STRASMORE/EXPLORE 2,985 QUERIES

resolution

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 2026-10-03, from open-source-trading-terminal-for-python.

as of series 21×4read in context →
resolution — 21 rows by 4 columns, computed from US exchange, SIP and OPRA data.
session_datesession_labeldaily_range_bpswidest_minute_bps
2026-06-01Jun 1193.352.9
2026-06-02Jun 2278.158.2
2026-06-03Jun 3260.742.2
2026-06-04Jun 412572.9
2026-06-05Jun 5260.953.8
2026-06-08Jun 8538.2151.6
2026-06-09Jun 9446.467.8
2026-06-10Jun 10252.856.8
2026-06-11Jun 11250.771.4
2026-06-12Jun 12258.360.7
2026-06-15Jun 15205.176.6
2026-06-16Jun 16217.640.4
2026-06-17Jun 17260.559.8
2026-06-18Jun 18166.157.6
2026-06-22Jun 22190.685.2
2026-06-23Jun 23253.5105.9
2026-06-24Jun 24230.773.8
2026-06-25Jun 25547140.9
2026-06-26Jun 26413.7132.9
2026-06-29Jun 29302.489.7
2026-06-30Jun 30319.584.3
Rows × columns
21 × 4
Period covered
to
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 resolution, derived from the stored result.
ColumnTypeRangeNotes
session_date date 2026-06-01 to 2026-06-30
session_label text 21 distinct values (Jun 1, Jun 10, Jun 11…)
daily_range_bps number 125 to 547
widest_minute_bps number 40.4 to 151.6

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.

SELECT
    toString(bars.session)                AS session_date,
    formatDateTime(bars.session, '%b %e') AS session_label,
    round((toFloat64(bars.high) - toFloat64(bars.low)) / toFloat64(bars.close) * 10000, 1) AS daily_range_bps,
    round(mins.widest_minute_bps, 1)      AS widest_minute_bps
FROM
(
    SELECT date AS session, high, low, close
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'AAPL'
      AND date >= '2026-06-01'
      AND date <  '2026-07-01'
) AS bars
INNER JOIN
(
    SELECT
        toDate(toTimeZone(window_start, 'America/New_York')) AS session,
        max((toFloat64(high) - toFloat64(low)) / toFloat64(close) * 10000) AS widest_minute_bps
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'AAPL'
      AND window_start >= '2026-06-01 04:00:00'
      AND window_start <  '2026-07-01 04:00:00'
      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 session
) AS mins ON mins.session = bars.session
ORDER BY bars.session
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