STRASMORE/EXPLORE 2,170 QUERIES

NVDA options: contracts, trades, call share, and premium by session

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 NVDA's May 2023 Earnings: The AI Gap.

as of series 7×5read in context →
NVDA options: contracts, trades, call share, and premium by session — 7 rows by 5 columns, computed from US exchange, SIP and OPRA data.
sessioncontracts_mtrades_kcall_share_pctpremium_bn
2023-05-170.8717358.20.74
2023-05-181.7833659.81.2
2023-05-191.1822757.90.72
2023-05-220.3790520.39
2023-05-230.429551.50.4
2023-05-240.9320354.50.77
2023-05-252.7560259.84.82
Rows × columns
7 × 5
Period covered
to
Computed
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 NVDA options: contracts, trades, call share, and premium by session, derived from the stored result.
ColumnTypeRangeNotes
session date 2023-05-17 to 2023-05-25
contracts_m number 0.37 to 2.75 count
trades_k number 90 to 602 count
call_share_pct number 51.5 to 59.8 percent
premium_bn number 0.39 to 4.82 US dollars

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.

the exact SQL behind every number
SELECT
    toString(toDate(toTimeZone(sip_timestamp, 'America/New_York'))) AS session,
    round(sum(size) / 1e6, 2) AS contracts_m,
    round(count() / 1e3, 0) AS trades_k,
    round(100.0 * sumIf(size, substring(ticker, 13, 1) = 'C') / sum(size), 1) AS call_share_pct,
    round(sum(size * price * 100) / 1e9, 2) AS premium_bn
FROM global_markets.options_trades
WHERE ticker LIKE 'O:NVDA2%'
  AND sip_timestamp >= toDateTime('2023-05-17 00:00:00') AND sip_timestamp < toDateTime('2023-05-26 00:00:00')
GROUP BY session
ORDER BY session

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