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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.

as of series 24×4read in context →
Average distance from one session's close to the next session's open, monthly — 24 rows by 4 columns, computed from US exchange, SIP and OPRA data.
monthmonth_labelspy_gap_bpsnvda_gap_bps
2024-08August 202461.9232.7
2024-09September 202433.6131.1
2024-10October 20243096.7
2024-11November 202432.9102.6
2024-12December 202431.7103
2025-01January 202554.9238.7
2025-02February 202531.8132.2
2025-03March 202556.9174.9
2025-04April 2025130.3288.4
2025-05May 202569.8174.2
2025-06June 202532.271.3
2025-07July 202522.878.4
2025-08August 202525.559
2025-09September 202526.5102.1
2025-10October 202538.6114.2
2025-11November 202547.6189.1
2025-12December 202523.881.6
2026-01January 202633.889.4
2026-02February 202631.4103.9
2026-03March 202672.4100.6
2026-04April 20264586.1
2026-05May 202631.694.8
2026-06June 202651.5116
2026-07July 202640.8107.4
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
Formats
JSON · CSV · the SQL below

What each column holds

Column definitions for Average distance from one session's close to the next session's open, monthly, derived from the stored result.
ColumnTypeRangeNotes
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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