(toTimeZone(window_start, 'America/New_York'))) < 960
GROUP BY et_time
ORDER BY et_time
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