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Eleven tickers on February 24, 2022: gap, intraday extremes, close (% vs prior close)

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 2026-07-26, from Feb 24, 2022: War at the Open, Green by Close.

as of table 11×6read in context →
Eleven tickers on February 24, 2022: gap, intraday extremes, close (% vs prior close) — 11 rows by 6 columns, computed from US exchange, SIP and OPRA data.
tickerprior_closegap_pctlow_vs_prior_pcthigh_vs_prior_pctday_change_pct
QQQ329.41-3.2-3.43.53.4
SPY421.97-2.6-2.71.61.5
DIA331.44-2.6-2.60.40.3
XLE67.642.1-3.42.2-0.9
GLD178.282.4-1.72.4-0.6
TLT136.621.8-0.320.2
VIXY20.1711.1-3.811.2-3
LMT389.142.3-0.42.51.7
NOC386.12.514.42.4
RTX92.270.8-3.22.62.1
GD216.250.2-2.21.21.1
Rows × columns
11 × 6
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 Eleven tickers on February 24, 2022: gap, intraday extremes, close (% vs prior close), derived from the stored result.
ColumnTypeRangeNotes
ticker text 11 distinct values (DIA, GD, GLD…)
prior_close number 20.17 to 421.97 US dollars
gap_pct number -3.2 to 11.1 percent
low_vs_prior_pct number -3.8 to 1 percent
high_vs_prior_pct number 0.4 to 11.2 percent
day_change_pct number -3 to 3.4 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.

the exact SQL behind every number 
SELECT
    ticker,
    round(prior_close, 2) AS prior_close,
    round((rth_open / prior_close - 1) * 100, 1) AS gap_pct,
    round((rth_low / prior_close - 1) * 100, 1) AS low_vs_prior_pct,
    round((rth_high / prior_close - 1) * 100, 1) AS high_vs_prior_pct,
    round((rth_close / prior_close - 1) * 100, 1) AS day_change_pct
FROM (
    SELECT
        ticker,
        argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959 AND toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2022-02-23')) AS prior_close,
        argMinIf(toFloat64(open), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959 AND toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2022-02-24')) AS rth_open,
        minIf(toFloat64(low), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959 AND toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2022-02-24')) AS rth_low,
        maxIf(toFloat64(high), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959 AND toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2022-02-24')) AS rth_high,
        argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959 AND toDate(toTimeZone(window_start, 'America/New_York')) = toDate('2022-02-24')) AS rth_close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('QQQ', 'SPY', 'DIA', 'XLE', 'GLD', 'TLT', 'VIXY', 'LMT', 'NOC', 'RTX', 'GD')
      AND window_start >= toDateTime('2022-02-23 00:00:00') AND window_start < toDateTime('2022-02-24 23:59:00')
    GROUP BY ticker
)
ORDER BY indexOf(['QQQ', 'SPY', 'DIA', 'XLE', 'GLD', 'TLT', 'VIXY', 'LMT', 'NOC', 'RTX', 'GD'], ticker)

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More from this analysisFeb 24, 2022: War at the Open, Green by Close
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