STRASMORE/EXPLORE 3,293 QUERIES

wat_clock

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 2026-10-16, from options-expiration-day-clock-lagos-time.

as of series 7×3read in context →
wat_clock — 7 rows by 3 columns, computed from US exchange, SIP and OPRA data.
wat_timeexpiry_friday_pctother_friday_pct
14:0010.711.38
15:0016.9815.71
16:0017.3812.76
17:009.9210.75
18:009.179.97
19:0011.0111.63
20:0024.8427.79
Rows × columns
7 × 3
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 wat_clock, derived from the stored result.
ColumnTypeRangeNotes
wat_time text 7 distinct values (14:00, 15:00, 16:00…)
expiry_friday_pct number 9.17 to 24.84 percent
other_friday_pct number 9.97 to 27.79 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 session_bars AS
(
    SELECT
        toTimeZone(window_start, 'America/New_York') AS et_ts,
        toTimeZone(window_start, 'Africa/Lagos')     AS wat_ts,
        toFloat64(volume)                            AS vol
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND window_start >= toDateTime('2026-03-13 00:00:00')
      AND window_start <  toDateTime('2026-10-10 00:00:00')
      AND toDayOfWeek(toTimeZone(window_start, 'America/New_York')) = 5
      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
)
SELECT
    hourly.wat_time                                       AS wat_time,
    round(100 * hourly.expiry_vol / totals.expiry_vol, 2) AS expiry_friday_pct,
    round(100 * hourly.other_vol / totals.other_vol, 2)   AS other_friday_pct
FROM
(
    SELECT
        formatDateTime(toStartOfHour(wat_ts), '%H:00')          AS wat_time,
        sumIf(vol, toDayOfMonth(et_ts) BETWEEN 15 AND 21)       AS expiry_vol,
        sumIf(vol, NOT (toDayOfMonth(et_ts) BETWEEN 15 AND 21)) AS other_vol
    FROM session_bars
    GROUP BY wat_time
    HAVING countIf(toDayOfMonth(et_ts) BETWEEN 15 AND 21) > 0
       AND countIf(NOT (toDayOfMonth(et_ts) BETWEEN 15 AND 21)) > 0
) AS hourly
CROSS JOIN
(
    SELECT
        sumIf(vol, toDayOfMonth(et_ts) BETWEEN 15 AND 21)       AS expiry_vol,
        sumIf(vol, NOT (toDayOfMonth(et_ts) BETWEEN 15 AND 21)) AS other_vol
    FROM session_bars
) AS totals
ORDER BY wat_time
⌘/Ctrl + Enter

Use dis data for your AI assistant

E go open ready to query, with dis page data. Free, no account.

Claude ChatGPT Gemini Muse Your own agent