Market Data Skills for AI Agents
Trading sessions per month: SPY bars, July 2025 through June 2026series ·
2026-08-01 · 12×4
Where the volume sits: share of June 2026 volume by half hour, New York timeseries ·
2026-08-01 · 32×3
One company, two tickers: share class volume split, June 2026ranking ·
2026-08-01 · 6×4
Minute bars per session: nine widely held names, June 2026series ·
2026-08-01 · 9×4
Trading sessions per month: SPY bars, July 2025 through June 2026
Trading sessions per month: SPY bars, July 2025 through June 2026
| month | sessions | calendar_days | short_sessions |
|---|---|---|---|
| 2025-07 | 22 | 31 | 2 |
| 2025-08 | 21 | 31 | 0 |
| 2025-09 | 21 | 30 | 1 |
| 2025-10 | 23 | 31 | 0 |
| 2025-11 | 19 | 30 | 1 |
| 2025-12 | 22 | 31 | 1 |
| 2026-01 | 20 | 31 | 1 |
| 2026-02 | 19 | 28 | 1 |
| 2026-03 | 22 | 31 | 2 |
| 2026-04 | 21 | 30 | 1 |
| 2026-05 | 20 | 31 | 2 |
| 2026-06 | 21 | 30 | 2 |
the exact SQL behind every number
WITH sessions AS (
SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS session_date,
argMax(formatDateTime(toTimeZone(window_start, 'America/New_York'), '%H:%i'), volume) AS busiest_minute
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2025-07-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-06-30')
GROUP BY session_date
)
SELECT formatDateTime(toStartOfMonth(session_date), '%Y-%m') AS month,
count() AS sessions,
toDayOfMonth(toLastDayOfMonth(min(session_date))) AS calendar_days,
countIf(busiest_minute < '15:00') AS short_sessions
FROM sessions
GROUP BY toStartOfMonth(session_date)
ORDER BY toStartOfMonth(session_date)
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