The eleven sector baskets: June 29 vs the June 26 close, regular hours

Name: The eleven sector baskets: June 29 vs the June 26 close, regular hours
Creator: Strasmore
Published: 2026-07-26
License: https://www.strasmore.com/terms/

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 Market Recap: June 29, 2026, The Day in Numbers.

as of 2026-07-26 table 11×6read in context →

The eleven sector baskets: June 29 vs the June 26 close, regular hours — 11 rows by 6 columns, computed 2026-07-26 from US exchange, SIP and OPRA data.
sector	ticker	pct_change	range_pct	dollar_volume_m	pct_above_worst_sector
Technology	XLK	2.52	3.91	2075	4.34
Consumer discretionary	XLY	2.37	2.17	1142	4.2
Communication services	XLC	1.66	0.78	669	3.48
Industrials	XLI	0.89	1.23	1231	2.72
Financials	XLF	0.28	0.64	1593	2.1
Health care	XLV	0.26	0.76	1897	2.08
Utilities	XLU	-0.32	1.18	759	1.5
Consumer staples	XLP	-0.38	1.23	764	1.44
Energy	XLE	-0.52	1.51	1098	1.3
Real estate	XLRE	-0.64	1.45	217	1.18
Materials	XLB	-1.82	2.28	626	0

Rows × columns: 11 × 6
Computed: 2026-07-26
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 The eleven sector baskets: June 29 vs the June 26 close, regular hours, derived from the stored result.
Column	Type	Range	Notes
`sector`	text	11 distinct values
`ticker`	text	11 distinct values (XLB, XLC, XLE…)
`pct_change`	number	-1.82 to 2.52	percent
`range_pct`	number	0.64 to 3.91	percent
`dollar_volume_m`	number	217 to 2,075	count
`pct_above_worst_sector`	number	0 to 4.34	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 2026-07-26, and the query below is what returned them.

the exact SQL behind every number

WITH per_etf AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')) AS friday_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')) AS monday_close,
        maxIf(toFloat64(high), window_start >= '2026-06-29 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-06-29 00:00:00') AS day_low,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-06-29 00:00:00') / 1e6, 0) AS dollar_volume_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
      AND ((window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
        OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'))
    GROUP BY ticker
)
SELECT
    sector,
    ticker,
    round((monday_close / friday_close - 1) * 100, 2) AS pct_change,
    round((day_high / day_low - 1) * 100, 2) AS range_pct,
    dollar_volume_m,
    round((monday_close / friday_close - 1) * 100 - min((monday_close / friday_close - 1) * 100) OVER (), 2) AS pct_above_worst_sector
FROM (
    SELECT *,
        multiIf(ticker = 'XLB', 'Materials',
                ticker = 'XLC', 'Communication services',
                ticker = 'XLE', 'Energy',
                ticker = 'XLF', 'Financials',
                ticker = 'XLI', 'Industrials',
                ticker = 'XLK', 'Technology',
                ticker = 'XLP', 'Consumer staples',
                ticker = 'XLRE', 'Real estate',
                ticker = 'XLU', 'Utilities',
                ticker = 'XLV', 'Health care',
                'Consumer discretionary') AS sector
    FROM per_etf
)
ORDER BY pct_change DESC

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