Minute bars per session: nine widely held names, June 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 2026-09-28, from Market Data Skills for AI Agents.
| ticker | sessions_with_data | median_bars_per_session | quietest_session_bars |
|---|---|---|---|
| NVDA | 21 | 954 | 936 |
| QQQ | 21 | 946 | 925 |
| MSFT | 21 | 921 | 876 |
| SPY | 21 | 909 | 860 |
| AAPL | 21 | 858 | 784 |
| XOM | 21 | 505 | 447 |
| KO | 21 | 502 | 470 |
| PG | 21 | 422 | 404 |
| JNJ | 21 | 418 | 399 |
- Rows × columns
- 9 × 4
- 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 | 9 distinct values (AAPL, JNJ, KO…) | |
sessions_with_data |
number | every row is 21 | |
median_bars_per_session |
number | 418 to 954 | |
quietest_session_bars |
number | 399 to 936 |
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,
count() AS bars
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('SPY', 'QQQ', 'NVDA', 'AAPL', 'MSFT', 'XOM', 'JNJ', 'KO', 'PG')
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2026-06-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-06-30')
GROUP BY ticker, session_date
)
SELECT ticker,
count() AS sessions_with_data,
round(quantileDeterministic(0.5)(bars, cityHash64(session_date)), 0) AS median_bars_per_session,
min(bars) AS quietest_session_bars
FROM daily
GROUP BY ticker
ORDER BY median_bars_per_session DESC
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.