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?.
| session_date | session_label | prior_day_high | session_high | session_close | poke_above_pct | close_below_pct |
|---|---|---|---|---|---|---|
| 2025-11-20 | November 20, 2025 | 667.34 | 675.56 | 652.53 | 1.23 | 2.22 |
| 2025-10-15 | October 15, 2025 | 665.83 | 670.23 | 665.17 | 0.66 | 0.1 |
| 2025-08-07 | August 7, 2025 | 633.44 | 636.98 | 632.25 | 0.56 | 0.19 |
| 2025-01-31 | January 31, 2025 | 606.6 | 609.96 | 601.82 | 0.55 | 0.79 |
| 2025-09-05 | September 5, 2025 | 649.15 | 652.21 | 647.24 | 0.47 | 0.29 |
| 2025-04-28 | April 28, 2025 | 551.05 | 553.55 | 550.85 | 0.45 | 0.04 |
| 2025-03-03 | March 3, 2025 | 594.72 | 597.34 | 583.77 | 0.44 | 1.84 |
| 2025-07-15 | July 15, 2025 | 625.16 | 627.86 | 622.14 | 0.43 | 0.48 |
| 2025-04-08 | April 8, 2025 | 523.17 | 524.98 | 496.48 | 0.35 | 5.1 |
| 2025-07-31 | July 31, 2025 | 637.68 | 639.85 | 632.08 | 0.34 | 0.88 |
- 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
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
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
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