STRASMORE/EXPLORE 2,170 QUERIES

How far Shopify's US price wanders after the 4 p.m. close, May to July 2026

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 2026-08-19, from TSX Trading Hours and Holidays vs NYSE.

as of series 16×3read in context →
How far Shopify's US price wanders after the 4 p.m. close, May to July 2026 — 16 rows by 3 columns, computed from US exchange, SIP and OPRA data.
et_timeavg_abs_move_pctsession_count
16:000.22463
16:150.25659
16:300.3357
16:450.29953
17:000.3747
17:150.38646
17:300.34948
17:450.41848
18:000.41546
18:150.42452
18:300.52343
18:450.43849
19:000.40849
19:150.42546
19:300.42250
19:450.40453
Rows × columns
16 × 3
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 How far Shopify's US price wanders after the 4 p.m. close, May to July 2026, derived from the stored result.
ColumnTypeRangeNotes
et_time date 16:00 to 19:45
avg_abs_move_pct number 0.224 to 0.523 percent
session_count number 43 to 63 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.

the exact SQL behind every number 
SELECT
    formatDateTime(b.bucket, '%H:%i')                                     AS et_time,
    round(avg(abs(toFloat64(b.px) / toFloat64(c.close_px) - 1)) * 100, 3) AS avg_abs_move_pct,
    count()                                                               AS session_count
FROM
(
    SELECT
        toDate(toTimeZone(window_start, 'America/New_York'))                               AS d,
        toStartOfInterval(toTimeZone(window_start, 'America/New_York'), INTERVAL 15 MINUTE) AS bucket,
        argMax(close, window_start)                                                        AS px
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SHOP'
      AND window_start >= '2026-05-01 04:00:00'
      AND window_start <  '2026-08-01 04:00:00'
      AND volume > 0
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) >= 960
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) <  1200
    GROUP BY d, bucket
) AS b
INNER JOIN
(
    SELECT
        toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        argMax(close, window_start)                          AS close_px
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SHOP'
      AND window_start >= '2026-05-01 04:00:00'
      AND window_start <  '2026-08-01 04:00:00'
      AND volume > 0
      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 d
) AS c ON c.d = b.d
GROUP BY et_time
HAVING count() >= 10
ORDER BY et_time

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