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The blind window: what five stocks did between the settlement date and the day its short interest was published

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 2026-08-22, from Why Short Interest Data Is Always Two Weeks Old.

as of table 5×6read in context →
The blind window: what five stocks did between the settlement date and the day its short interest was published — 5 rows by 6 columns, computed from US exchange, SIP and OPRA data.
tickershares_short_mclose_at_settlementclose_when_publishedmove_while_pending_pctsessions_in_window
GME53.721.7218.83-13.38
NVDA292.7200.81217.488.38
TSLA68.5311.11332.878
MU29.9822.59868.25.58
AAPL141.6309.03304.9-1.38
Rows × columns
5 × 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 The blind window: what five stocks did between the settlement date and the day its short interest was published, derived from the stored result.
ColumnTypeRangeNotes
ticker text 5 distinct values (AAPL, GME, MU…)
shares_short_m number 29.9 to 292.7 count
close_at_settlement number 21.72 to 822.59 US dollars
close_when_published number 18.83 to 868.2 US dollars
move_while_pending_pct number -13.3 to 8.3 percent
sessions_in_window number every row is 8

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 latest AS (SELECT max(settlement_date) AS d FROM global_markets.stocks_short_interest),
arrived AS (
    SELECT toDate(min(_ingest_time)) AS a
    FROM global_markets.stocks_short_interest
    WHERE settlement_date = (SELECT d FROM latest)
),
daily AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS session,
           argMax(toFloat64(close), window_start) AS rth_close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL', 'TSLA', 'NVDA', 'GME', 'MU')
      AND toDate(toTimeZone(window_start, 'America/New_York')) >= (SELECT d FROM latest)
      AND toDate(toTimeZone(window_start, 'America/New_York')) <= (SELECT a FROM arrived)
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60
          + toMinute(toTimeZone(window_start, 'America/New_York')) BETWEEN 570 AND 959
    GROUP BY ticker, session
)
SELECT si.ticker AS ticker,
       round(si.short_interest / 1e6, 1) AS shares_short_m,
       round(argMin(d.rth_close, d.session), 2) AS close_at_settlement,
       round(argMax(d.rth_close, d.session), 2) AS close_when_published,
       round((argMax(d.rth_close, d.session) / argMin(d.rth_close, d.session) - 1) * 100, 1) AS move_while_pending_pct,
       count() AS sessions_in_window
FROM global_markets.stocks_short_interest AS si
INNER JOIN daily AS d ON d.ticker = si.ticker
WHERE si.settlement_date = (SELECT d FROM latest)
GROUP BY si.ticker, si.short_interest
ORDER BY abs(move_while_pending_pct) DESC, si.ticker ASC

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More from this analysisWhy Short Interest Data Is Always Two Weeks Old
GME, winter 2020-21: each short interest print and the price move before it went public (as-traded prices) table 6×5 → Every incrementally-delivered settlement: measured on one date, on file days later series 10×3 → The pipeline lag in one row: plus the bulk backfill these figures deliberately exclude scalar 1×6 → GME days to cover: as reported in the file, and recomputed on the volume that traded while the print was pending scalar 1×8 → The current state of the cycle: the newest print on file, and the one still in the pipeline scalar 1×6 → The short interest release schedule: recent FINRA settlement dates and names reported series 16×2 → See all 2,170 queries →