thai_clock_spread
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 2026-10-09, from us-premarket-and-after-hours-thai-time.
| ict_clock | et_clock | aapl_spread_bps | etsy_spread_bps |
|---|---|---|---|
| 15:00 | 04:00 | 5.9 | 436.9 |
| 16:00 | 05:00 | 5.9 | 446.7 |
| 17:00 | 06:00 | 6.2 | 459.2 |
| 18:00 | 07:00 | 5 | 264 |
| 19:00 | 08:00 | 2.9 | 324.8 |
| 20:00 | 09:00 | 4.7 | 178.3 |
| 21:00 | 10:00 | 1.8 | 19.1 |
| 03:00 | 16:00 | 3.5 | 362.3 |
| 04:00 | 17:00 | 1.8 | 410.9 |
| 05:00 | 18:00 | 3.5 | 250.2 |
| 06:00 | 19:00 | 2.1 | 183.6 |
- Rows × columns
- 11 × 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 |
|---|---|---|---|
ict_clock |
text | 11 distinct values (03:00, 04:00, 05:00…) | |
et_clock |
text | 11 distinct values (04:00, 05:00, 06:00…) | |
aapl_spread_bps |
number | 1.8 to 6.2 | |
etsy_spread_bps |
number | 19.1 to 459.2 |
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 quotes AS
(
SELECT
ticker,
toHour(toTimeZone(sip_timestamp, 'America/New_York')) AS et_hour,
toTimeZone(sip_timestamp, 'America/New_York') AS et_ts,
toTimeZone(sip_timestamp, 'Asia/Bangkok') AS ict_ts,
toUInt64(sequence_number) AS seq,
20000 * toFloat64(ask_price - bid_price) / toFloat64(ask_price + bid_price) AS spread_bps
FROM global_markets.cache_stocks_quotes
WHERE ticker IN ('AAPL', 'ETSY')
AND bid_price > 0
AND ask_price > bid_price
AND (
(sip_timestamp >= '2026-09-22 08:00:00' AND sip_timestamp < '2026-09-22 13:30:00')
OR (sip_timestamp >= '2026-09-22 14:00:00' AND sip_timestamp < '2026-09-22 14:30:00')
OR (sip_timestamp >= '2026-09-22 20:00:00' AND sip_timestamp < '2026-09-23 00:00:00')
)
)
SELECT
formatDateTime(min(ict_ts), '%H:00') AS ict_clock,
formatDateTime(min(et_ts), '%H:00') AS et_clock,
round(quantileDeterministicIf(0.5)(spread_bps, seq, ticker = 'AAPL'), 1) AS aapl_spread_bps,
if(countIf(ticker = 'ETSY') > 0,
round(quantileDeterministicIf(0.5)(spread_bps, seq, ticker = 'ETSY'), 1),
NULL) AS etsy_spread_bps
FROM quotes
GROUP BY et_hour
HAVING countIf(ticker = 'AAPL') > 0
ORDER BY et_hour
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.