How big a typical session is: SPY close-to-close moves by size band, calendar 2025
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 2026-07-31, from Multi-Agent AI Trading Systems: What Is Real.
| move_bucket | sessions | pct_of_sessions |
|---|---|---|
| under 0.25% | 62 | 24.9 |
| 0.25 to 0.5% | 60 | 24.1 |
| 0.5 to 1% | 70 | 28.1 |
| 1 to 2% | 44 | 17.7 |
| 2% and up | 13 | 5.2 |
- Rows × columns
- 5 × 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 |
|---|---|---|---|
move_bucket |
text | 5 distinct values (0.25 to 0.5%, 0.5 to 1%, 1 to 2%…) | |
sessions |
number | 13 to 70 | |
pct_of_sessions |
number | 5.2 to 28.1 | 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 d,
argMax(close, window_start) AS close_px
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2025-01-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2025-12-31')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY d
),
paired AS (
SELECT d, close_px,
any(close_px) OVER (ORDER BY d ASC ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS prev_px
FROM daily
),
rets AS (
SELECT d, round(100 * (toFloat64(close_px) / toFloat64(prev_px) - 1), 3) AS ret_pct
FROM paired
WHERE prev_px > 0
)
SELECT multiIf(abs(ret_pct) < 0.25, 'under 0.25%',
abs(ret_pct) < 0.5, '0.25 to 0.5%',
abs(ret_pct) < 1.0, '0.5 to 1%',
abs(ret_pct) < 2.0, '1 to 2%',
'2% and up') AS move_bucket,
count() AS sessions,
round(100 * count() / sum(count()) OVER (), 1) AS pct_of_sessions
FROM rets
GROUP BY move_bucket
ORDER BY min(abs(ret_pct))
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