STRASMORE/EXPLORE 2,985 QUERIES

GME days to cover: as reported in the file, and recomputed on the volume that traded while the print was pending

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 Why Short Interest Data Is Always Two Weeks Old.

as of scalar 1×8read in context →
settlement
2026-09-15
shares short m
39.1
file adv m
9.11
reported days to cover
4.29
tape adv since settlement m
8.82
days to cover on recent volume
4.43
days of difference
0.14
sessions measured
7
Rows × columns
1 × 8
Period covered
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 GME days to cover: as reported in the file, and recomputed on the volume that traded while the print was pending, derived from the stored result.
ColumnTypeRangeNotes
settlement date 2026-09-15
shares_short_m number every row is 39.1 count
file_adv_m number every row is 9.11
reported_days_to_cover number every row is 4.29
tape_adv_since_settlement_m number every row is 8.82
days_to_cover_on_recent_volume number every row is 4.43 count
days_of_difference number every row is 0.14
sessions_measured number every row is 7

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 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)
),
tape AS (
    SELECT count(DISTINCT toDate(toTimeZone(window_start, 'America/New_York'))) AS sessions,
           sum(toFloat64(volume)) AS shares
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'GME'
      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
)
SELECT toString((SELECT d FROM latest)) AS settlement,
       round(si.short_interest / 1e6, 1) AS shares_short_m,
       round(si.avg_daily_volume / 1e6, 2) AS file_adv_m,
       round(si.days_to_cover, 2) AS reported_days_to_cover,
       round(tape.shares / tape.sessions / 1e6, 2) AS tape_adv_since_settlement_m,
       round(si.short_interest / (tape.shares / tape.sessions), 2) AS days_to_cover_on_recent_volume,
       round(abs(si.short_interest / (tape.shares / tape.sessions) - si.days_to_cover), 2) AS days_of_difference,
       tape.sessions AS sessions_measured
FROM global_markets.stocks_short_interest AS si, tape
WHERE si.ticker = 'GME'
  AND si.settlement_date = (SELECT d FROM latest)
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More from this analysisWhy Short Interest Data Is Always Two Weeks Old
The pipeline lag in one row: plus the bulk backfill these figures deliberately exclude scalar 1×6 → The current state of the cycle: the newest print on file, and the one still in the pipeline scalar 1×6 → Every incrementally-delivered settlement: measured on one date, on file days later series 13×3 → GME, winter 2020-21: each short interest print and the price move before it went public (as-traded prices) table 6×5 → The blind window: what five stocks did between the settlement date and the day its short interest was published table 5×6 → The publication lag in one row: fastest, median and slowest across incrementally-delivered settlements scalar 1×4 → See all 2,985 queries →