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SPY volume and one-minute range by 30-minute bucket

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 2026-11-07, from US Market Hours in German Time (CET/CEST).

as of series 16×4read in context →
SPY volume and one-minute range by 30-minute bucket — 16 rows by 4 columns, computed from US exchange, SIP and OPRA data.
et_timegerman_timeavg_volume_millionsavg_range_pct
09:0015:000.260.021
09:3015:304.080.056
10:0016:002.960.048
10:3016:302.560.044
11:0017:002.730.04
11:3017:301.980.035
12:0018:001.70.032
12:3018:301.660.032
13:0019:001.740.032
13:3019:301.550.028
14:0020:001.960.03
14:3020:302.390.032
15:0021:002.670.031
15:3021:308.270.037
16:0022:006.440.022
16:3022:300.310.011
Rows × columns
16 × 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 SPY volume and one-minute range by 30-minute bucket, derived from the stored result.
ColumnTypeRangeNotes
et_time text 16 distinct values (09:00, 09:30, 10:00…)
german_time text 16 distinct values (15:00, 15:30, 16:00…)
avg_volume_millions number 0.26 to 8.27 count
avg_range_pct number 0.011 to 0.056 percent

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 toHour(toTimeZone(toDateTime(concat(toString(max(date)), ' 12:00:00'), 'America/New_York'), 'Europe/Berlin')) - 12
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'SPY'
    ) AS german_offset_hours,
    (
        SELECT sum(closing_print)
        FROM
        (
            SELECT toDate(toTimeZone(sip_timestamp, 'America/New_York')) AS trade_date,
                   toFloat64(maxIf(size, has(conditions, 8)))            AS closing_print
            FROM global_markets.stocks_trades
            WHERE ticker = 'SPY'
              AND sip_timestamp >= today() - 60
              AND sip_timestamp <  today() - 1
            GROUP BY trade_date
        )
    ) AS closing_prints_total
SELECT
    formatDateTime(toDateTime(bucket_min * 60, 'UTC'), '%H:%i')                                            AS et_time,
    formatDateTime(toDateTime(((bucket_min + german_offset_hours * 60) % 1440) * 60, 'UTC'), '%H:%i')       AS german_time,
    round((toFloat64(sum(volume)) + if(bucket_min = 960, closing_prints_total, 0))
          / countDistinct(session_date) / 1e6, 2)                                                           AS avg_volume_millions,
    round(avg(bar_range_pct), 3)                                                                           AS avg_range_pct
FROM
(
    SELECT
        toDate(et)                                     AS session_date,
        intDiv(toHour(et) * 60 + toMinute(et), 30) * 30 AS bucket_min,
        volume,
        toFloat64(high - low) / toFloat64(close) * 100  AS bar_range_pct
    FROM
    (
        SELECT
            toTimeZone(window_start, 'America/New_York') AS et,
            volume,
            high,
            low,
            close
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY'
          AND window_start >= today() - 60
          AND window_start <  today() - 1
    )
    WHERE (toHour(et) * 60 + toMinute(et)) >= 540
      AND (toHour(et) * 60 + toMinute(et)) <  1020
)
GROUP BY bucket_min
ORDER BY bucket_min
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