close_gap_by_time
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 2026-09-24, from mutual-fund-forward-pricing-rule.
| et_time | avg_move_to_close_pct | observations |
|---|---|---|
| 09:30 | 0.401 | 104 |
| 10:00 | 0.366 | 104 |
| 10:30 | 0.334 | 104 |
| 11:00 | 0.31 | 104 |
| 11:30 | 0.31 | 104 |
| 12:00 | 0.277 | 104 |
| 12:30 | 0.249 | 104 |
| 13:00 | 0.237 | 104 |
| 13:30 | 0.198 | 103 |
| 14:00 | 0.179 | 103 |
| 14:30 | 0.15 | 103 |
| 15:00 | 0.126 | 103 |
| 15:30 | 0.104 | 103 |
- Rows × columns
- 13 × 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 | 13 distinct values (09:30, 10:00, 10:30…) | |
avg_move_to_close_pct |
number | 0.104 to 0.401 | percent |
observations |
number | 103 to 104 |
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 bars AS
(
SELECT
toDate(toTimeZone(window_start, 'America/New_York')) AS session_date,
toTimeZone(window_start, 'America/New_York') AS et,
toFloat64(close) AS px
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND window_start >= today() - 150
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
),
final_print AS
(
SELECT
session_date,
argMax(px, et) AS closing_px
FROM bars
GROUP BY session_date
)
SELECT
formatDateTime(b.et, '%H:%i') AS et_time,
round(avg(abs(f.closing_px / b.px - 1)) * 100, 3) AS avg_move_to_close_pct,
count() AS observations
FROM bars AS b
INNER JOIN final_print AS f ON f.session_date = b.session_date
WHERE toMinute(b.et) IN (0, 30)
GROUP BY et_time
ORDER BY et_time
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.