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Sector ETFs, July 30 close vs July 29 close, ranked

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 2026-08-01, from Market Recap: July 30, 2026, The Day in Numbers.

as of ranking 11×3read in context →
Sector ETFs, July 30 close vs July 29 close, ranked — 11 rows by 3 columns, computed from US exchange, SIP and OPRA data.
sectorday_pctpoints_behind_best
Technology5.520
Industrials0.974.55
Consumer Discretionary0.784.74
Financials0.64.92
Energy0.564.96
Materials-0.175.69
Utilities-0.566.08
Real Estate-1.446.96
Health Care-1.677.19
Staples-2.127.64
Communications-2.698.21
Rows × columns
11 × 3
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 Sector ETFs, July 30 close vs July 29 close, ranked, derived from the stored result.
ColumnTypeRangeNotes
sector text 11 distinct values
day_pct number -2.69 to 5.52 percent
points_behind_best number 0 to 8.21

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 sector, day_pct, round(max(day_pct) OVER () - day_pct, 2) AS points_behind_best
FROM (
    SELECT multiIf(ticker = 'XLK', 'Technology', ticker = 'XLC', 'Communications', ticker = 'XLE', 'Energy',
                   ticker = 'XLF', 'Financials', ticker = 'XLI', 'Industrials', ticker = 'XLB', 'Materials',
                   ticker = 'XLP', 'Staples', ticker = 'XLRE', 'Real Estate', ticker = 'XLU', 'Utilities',
                   ticker = 'XLV', 'Health Care', 'Consumer Discretionary') AS sector,
           round((c30 / c29 - 1) * 100, 2) AS day_pct
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start < '2026-07-30 00:00:00') AS c29,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-30 00:00:00') AS c30
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
          AND ((window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00')
            OR (window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'))
        GROUP BY ticker
        HAVING c29 > 0 AND c30 > 0
    )
)
ORDER BY day_pct DESC

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More from this analysisMarket Recap: July 30, 2026, The Day in Numbers
Biggest gainers and decliners: July 30 close vs July 29 close, $5M+ traded, splits excluded ranking 16×4 → Top 6 by dollars traded, top 4 by shares traded: July 30 regular hours table 10×5 → Eight mega-caps: change vs July 29 and regular-hours dollars, July 30 table 8×5 → Seven names: RTH median quoted spread in basis points, with quote-quality counts, July 30 table 7×5 → Treasury curve prints on file, July 27 through July 30 series 4×5 → SPY / QQQ / DIA / IWM: July 30 vs the July 29 close, regular hours table 4×10 → See all 2,170 queries →