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SPY 2025: the ten widest pokes above the prior day's high that closed back below it

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 2026-09-28, from What Is a Liquidity Sweep in Trading?.

as of series 10×7read in context →
SPY 2025: the ten widest pokes above the prior day's high that closed back below it — 10 rows by 7 columns, computed from US exchange, SIP and OPRA data.
session_datesession_labelprior_day_highsession_highsession_closepoke_above_pctclose_below_pct
2025-11-20November 20, 2025667.34675.56652.531.232.22
2025-10-15October 15, 2025665.83670.23665.170.660.1
2025-08-07August 7, 2025633.44636.98632.250.560.19
2025-01-31January 31, 2025606.6609.96601.820.550.79
2025-09-05September 5, 2025649.15652.21647.240.470.29
2025-04-28April 28, 2025551.05553.55550.850.450.04
2025-03-03March 3, 2025594.72597.34583.770.441.84
2025-07-15July 15, 2025625.16627.86622.140.430.48
2025-04-08April 8, 2025523.17524.98496.480.355.1
2025-07-31July 31, 2025637.68639.85632.080.340.88
Rows × columns
10 × 7
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 SPY 2025: the ten widest pokes above the prior day's high that closed back below it, derived from the stored result.
ColumnTypeRangeNotes
session_date date 2025-01-31 to 2025-11-20
session_label text 10 distinct values
prior_day_high number 523.17 to 667.34 US dollars
session_high number 524.98 to 675.56 US dollars
session_close number 496.48 to 665.17 US dollars
poke_above_pct number 0.34 to 1.23 percent
close_below_pct number 0.04 to 5.1 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.

SELECT
    toString(toDate(date))                                                                     AS session_date,
    concat(monthName(date), ' ', toString(toDayOfMonth(date)), ', ', toString(toYear(date)))   AS session_label,
    round(toFloat64(prior_hi), 2)                                                              AS prior_day_high,
    round(toFloat64(hi), 2)                                                                    AS session_high,
    round(toFloat64(cl), 2)                                                                    AS session_close,
    round(100.0 * (toFloat64(hi) - toFloat64(prior_hi)) / toFloat64(prior_hi), 2)              AS poke_above_pct,
    round(100.0 * (toFloat64(prior_hi) - toFloat64(cl)) / toFloat64(prior_hi), 2)              AS close_below_pct
FROM
(
    SELECT
        date,
        hi,
        cl,
        lagInFrame(hi) OVER (ORDER BY date ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prior_hi
    FROM
    (
        SELECT
            date,
            argMax(high,  _ingest_time) AS hi,
            argMax(close, _ingest_time) AS cl
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'SPY'
          AND date >= '2024-12-31'
          AND date <  '2026-01-01'
        GROUP BY date
    )
)
WHERE date >= '2025-01-01'
  AND prior_hi > 0
  AND hi > prior_hi
  AND cl < prior_hi
ORDER BY (toFloat64(hi) - toFloat64(prior_hi)) / toFloat64(prior_hi) DESC, date DESC
LIMIT 10
⌘/Ctrl + Enter

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More from this analysisWhat Is a Liquidity Sweep in Trading?
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