STRASMORE/EXPLORE 3,127 QUERIES

One month of a crowded short, January 2021

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 2026-10-06, from Is a Covered Put Actually Covered?.

as of series 24×4read in context →
One month of a crowded short, January 2021 — 24 rows by 4 columns, computed from US exchange, SIP and OPRA data.
datedate_labelchange_vs_start_pctwindow_peak_vs_start_pct
2021-01-04Jan 4, 202101914.6
2021-01-05Jan 5, 20210.71914.6
2021-01-06Jan 6, 20216.41914.6
2021-01-07Jan 7, 20214.81914.6
2021-01-08Jan 8, 20212.61914.6
2021-01-11Jan 11, 202115.61914.6
2021-01-12Jan 12, 202115.71914.6
2021-01-13Jan 13, 2021821914.6
2021-01-14Jan 14, 2021131.41914.6
2021-01-15Jan 15, 2021105.81914.6
2021-01-19Jan 19, 2021128.21914.6
2021-01-20Jan 20, 2021126.81914.6
2021-01-21Jan 21, 2021149.41914.6
2021-01-22Jan 22, 2021276.91914.6
2021-01-25Jan 25, 2021345.21914.6
2021-01-26Jan 26, 2021757.91914.6
2021-01-27Jan 27, 20211914.61914.6
2021-01-28Jan 28, 20211022.31914.6
2021-01-29Jan 29, 20211784.11914.6
2021-02-01Feb 1, 20211204.31914.6
2021-02-02Feb 2, 2021421.71914.6
2021-02-03Feb 3, 2021435.71914.6
2021-02-04Feb 4, 2021210.11914.6
2021-02-05Feb 5, 2021269.71914.6
Rows × columns
24 × 4
Period covered
to
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 One month of a crowded short, January 2021, derived from the stored result.
ColumnTypeRangeNotes
date date 2021-01-04 to 2021-02-05
date_label text 24 distinct values (Feb 1, 2021, Feb 2, 2021, Feb 3, 2021…)
change_vs_start_pct number 0 to 1,914.6 percent
window_peak_vs_start_pct number every row is 1,914.6 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 px AS
(
    SELECT
        date,
        toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'GME'
      AND date >= '2021-01-04'
      AND date <= '2021-02-05'
),
anchor AS
(
    SELECT
        argMin(c, date) AS first_close,
        max(c)          AS peak_close
    FROM px
)
SELECT
    toString(px.date)                                            AS date,
    formatDateTime(px.date, '%b %e, %Y')                         AS date_label,
    round((px.c / anchor.first_close - 1) * 100, 1)               AS change_vs_start_pct,
    round((anchor.peak_close / anchor.first_close - 1) * 100, 1)  AS window_peak_vs_start_pct
FROM px
CROSS JOIN anchor
ORDER BY px.date
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