The eleven S&P sector ETFs on July 1, best to worst vs Tuesday's close

Name: The eleven S&P sector ETFs on July 1, best to worst vs Tuesday's close
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: July 1, 2026, The Day in Numbers.

as of 2026-07-26 table 11×7read in context →

The eleven S&P sector ETFs on July 1, best to worst vs Tuesday's close — 11 rows by 7 columns, computed 2026-07-26 from US exchange, SIP and OPRA data.
ticker	prior_close	day_close	pct_chg	pct_above_worst_sector	green_so_far	day_dollar_bn
XLC	107.16	109.74	2.41	4.97	1	1.46
XLF	53.61	54.79	2.2	4.76	2	2.35
XLY	117.27	118.07	0.68	3.24	3	1.02
XLV	158.67	159.57	0.57	3.13	4	1.48
XLRE	44.02	44.18	0.36	2.93	5	0.33
XLB	50.84	51	0.31	2.88	6	0.63
XLP	83.08	83.33	0.3	2.86	7	0.85
XLE	53.14	52.82	-0.6	1.96	7	1.66
XLI	185.22	183.41	-0.98	1.59	7	1.35
XLU	45.34	44.76	-1.28	1.28	7	1.17
XLK	190.42	185.54	-2.56	0	7	1.7

Rows × columns: 11 × 7
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 S&P sector ETFs on July 1, best to worst vs Tuesday's close, derived from the stored result.
Column	Type	Range	Notes
`ticker`	text	11 distinct values (XLB, XLC, XLE…)
`prior_close`	number	44.02 to 190.42	US dollars
`day_close`	number	44.18 to 185.54	US dollars
`pct_chg`	number	-2.56 to 2.41	percent
`pct_above_worst_sector`	number	0 to 4.97	percent
`green_so_far`	number	1 to 7
`day_dollar_bn`	number	0.33 to 2.35

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.

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 per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-01 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-01 00:00:00')) AS day_close,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-01 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
      AND ((window_start >= '2026-06-30 13:30:00' AND window_start < '2026-06-30 20:00:00')
        OR (window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(prior_close, 2) AS prior_close,
    round(day_close, 2) AS day_close,
    round((day_close / prior_close - 1) * 100, 2) AS pct_chg,
    round((day_close / prior_close - 1) * 100 - min((day_close / prior_close - 1) * 100) OVER (), 2) AS pct_above_worst_sector,
    sum(if(day_close > prior_close, 1, 0)) OVER (ORDER BY (day_close / prior_close) DESC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS green_so_far,
    day_dollar_bn
FROM per_name
ORDER BY pct_chg DESC

SQL to run

WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-01 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-01 00:00:00')) AS day_close,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-01 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
      AND ((window_start >= '2026-06-30 13:30:00' AND window_start < '2026-06-30 20:00:00')
        OR (window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(prior_close, 2) AS prior_close,
    round(day_close, 2) AS day_close,
    round((day_close / prior_close - 1) * 100, 2) AS pct_chg,
    round((day_close / prior_close - 1) * 100 - min((day_close / prior_close - 1) * 100) OVER (), 2) AS pct_above_worst_sector,
    sum(if(day_close > prior_close, 1, 0)) OVER (ORDER BY (day_close / prior_close) DESC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS green_so_far,
    day_dollar_bn
FROM per_name
ORDER BY pct_chg DESC

⌘/Ctrl + Enter

More from this analysisMarket Recap: July 1, 2026, The Day in Numbers

The day's last twelve news articles tagging MU, SNDK, STX, WDC or META (one licensed feed) table 12×4 → Volume leaders two ways: top 6 by dollars traded, top 4 by shares traded (one reused-symbol listing excluded pending entity verification) table 10×6 → The biggest names going ex-dividend on July 1, ranked by the day's dollar volume table 8×7 → The memory complex and the megacaps: change vs Tuesday's close, range timing, and dollar volume table 8×9 → The volatility complex on July 1: VIX-futures ETFs vs Tuesday's close, with SPY as the anchor table 5×5 → SPY / QQQ / DIA / IWM: July 1 vs the June 30 close, regular hours table 4×8 → See all 2,433 queries →