Five-name baskets per sector: members up vs down, median, best and worst, H1 2026

Name: Five-name baskets per sector: members up vs down, median, best and worst, H1 2026
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 Sector Scorecard H1 2026: Winners and Losers.

as of 2026-07-26 table 11×9read in context →

Five-name baskets per sector: members up vs down, median, best and worst, H1 2026 — 11 rows by 9 columns, computed 2026-07-26 from US exchange, SIP and OPRA data.
sector	members	members_up	members_down	median_member_pct	best_member	best_member_pct	worst_member	worst_member_pct
health care (XLV)	5	5	0	21.7	UNH	25.5	ABBV	9.9
real estate (XLRE)	5	4	1	21.3	EQIX	36.1	AMT	-6.7
materials (XLB)	5	5	0	19.5	LIN	21.7	SHW	6.4
industrials (XLI)	5	5	0	17.5	CAT	84.3	RTX	3.4
energy (XLE)	5	5	0	13.6	EOG	23.5	CVX	9
utilities (XLU)	5	4	1	9.1	AEP	18.5	CEG	-30.6
technology (XLK)	5	3	2	5.2	AVGO	6.9	ORCL	-25.7
financials (XLF)	5	4	1	3.5	MS	17.1	WFC	-11.4
consumer staples (XLP)	5	4	1	2.4	KO	16.3	PEP	-5.5
consumer discretionary (XLY)	5	2	3	-8.2	AMZN	3	NKE	-35.9
communication services (XLC)	5	1	4	-15.2	GOOGL	12.7	NFLX	-24.2

Rows × columns: 11 × 9
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 Five-name baskets per sector: members up vs down, median, best and worst, H1 2026, derived from the stored result.
Column	Type	Range	Notes
`sector`	text	11 distinct values
`members`	number	every row is 5
`members_up`	number	1 to 5
`members_down`	number	0 to 4
`median_member_pct`	number	-15.2 to 21.7	percent
`best_member`	text	11 distinct values (AEP, AMZN, AVGO…)
`best_member_pct`	number	3 to 84.3	percent
`worst_member`	text	11 distinct values (ABBV, AMT, CEG…)
`worst_member_pct`	number	-35.9 to 9.9	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.

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.

SELECT sector,
       count() AS members,
       countIf(ret_pct > 0) AS members_up,
       countIf(ret_pct <= 0) AS members_down,
       round(quantileDeterministic(0.5)(ret_pct, cityHash64(ticker)), 1) AS median_member_pct,
       argMax(ticker, (ret_pct, ticker)) AS best_member,
       round(max(ret_pct), 1) AS best_member_pct,
       argMin(ticker, (ret_pct, ticker)) AS worst_member,
       round(min(ret_pct), 1) AS worst_member_pct
FROM (
    SELECT ticker,
           multiIf(ticker IN ('AAPL','MSFT','NVDA','AVGO','ORCL'), 'technology (XLK)',
                   ticker IN ('META','GOOGL','NFLX','DIS','TMUS'), 'communication services (XLC)',
                   ticker IN ('XOM','CVX','COP','EOG','SLB'), 'energy (XLE)',
                   ticker IN ('JPM','BAC','WFC','GS','MS'), 'financials (XLF)',
                   ticker IN ('GE','CAT','RTX','UNP','UPS'), 'industrials (XLI)',
                   ticker IN ('LLY','UNH','JNJ','ABBV','MRK'), 'health care (XLV)',
                   ticker IN ('AMZN','TSLA','HD','MCD','NKE'), 'consumer discretionary (XLY)',
                   ticker IN ('PG','COST','WMT','KO','PEP'), 'consumer staples (XLP)',
                   ticker IN ('NEE','SO','DUK','CEG','AEP'), 'utilities (XLU)',
                   ticker IN ('PLD','AMT','EQIX','WELL','SPG'), 'real estate (XLRE)',
                   'materials (XLB)') AS sector,
           (argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959)
            / argMinIf(toFloat64(open), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) - 1) * 100 AS ret_pct
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL','MSFT','NVDA','AVGO','ORCL','META','GOOGL','NFLX','DIS','TMUS','XOM','CVX','COP','EOG','SLB','JPM','BAC','WFC','GS','MS','GE','CAT','RTX','UNP','UPS','LLY','UNH','JNJ','ABBV','MRK','AMZN','TSLA','HD','MCD','NKE','PG','COST','WMT','KO','PEP','NEE','SO','DUK','CEG','AEP','PLD','AMT','EQIX','WELL','SPG','LIN','SHW','APD','FCX','ECL')
      AND window_start >= toDateTime('2026-01-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    GROUP BY ticker
)
GROUP BY sector
ORDER BY median_member_pct DESC, sector

