STRASMORE/EXPLORE 2,948 QUERIES

Strongest and weakest sleeve versus SPY, by calendar month

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 2026-09-26, from ETF Relative Strength and Alpha Attribution.

as of series 13×4read in context →
Strongest and weakest sleeve versus SPY, by calendar month — 13 rows by 4 columns, computed from US exchange, SIP and OPRA data.
monthleading_etfleader_excess_pctlaggard_excess_pct
2025-08IWM5.67-5.48
2025-09GLD5.13-5.35
2025-10XLK3.61-3.95
2025-11XLV9.13-5.2
2025-12XLF3.31-3.78
2026-01XLE10.54-4
2026-02GLD14.61-3.46
2026-03XLE12.65-6.93
2026-04XLK8.55-12.91
2026-05XLK13.04-9.55
2026-06XLV8.87-8.87
2026-07XLE12.59-5.7
2026-08GLD8.64-6.04
Rows × columns
13 × 4
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 Strongest and weakest sleeve versus SPY, by calendar month, derived from the stored result.
ColumnTypeRangeNotes
month text 13 distinct values (2025-08, 2025-09, 2025-10…)
leading_etf text 6 distinct values (GLD, IWM, XLE…)
leader_excess_pct number 3.31 to 14.61 percent
laggard_excess_pct number -12.91 to -3.46 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.

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 monthly AS
(
    SELECT
        toStartOfMonth(date)           AS m,
        ticker,
        argMin(toFloat64(close), date) AS first_close,
        argMax(toFloat64(close), date) AS last_close
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'IWM', 'XLK', 'XLE', 'XLF', 'XLV', 'XLU', 'GLD', 'EFA')
      AND date >= toStartOfMonth(today() - 400)
      AND date <  toStartOfMonth(today())
    GROUP BY m, ticker
)
SELECT
    formatDateTime(x.m, '%Y-%m')   AS month,
    argMax(x.ticker, x.excess_pct) AS leading_etf,
    round(max(x.excess_pct), 2)    AS leader_excess_pct,
    round(min(x.excess_pct), 2)    AS laggard_excess_pct
FROM
(
    SELECT
        sleeve.m      AS m,
        sleeve.ticker AS ticker,
        (sleeve.last_close / sleeve.first_close - 1) * 100
          - (bench.spy_last / bench.spy_first - 1) * 100 AS excess_pct
    FROM monthly AS sleeve
    INNER JOIN
    (
        SELECT
            m,
            first_close AS spy_first,
            last_close  AS spy_last
        FROM monthly
        WHERE ticker = 'SPY'
    ) AS bench USING (m)
    WHERE sleeve.ticker != 'SPY'
) AS x
GROUP BY x.m
ORDER BY x.m
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