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A 10% trail ratcheting under AAPL's high-water close, January to mid-March 2022

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 2026-10-08, from Trailing Stop vs Trailing Stop-Limit Orders.

as of series 50×5read in context →
A 10% trail ratcheting under AAPL's high-water close, January to mid-March 2022 — 50 rows by 5 columns, computed from US exchange, SIP and OPRA data.
session_datecalendar_labelclose_pxhigh_water_closetrail_10pct_level
2022-01-03Jan 3, 2022182.01182.01163.81
2022-01-04Jan 4, 2022179.7182.01163.81
2022-01-05Jan 5, 2022174.92182.01163.81
2022-01-06Jan 6, 2022172182.01163.81
2022-01-07Jan 7, 2022172.17182.01163.81
2022-01-10Jan 10, 2022172.19182.01163.81
2022-01-11Jan 11, 2022175.08182.01163.81
2022-01-12Jan 12, 2022175.53182.01163.81
2022-01-13Jan 13, 2022172.19182.01163.81
2022-01-14Jan 14, 2022173.07182.01163.81
2022-01-18Jan 18, 2022169.8182.01163.81
2022-01-19Jan 19, 2022166.23182.01163.81
2022-01-20Jan 20, 2022164.51182.01163.81
2022-01-21Jan 21, 2022162.41182.01163.81
2022-01-24Jan 24, 2022161.62182.01163.81
2022-01-25Jan 25, 2022159.78182.01163.81
2022-01-26Jan 26, 2022159.69182.01163.81
2022-01-27Jan 27, 2022159.22182.01163.81
2022-01-28Jan 28, 2022170.33182.01163.81
2022-01-31Jan 31, 2022174.78182.01163.81
2022-02-01Feb 1, 2022174.61182.01163.81
2022-02-02Feb 2, 2022175.84182.01163.81
2022-02-03Feb 3, 2022172.9182.01163.81
2022-02-04Feb 4, 2022172.39182.01163.81
2022-02-07Feb 7, 2022171.66182.01163.81
2022-02-08Feb 8, 2022174.83182.01163.81
2022-02-09Feb 9, 2022176.28182.01163.81
2022-02-10Feb 10, 2022172.12182.01163.81
2022-02-11Feb 11, 2022168.64182.01163.81
2022-02-14Feb 14, 2022168.88182.01163.81
2022-02-15Feb 15, 2022172.79182.01163.81
2022-02-16Feb 16, 2022172.55182.01163.81
2022-02-17Feb 17, 2022168.88182.01163.81
2022-02-18Feb 18, 2022167.3182.01163.81
2022-02-22Feb 22, 2022164.32182.01163.81
2022-02-23Feb 23, 2022160.07182.01163.81
2022-02-24Feb 24, 2022162.74182.01163.81
2022-02-25Feb 25, 2022164.85182.01163.81
2022-02-28Feb 28, 2022165.12182.01163.81
2022-03-01Mar 1, 2022163.2182.01163.81
2022-03-02Mar 2, 2022166.56182.01163.81
2022-03-03Mar 3, 2022166.23182.01163.81
2022-03-04Mar 4, 2022163.17182.01163.81
2022-03-07Mar 7, 2022159.3182.01163.81
2022-03-08Mar 8, 2022157.44182.01163.81
2022-03-09Mar 9, 2022162.95182.01163.81
2022-03-10Mar 10, 2022158.52182.01163.81
2022-03-11Mar 11, 2022154.73182.01163.81
2022-03-14Mar 14, 2022150.62182.01163.81
2022-03-15Mar 15, 2022155.09182.01163.81
Rows × columns
50 × 5
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 A 10% trail ratcheting under AAPL's high-water close, January to mid-March 2022, derived from the stored result.
ColumnTypeRangeNotes
session_date date 2022-01-03 to 2022-03-15
calendar_label text 50 distinct values (Feb 1, 2022, Feb 10, 2022, Feb 11, 2022…)
close_px number 150.62 to 182.01 US dollars
high_water_close number every row is 182.01 US dollars
trail_10pct_level number every row is 163.81

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 daily AS
(
    SELECT
        date,
        toFloat64(any(close)) AS close_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'AAPL'
      AND date >= '2022-01-03'
      AND date <  '2022-03-16'
    GROUP BY date
),
marked AS
(
    SELECT
        date,
        close_px,
        max(close_px) OVER (ORDER BY date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS peak_close
    FROM daily
)
SELECT
    toString(date)                    AS session_date,
    formatDateTime(date, '%b %e, %Y') AS calendar_label,
    round(close_px, 2)                AS close_px,
    round(peak_close, 2)              AS high_water_close,
    round(peak_close * 0.9, 2)        AS trail_10pct_level
FROM marked
ORDER BY date ASC
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