SQL to run

SELECT sector,
       count() AS members,
       countIf(ret_pct > 0) AS members_up,
       countIf(ret_pct <= 0) AS members_down,
       round(quantileDeterministic(0.5)(ret_pct, cityHash64(ticker)), 1) AS median_member_pct,
       argMax(ticker, (ret_pct, ticker)) AS best_member,
       round(max(ret_pct), 1) AS best_member_pct,
       argMin(ticker, (ret_pct, ticker)) AS worst_member,
       round(min(ret_pct), 1) AS worst_member_pct
FROM (
    SELECT ticker,
           multiIf(ticker IN ('AAPL','MSFT','NVDA','AVGO','ORCL'), 'technology (XLK)',
                   ticker IN ('META','GOOGL','NFLX','DIS','TMUS'), 'communication services (XLC)',
                   ticker IN ('XOM','CVX','COP','EOG','SLB'), 'energy (XLE)',
                   ticker IN ('JPM','BAC','WFC','GS','MS'), 'financials (XLF)',
                   ticker IN ('GE','CAT','RTX','UNP','UPS'), 'industrials (XLI)',
                   ticker IN ('LLY','UNH','JNJ','ABBV','MRK'), 'health care (XLV)',
                   ticker IN ('AMZN','TSLA','HD','MCD','NKE'), 'consumer discretionary (XLY)',
                   ticker IN ('PG','COST','WMT','KO','PEP'), 'consumer staples (XLP)',
                   ticker IN ('NEE','SO','DUK','CEG','AEP'), 'utilities (XLU)',
                   ticker IN ('PLD','AMT','EQIX','WELL','SPG'), 'real estate (XLRE)',
                   'materials (XLB)') AS sector,
           (argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959)
            / argMinIf(toFloat64(open), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) - 1) * 100 AS ret_pct
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL','MSFT','NVDA','AVGO','ORCL','META','GOOGL','NFLX','DIS','TMUS','XOM','CVX','COP','EOG','SLB','JPM','BAC','WFC','GS','MS','GE','CAT','RTX','UNP','UPS','LLY','UNH','JNJ','ABBV','MRK','AMZN','TSLA','HD','MCD','NKE','PG','COST','WMT','KO','PEP','NEE','SO','DUK','CEG','AEP','PLD','AMT','EQIX','WELL','SPG','LIN','SHW','APD','FCX','ECL')
      AND window_start >= toDateTime('2026-01-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    GROUP BY ticker
)
GROUP BY sector
ORDER BY median_member_pct DESC, sector

⌘/Ctrl + Enter

Work with this data in your AI assistant

Opens ready to query, with this page's data. Free, no account.

Claude ChatGPT Gemini Muse Your own agent

More from this analysisSector Scorecard H1 2026: Winners and Losers

The eleven sector ETFs: H1 2026 return, Q1 and Q2 split, and H1 dollar volume table 11×5 → Eight heavily traded chip and memory names: H1 return, quarter split, H1 dollar volume table 8×5 → XLE, XLF, and XLK by month: the H1 2026 rotation at month grain series 18×3 → Median quoted spread by sector ETF, one representative session (June 29, 2026), regular hours ranking 11×4 → Sector ETF dollar volume, Q1 vs Q2 2026: every fund traded less in the recovery quarter ranking 11×4 → Growth of $100 in the 1x SOXX vs the 3x SOXL, Jan 2 to Jul 13 2026 series 131×3 → See all 2,985 queries →