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Eight declared sector baskets, three names each: July 9 close-over-close, equal-weighted

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 9, 2026, The Day in Numbers.

as of table 8×6read in context →
Eight declared sector baskets, three names each: July 9 close-over-close, equal-weighted — 8 rows by 6 columns, computed from US exchange, SIP and OPRA data.
sectornamesavg_pct_chgworst_name_pctbest_name_pctgap_to_best_sector_pct
Semiconductors34.243.25.680
Financials31.881.462.61-2.36
Industrials30.42-11.4-3.82
Healthcare30.22-1.621.41-4.02
Big tech30.17-0.760.93-4.07
Staples3-0.91-1.04-0.75-5.15
Utilities3-0.96-1.28-0.41-5.21
Energy3-2.01-2.54-1.06-6.25
Rows × columns
8 × 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 Eight declared sector baskets, three names each: July 9 close-over-close, equal-weighted, derived from the stored result.
ColumnTypeRangeNotes
sector text 8 distinct values (Big tech, Energy, Financials…)
names number every row is 3
avg_pct_chg number -2.01 to 4.24 percent
worst_name_pct number -2.54 to 3.2 percent
best_name_pct number -1.06 to 5.68 percent
gap_to_best_sector_pct number -6.25 to 0 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
    sector,
    count() AS names,
    round(avg(pct_chg), 2) AS avg_pct_chg,
    round(min(pct_chg), 2) AS worst_name_pct,
    round(max(pct_chg), 2) AS best_name_pct,
    round(avg(pct_chg) - max(avg(pct_chg)) OVER (), 2) AS gap_to_best_sector_pct
FROM (
    SELECT
        ticker,
        multiIf(ticker IN ('AMD', 'AVGO', 'KLAC'), 'Semiconductors',
                ticker IN ('AAPL', 'MSFT', 'GOOGL'), 'Big tech',
                ticker IN ('JPM', 'BAC', 'GS'), 'Financials',
                ticker IN ('CAT', 'HON', 'GE'), 'Industrials',
                ticker IN ('XOM', 'CVX', 'COP'), 'Energy',
                ticker IN ('JNJ', 'UNH', 'PFE'), 'Healthcare',
                ticker IN ('KO', 'PG', 'WMT'), 'Staples',
                'Utilities') AS sector,
        (toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-09 13:30:00' AND window_start < '2026-07-09 20:00:00'))
         / toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-08 13:30:00' AND window_start < '2026-07-08 20:00:00')) - 1) * 100 AS pct_chg
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AMD', 'AVGO', 'KLAC', 'AAPL', 'MSFT', 'GOOGL', 'JPM', 'BAC', 'GS', 'CAT', 'HON', 'GE',
                     'XOM', 'CVX', 'COP', 'JNJ', 'UNH', 'PFE', 'KO', 'PG', 'WMT', 'NEE', 'DUK', 'SO')
      AND window_start >= '2026-07-08 13:30:00' AND window_start < '2026-07-09 20:00:00'
    GROUP BY ticker
)
GROUP BY sector
ORDER BY avg_pct_chg DESC

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