STRASMORE/EXPLORE 3,214 QUERIES

perfil_horario_madrid

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 2026-10-08, from us-stock-market-hours-madrid-time.

as of table 16×5read in context →
perfil_horario_madrid — 16 rows by 5 columns, computed from US exchange, SIP and OPRA data.
hora_madridhora_nycuota_pctmillones_por_sesionfin_del_tramo
10:0004:000.240.110:59
11:0005:000.110.0512:00
12:0006:000.170.0713:00
13:0007:000.480.2114:00
14:0008:001.030.4415:00
15:0009:009.84.1716:00
16:0010:0012.585.3617:00
17:0011:0011.374.8418:00
18:0012:007.713.2819:00
19:0013:007.113.0320:00
20:0014:009.964.2421:00
21:0015:0024.8410.5822:00
22:0016:0013.495.7523:00
23:0017:000.760.3200:00
00:0018:000.260.1100:58
01:0019:000.090.0402:00
Rows × columns
16 × 5
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 perfil_horario_madrid, derived from the stored result.
ColumnTypeRangeNotes
hora_madrid text 16 distinct values (00:00, 01:00, 10:00…)
hora_ny text 16 distinct values (04:00, 05:00, 06:00…)
cuota_pct number 0.09 to 24.84 percent
millones_por_sesion number 0.04 to 10.58
fin_del_tramo text 16 distinct values (00:00, 00:58, 02:00…)

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 sum(volume)
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY'
          AND window_start >= '2026-09-01 00:00:00'
          AND window_start <  '2026-10-07 00:00:00'
    ) AS volumen_total
SELECT
    concat(leftPad(toString(hora), 2, '0'), ':00')                      AS hora_madrid,
    min(formatDateTime(reloj_ny, '%H:00'))                              AS hora_ny,
    round(100 * toFloat64(sum(volumen)) / toFloat64(volumen_total), 2)  AS cuota_pct,
    round(toFloat64(sum(volumen)) / countDistinct(sesion) / 1e6, 2)     AS millones_por_sesion,
    formatDateTime(toTimeZone(max(inicio) + toIntervalMinute(1), 'Europe/Madrid'), '%H:%i') AS fin_del_tramo
FROM
(
    SELECT
        window_start                                          AS inicio,
        toHour(toTimeZone(window_start, 'Europe/Madrid'))     AS hora,
        toTimeZone(window_start, 'America/New_York')          AS reloj_ny,
        toDate(toTimeZone(window_start, 'America/New_York'))  AS sesion,
        volume                                                AS volumen
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND window_start >= '2026-09-01 00:00:00'
      AND window_start <  '2026-10-07 00:00:00'
)
GROUP BY hora
ORDER BY if(hora < 4, hora + 24, hora)
⌘/Ctrl + Enter

Work with this data in your AI assistant

Opens ready to query, with this page's data. Free, no account.