Same session, same 10-session lookback: same-time basis vs full-day basis through the day
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 Why Relative Volume Differs Between Platforms.
| et_time | same_time_basis_ratio | full_day_basis_ratio |
|---|---|---|
| 09:45 | 3.1 | 0.22 |
| 10:00 | 2.48 | 0.3 |
| 10:30 | 1.92 | 0.39 |
| 11:00 | 1.88 | 0.51 |
| 11:30 | 1.88 | 0.63 |
| 12:00 | 2.02 | 0.79 |
| 12:30 | 2.01 | 0.88 |
| 13:00 | 2.16 | 1.03 |
| 13:30 | 2.12 | 1.11 |
| 14:00 | 2.16 | 1.22 |
| 14:30 | 2.09 | 1.29 |
| 15:00 | 2 | 1.35 |
| 15:30 | 1.98 | 1.44 |
| 16:00 | 1.77 | 1.77 |
- Rows × columns
- 14 × 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 |
|---|---|---|---|
et_time |
text | 14 distinct values (09:45, 10:00, 10:30…) | |
same_time_basis_ratio |
number | 1.77 to 3.1 | ratio or rate |
full_day_basis_ratio |
number | 0.22 to 1.77 | ratio or rate |
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
toDate('2026-09-10') AS session_day,
[585, 600, 630, 660, 690, 720, 750, 780, 810, 840, 870, 900, 930, 960] AS checkpoints
SELECT
formatDateTime(toDateTime(session_day, 'UTC') + checkpoints[i] * 60, '%H:%i', 'UTC') AS et_time,
round(session_cums[i] / typical_cums[i], 2) AS same_time_basis_ratio,
round(session_cums[i] / typical_cums[14], 2) AS full_day_basis_ratio
FROM
(
SELECT
arrayJoin(arrayEnumerate(checkpoints)) AS i,
session_cums,
arrayMap(k -> arrayAvg(x -> arrayElement(tupleElement(x, 2), k), arraySlice(prior, 1, 10)),
arrayEnumerate(checkpoints)) AS typical_cums
FROM
(
SELECT
anyIf(cums, d = session_day) AS session_cums,
arrayReverseSort(x -> tupleElement(x, 1), groupArrayIf((d, cums), d < session_day)) AS prior
FROM
(
SELECT
d,
arrayMap(cp -> arraySum(x -> if(tupleElement(x, 1) <= cp, tupleElement(x, 2), 0), mv),
checkpoints) AS cums
FROM
(
SELECT d, groupArray((minute_of_day, vol)) AS mv
FROM
(
SELECT
toDate(toTimeZone(window_start, 'America/New_York')) AS d,
toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York')) AS minute_of_day,
max(toFloat64(volume)) AS vol
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'AAPL'
AND window_start >= toDateTime(session_day - 20, 'America/New_York')
AND window_start < toDateTime(session_day + 1, 'America/New_York')
GROUP BY d, minute_of_day
HAVING minute_of_day >= 570 AND minute_of_day <= 959
UNION ALL
SELECT
toDate(toTimeZone(sip_timestamp, 'America/New_York')) AS d,
960 AS minute_of_day,
toFloat64(maxIf(size, has(conditions, 8))) AS vol
FROM global_markets.stocks_trades
WHERE ticker = 'AAPL'
AND sip_timestamp >= toDateTime(session_day - 20, 'America/New_York')
AND sip_timestamp < toDateTime(session_day + 1, 'America/New_York')
GROUP BY d
HAVING countIf(has(conditions, 8)) > 0
)
GROUP BY d
)
)
HAVING length(session_cums) = 14 AND length(prior) >= 10
)
)
ORDER BY i
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.