Median and 90th percentile full day range, eight names, H1 2026
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 2026-08-01, from Pattern Day Trader Rule: What Replaced It.
| ticker | median_range_pct | p90_range_pct |
|---|---|---|
| TSLA | 3.93 | 6 |
| NVDA | 3.37 | 5.72 |
| MSFT | 2.63 | 4.96 |
| AAPL | 2.44 | 3.95 |
| JNJ | 1.94 | 3.15 |
| QQQ | 1.78 | 3.2 |
| KO | 1.76 | 2.68 |
| SPY | 1.18 | 2.31 |
- Rows × columns
- 8 × 3
- 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 |
|---|---|---|---|
ticker |
text | 8 distinct values (AAPL, JNJ, KO…) | |
median_range_pct |
number | 1.18 to 3.93 | percent |
p90_range_pct |
number | 2.31 to 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 daily AS (
SELECT ticker,
toDate(toTimeZone(window_start, 'America/New_York')) AS session_date,
toFloat64(max(high)) AS day_high,
toFloat64(min(low)) AS day_low,
toFloat64(argMin(open, window_start)) AS day_open
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('SPY', 'QQQ', 'AAPL', 'MSFT', 'NVDA', 'TSLA', 'KO', 'JNJ')
AND window_start >= toDateTime('2026-01-01 05:00:00')
AND window_start < toDateTime('2026-07-01 04:00:00')
GROUP BY ticker, session_date
)
SELECT ticker,
round(quantileDeterministic(0.5)(100 * (day_high - day_low) / day_open, cityHash64(session_date)), 2) AS median_range_pct,
round(quantileDeterministic(0.9)(100 * (day_high - day_low) / day_open, cityHash64(session_date)), 2) AS p90_range_pct
FROM daily
WHERE day_open > 0
GROUP BY ticker
ORDER BY median_range_pct DESC
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