session_shape

Name: session_shape
Creator: Strasmore
Published: 2026-09-26
License: https://www.strasmore.com/terms/

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 2026-09-26, from the-10-am-rule-in-stocks.

as of 2026-09-26 series 13×3read in context →

session_shape — 13 rows by 3 columns, computed 2026-09-26 from US exchange, SIP and OPRA data.
et_time	volume_share_pct	range_share_pct
09:30	15.5	54.5
10:00	9.8	38.1
10:30	8	31.5
11:00	7	27.6
11:30	6	24.7
12:00	5.4	22.6
12:30	4.9	21.3
13:00	5	21.6
13:30	4.9	20.8
14:00	5.4	21.7
14:30	5.5	20.6
15:00	6.5	21
15:30	15.9	29.1

Rows × columns: 13 × 3
Computed: 2026-09-26
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 session_shape, derived from the stored result.
Column	Type	Range	Notes
`et_time`	text	13 distinct values (09:30, 10:00, 10:30…)
`volume_share_pct`	number	4.9 to 15.9	percent
`range_share_pct`	number	20.6 to 54.5	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 2026-09-26, 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 bars AS (
    SELECT
        ticker,
        toDate(toTimeZone(window_start, 'America/New_York'))            AS trade_day,
        toHour(toTimeZone(window_start, 'America/New_York')) * 60
            + toMinute(toTimeZone(window_start, 'America/New_York'))    AS et_minute,
        high,
        low,
        volume
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'AAPL', 'MSFT', 'NVDA', 'KO')
      AND window_start >= '2023-01-01'
      AND window_start <  '2026-01-01'
),
regular AS (
    SELECT *
    FROM bars
    WHERE et_minute >= 570 AND et_minute < 960
),
day_totals AS (
    SELECT
        ticker,
        trade_day,
        max(high)   AS day_high,
        min(low)    AS day_low,
        sum(volume) AS day_volume,
        count()     AS bar_count
    FROM regular
    GROUP BY ticker, trade_day
    HAVING bar_count >= 300
       AND day_high > day_low
       AND day_volume > 0
),
half_hours AS (
    SELECT
        ticker,
        trade_day,
        intDiv(et_minute - 570, 30) AS bucket,
        max(high)                   AS bucket_high,
        min(low)                    AS bucket_low,
        sum(volume)                 AS bucket_volume
    FROM regular
    GROUP BY ticker, trade_day, bucket
)
SELECT
    formatDateTime(toDateTime((570 + h.bucket * 30) * 60, 'UTC'), '%H:%i') AS et_time,
    round(avg(100 * toFloat64(h.bucket_volume) / toFloat64(d.day_volume)), 1) AS volume_share_pct,
    round(avg(100 * toFloat64(h.bucket_high - h.bucket_low)
                  / toFloat64(d.day_high - d.day_low)), 1)                   AS range_share_pct
FROM half_hours AS h
INNER JOIN day_totals AS d
    ON d.ticker = h.ticker AND d.trade_day = h.trade_day
GROUP BY h.bucket
ORDER BY h.bucket

SQL to run

WITH bars AS (
    SELECT
        ticker,
        toDate(toTimeZone(window_start, 'America/New_York'))            AS trade_day,
        toHour(toTimeZone(window_start, 'America/New_York')) * 60
            + toMinute(toTimeZone(window_start, 'America/New_York'))    AS et_minute,
        high,
        low,
        volume
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'AAPL', 'MSFT', 'NVDA', 'KO')
      AND window_start >= '2023-01-01'
      AND window_start <  '2026-01-01'
),
regular AS (
    SELECT *
    FROM bars
    WHERE et_minute >= 570 AND et_minute < 960
),
day_totals AS (
    SELECT
        ticker,
        trade_day,
        max(high)   AS day_high,
        min(low)    AS day_low,
        sum(volume) AS day_volume,
        count()     AS bar_count
    FROM regular
    GROUP BY ticker, trade_day
    HAVING bar_count >= 300
       AND day_high > day_low
       AND day_volume > 0
),
half_hours AS (
    SELECT
        ticker,
        trade_day,
        intDiv(et_minute - 570, 30) AS bucket,
        max(high)                   AS bucket_high,
        min(low)                    AS bucket_low,
        sum(volume)                 AS bucket_volume
    FROM regular
    GROUP BY ticker, trade_day, bucket
)
SELECT
    formatDateTime(toDateTime((570 + h.bucket * 30) * 60, 'UTC'), '%H:%i') AS et_time,
    round(avg(100 * toFloat64(h.bucket_volume) / toFloat64(d.day_volume)), 1) AS volume_share_pct,
    round(avg(100 * toFloat64(h.bucket_high - h.bucket_low)
                  / toFloat64(d.day_high - d.day_low)), 1)                   AS range_share_pct
FROM half_hours AS h
INNER JOIN day_totals AS d
    ON d.ticker = h.ticker AND d.trade_day = h.trade_day
GROUP BY h.bucket
ORDER BY h.bucket

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