SPY on witching sessions vs. the same month's other sessions: intraday range and net move (% of the open)
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 2026-09-28, from Triple Witching 2026 Dates and Volume Data.
| witching_session | witching_range_pct | other_days_median_range_pct | witching_net_move_pct |
|---|---|---|---|
| 2024-09-20 | 0.73 | 0.92 | 0.05 |
| 2024-12-20 | 2.55 | 0.61 | 1.56 |
| 2025-03-21 | 1.23 | 1.72 | 0.88 |
| 2025-06-20 | 1.1 | 0.73 | 0.69 |
| 2025-09-19 | 0.63 | 0.71 | 0.2 |
| 2025-12-19 | 0.68 | 0.7 | 0.59 |
| 2026-03-20 | 1.82 | 1.28 | 1.21 |
| 2026-06-18 | 0.58 | 1.12 | 0.16 |
- Rows × columns
- 8 × 4
- Period covered
- to
- 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 |
|---|---|---|---|
witching_session |
date | 2024-09-20 to 2026-06-18 | |
witching_range_pct |
number | 0.58 to 2.55 | percent |
other_days_median_range_pct |
number | 0.61 to 1.72 | percent |
witching_net_move_pct |
number | 0.05 to 1.56 | 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 daily AS (
SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS day,
toFloat64(max(high) - min(low)) / toFloat64(argMin(open, window_start)) * 100 AS range_pct,
abs(toFloat64(argMax(close, window_start)) - toFloat64(argMin(open, window_start)))
/ toFloat64(argMin(open, window_start)) * 100 AS net_move_pct
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND window_start >= '2024-09-01 04:00:00'
AND window_start < '2026-07-01 04:00:00'
AND toMonth(toTimeZone(window_start, 'America/New_York')) IN (3, 6, 9, 12)
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY day
),
witching AS (
SELECT toStartOfMonth(day) AS m,
maxIf(day, day <= addDays(toStartOfMonth(day),
((5 - toDayOfWeek(toStartOfMonth(day)) + 7) % 7) + 14)) AS witching_day
FROM daily
GROUP BY m
)
SELECT toString(w.witching_day) AS witching_session,
round(anyIf(d.range_pct, d.day = w.witching_day), 2) AS witching_range_pct,
round(quantileDeterministicIf(0.5)(d.range_pct, cityHash64(toString(d.day)), d.day != w.witching_day), 2) AS other_days_median_range_pct,
round(anyIf(d.net_move_pct, d.day = w.witching_day), 2) AS witching_net_move_pct
FROM daily AS d
INNER JOIN witching AS w ON toStartOfMonth(d.day) = w.m
GROUP BY w.witching_day
HAVING countIf(d.day != w.witching_day) > 0
ORDER BY w.witching_day
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