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How thin the denominator gets: sessions bucketed by names at a 52-week extreme

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 2026-08-04, from What Is the High-Low Index? Market Breadth.

as of table 3×5read in context →
How thin the denominator gets: sessions bucketed by names at a 52-week extreme — 3 rows by 5 columns, computed from US exchange, SIP and OPRA data.
sample_size_bucketreadingsavg_names_in_denominatormedian_record_high_pctmedian_distance_from_50
1 to 3 names39210050
4 to 9 names375.28030
10 or more names1107020
Rows × columns
3 × 5
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 How thin the denominator gets: sessions bucketed by names at a 52-week extreme, derived from the stored result.
ColumnTypeRangeNotes
sample_size_bucket text 3 distinct values
readings number 1 to 39
avg_names_in_denominator number 2 to 10
median_record_high_pct number 70 to 100 percent
median_distance_from_50 number 20 to 50

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 
WITH daily_close AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS session_date,
           argMax(close, window_start) AS close_px
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL','MSFT','NVDA','AMZN','GOOGL','AVGO','JPM','JNJ','XOM','PG',
                     'KO','PEP','WMT','HD','CVX','MRK','PFE','ABBV','CSCO','ORCL',
                     'CRM','ADBE','MCD','NKE','VZ','T','DIS','BA','CAT','GE',
                     'IBM','MMM','UNH','LLY','COST','TGT','SBUX','GS','MS','LIN')
      AND window_start >= toDateTime('2025-03-01 00:00:00')
      AND window_start < toDateTime('2026-08-01 05:00:00')
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, session_date
),
extremes AS (
    SELECT cur.ticker AS ticker,
           cur.session_date AS session_date,
           cur.close_px AS close_px,
           max(hist.close_px) AS high_52w,
           min(hist.close_px) AS low_52w
    FROM daily_close AS cur
    INNER JOIN daily_close AS hist ON cur.ticker = hist.ticker
    WHERE cur.session_date >= toDate('2026-04-01')
      AND hist.session_date <= cur.session_date
      AND hist.session_date > cur.session_date - 364
    GROUP BY cur.ticker, cur.session_date, cur.close_px
),
daily AS (
    SELECT session_date,
           countIf(close_px >= high_52w) AS new_highs,
           countIf(close_px >= high_52w) + countIf(close_px <= low_52w) AS names_at_extreme
    FROM extremes
    GROUP BY session_date
    HAVING names_at_extreme > 0
)
SELECT multiIf(names_at_extreme <= 3, '1 to 3 names',
               names_at_extreme <= 9, '4 to 9 names',
               '10 or more names') AS sample_size_bucket,
       count() AS readings,
       round(avg(names_at_extreme), 1) AS avg_names_in_denominator,
       round(quantileDeterministic(0.5)(100 * new_highs / names_at_extreme,
                                        cityHash64(session_date)), 1) AS median_record_high_pct,
       round(quantileDeterministic(0.5)(abs(100 * new_highs / names_at_extreme - 50),
                                        cityHash64(session_date)), 1) AS median_distance_from_50
FROM daily
GROUP BY sample_size_bucket
ORDER BY avg_names_in_denominator

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