One day of trades: session check plus the size of every print
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 2026-07-26, from Microstructure Deep-Dive: June 29, 2026.
- Rows × columns
- 1 × 8
- 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 |
|---|---|---|---|
holiday_rows_jun29 |
number | every row is 0 | |
spy_regular_session_bars |
number | every row is 390 | |
trades_m |
number | every row is 156.1 | count |
odd_lot_pct_of_trades |
number | every row is 72.61 | percent |
odd_lot_pct_of_shares |
number | every row is 8.79 | percent |
median_print_shares |
number | every row is 21 | count |
one_share_trades_m |
number | every row is 17.9 | count |
fractional_pct_of_trades |
number | every row is 4.73 | 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
(SELECT count() FROM global_markets.stocks_market_holidays WHERE date = '2026-06-29') AS holiday_rows,
(
SELECT countIf(window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00')
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY' AND window_start >= '2026-06-29 00:00:00' AND window_start < '2026-06-30 00:00:00'
) AS spy_bars
SELECT
holiday_rows AS holiday_rows_jun29,
spy_bars AS spy_regular_session_bars,
round(count() / 1e6, 1) AS trades_m,
round(100.0 * countIf(size < 100) / count(), 2) AS odd_lot_pct_of_trades,
round(100.0 * toFloat64(sumIf(size, size < 100)) / toFloat64(sum(size)), 2) AS odd_lot_pct_of_shares,
multiIf(
countIf(size <= 19) >= 0.5 * count(), 19,
countIf(size <= 20) >= 0.5 * count(), 20,
countIf(size <= 21) >= 0.5 * count(), 21,
countIf(size <= 22) >= 0.5 * count(), 22,
countIf(size <= 23) >= 0.5 * count(), 23,
0) AS median_print_shares,
round(countIf(size = 1) / 1e6, 1) AS one_share_trades_m,
round(100.0 * countIf(size != round(size)) / count(), 2) AS fractional_pct_of_trades
FROM global_markets.stocks_trades
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.