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.
| et_time | avg_abs_move_pct | session_count |
|---|---|---|
| 16:00 | 0.224 | 63 |
| 16:15 | 0.256 | 59 |
| 16:30 | 0.33 | 57 |
| 16:45 | 0.299 | 53 |
| 17:00 | 0.37 | 47 |
| 17:15 | 0.386 | 46 |
| 17:30 | 0.349 | 48 |
| 17:45 | 0.418 | 48 |
| 18:00 | 0.415 | 46 |
| 18:15 | 0.424 | 52 |
| 18:30 | 0.523 | 43 |
| 18:45 | 0.438 | 49 |
| 19:00 | 0.408 | 49 |
| 19:15 | 0.425 | 46 |
| 19:30 | 0.422 | 50 |
| 19:45 | 0.404 | 53 |
- Rows × columns
- 16 × 3
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
et_time |
text | 16 distinct values (16:00, 16:15, 16:30…) | |
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.
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
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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