Average distance from one session's close to the next session's open, monthly
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 2026-08-02, from Learn Quant Trading From an Open Source Book.
| month | month_label | spy_gap_bps | nvda_gap_bps |
|---|---|---|---|
| 2024-08 | August 2024 | 61.9 | 232.7 |
| 2024-09 | September 2024 | 33.6 | 131.1 |
| 2024-10 | October 2024 | 30 | 96.7 |
| 2024-11 | November 2024 | 32.9 | 102.6 |
| 2024-12 | December 2024 | 31.7 | 103 |
| 2025-01 | January 2025 | 54.9 | 238.7 |
| 2025-02 | February 2025 | 31.8 | 132.2 |
| 2025-03 | March 2025 | 56.9 | 174.9 |
| 2025-04 | April 2025 | 130.3 | 288.4 |
| 2025-05 | May 2025 | 69.8 | 174.2 |
| 2025-06 | June 2025 | 32.2 | 71.3 |
| 2025-07 | July 2025 | 22.8 | 78.4 |
| 2025-08 | August 2025 | 25.5 | 59 |
| 2025-09 | September 2025 | 26.5 | 102.1 |
| 2025-10 | October 2025 | 38.6 | 114.2 |
| 2025-11 | November 2025 | 47.6 | 189.1 |
| 2025-12 | December 2025 | 23.8 | 81.6 |
| 2026-01 | January 2026 | 33.8 | 89.4 |
| 2026-02 | February 2026 | 31.4 | 103.9 |
| 2026-03 | March 2026 | 72.4 | 100.6 |
| 2026-04 | April 2026 | 45 | 86.1 |
| 2026-05 | May 2026 | 31.6 | 94.8 |
| 2026-06 | June 2026 | 51.5 | 116 |
| 2026-07 | July 2026 | 40.8 | 107.4 |
- Rows × columns
- 24 × 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 |
|---|---|---|---|
month |
date | 2024-08 to 2026-07 | |
month_label |
text | 24 distinct values (April 2025, April 2026, August 2024…) | |
spy_gap_bps |
number | 22.8 to 130.3 | |
nvda_gap_bps |
number | 59 to 288.4 |
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
WITH sessions AS (
SELECT ticker,
toDate(toTimeZone(window_start, 'America/New_York')) AS session,
argMin(open, window_start) AS session_open,
argMax(close, window_start) AS session_close
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('SPY', 'NVDA')
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2024-08-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-07-31')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY ticker, session
),
gaps AS (
SELECT ticker,
session,
toFloat64(session_open) AS open_px,
toFloat64(lagInFrame(session_close) OVER (PARTITION BY ticker ORDER BY session)) AS prev_close
FROM sessions
)
SELECT formatDateTime(toStartOfMonth(session), '%Y-%m') AS month,
formatDateTimeInJodaSyntax(toStartOfMonth(session), 'MMMM yyyy') AS month_label,
round(avgIf(abs(open_px / prev_close - 1) * 10000, ticker = 'SPY'), 1) AS spy_gap_bps,
round(avgIf(abs(open_px / prev_close - 1) * 10000, ticker = 'NVDA'), 1) AS nvda_gap_bps
FROM gaps
WHERE prev_close > 0
GROUP BY month, month_label
HAVING countIf(ticker = 'SPY') > 0 AND countIf(ticker = 'NVDA') > 0
ORDER BY month
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