Strasmore Research
Deep Dives · Matt ConnorBy Matt Connor · · Updated 2026-07-25

NVDA: Full June 2026, Tick by Tick

NVIDIA June 2026 in full: a $235 pre-market peak, a 7.4% slide across 21 sessions, and di fourth-biggest dollar volume on di US tape.

NVIDIA open June 2026 for $215.77 and close di month for $199.76 — na -7.4% drop inside 21 sessions. Di stock reach im highest for $235 during pre-market trading for 2026-06-02 04:00 ET, hit regular-hours high of $232.28 for 2026-06-02 09:59 ET, and touch im lowest for $189.8 for 2026-06-29 10:17 ET — all na regular hours. Total volume: 2.75 billion shares, $571.9 billion for dollar turnover — e be di fourth-biggest name for US tape by regular-hours dollar volume (basis na June 1–30 regular hours; dem exclude one reused-symbol June listing until dem verify di entity). Every number here na stored query result; expand any panel make you see di exact SQL.

QueryDi month for one row: open, close, extremes, volume, and dia receipts
The exact SQL behind every number
WITH
    (
        SELECT (toString(argMax(et_date, c)), max(c), argMax(c, et_date))
        FROM (
            SELECT
                toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
                argMax(toFloat64(close), window_start) AS c
            FROM global_markets.delayed_stocks_minute_aggs
            WHERE ticker = 'NVDA'
              AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
              AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
            GROUP BY et_date
        )
    ) AS closes,
    (
        SELECT max(toFloat64(high)) FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'NVDA'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    ) AS hi,
    (
        SELECT min(toFloat64(low)) FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'NVDA'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    ) AS lo,
    (
        SELECT maxIf(toFloat64(high), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199)
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'NVDA'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    ) AS rth_hi,
    (
        SELECT minIf(toFloat64(low), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199)
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'NVDA'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    ) AS rth_lo,
    (
        SELECT count() FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY'
          AND window_start >= toDateTime('2026-06-19 00:00:00') AND window_start < toDateTime('2026-06-20 00:00:00')
    ) AS spy_jun19
SELECT
    round(toFloat64(argMin(open, window_start)), 2) AS month_open,
    closes.3 AS month_close,
    round((closes.3 / toFloat64(argMin(open, window_start)) - 1) * 100, 1) AS month_change_pct,
    round((1 - closes.3 / toFloat64(argMin(open, window_start))) * 100, 1) AS month_decline_abs_pct,
    closes.1 AS peak_close_date,
    round(closes.2, 2) AS peak_close,
    round(hi, 2) AS month_high,
    formatDateTime(toTimeZone(minIf(window_start, toFloat64(high) >= hi - 0.011), 'America/New_York'), '%Y-%m-%d %H:%i') AS month_high_first_bar_et,
    countIf(toFloat64(high) >= hi - 0.011) AS bars_within_cent_of_high,
    argMinIf(transactions, window_start, toFloat64(high) >= hi - 0.011) AS high_minute_trades,
    round(rth_hi, 2) AS rth_month_high,
    formatDateTime(toTimeZone(minIf(window_start, toFloat64(high) >= rth_hi - 0.011 AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199), 'America/New_York'), '%Y-%m-%d %H:%i') AS rth_high_first_bar_et,
    countIf(toFloat64(high) >= rth_hi - 0.011 AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) AS rth_bars_near_high,
    round(lo, 2) AS month_low,
    formatDateTime(toTimeZone(minIf(window_start, toFloat64(low) <= lo + 0.011), 'America/New_York'), '%Y-%m-%d %H:%i') AS month_low_bar_et,
    countIf(toFloat64(low) <= lo + 0.011) AS bars_within_cent_of_low,
    argMin(transactions, toFloat64(low)) AS low_minute_trades,
    round(rth_lo, 2) AS rth_month_low,
    round(rth_lo - lo, 2) AS rth_minus_extended_low,
    round(toFloat64(sum(volume)) / 1e9, 2) AS month_shares_bn,
    round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 1) AS month_dollar_bn,
    round(sumIf(toFloat64(close) * toFloat64(volume), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / 1e9, 1) AS rth_dollar_bn,
    round(sumIf(toFloat64(volume), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / 1e9, 2) AS rth_shares_bn,
    uniqExact(toDate(toTimeZone(window_start, 'America/New_York'))) AS session_days_observed,
    spy_jun19 AS spy_bars_june19
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'NVDA'
  AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')

Di movement na steady downtrend with one sharp gap down for di last week. NVDA close above $224.43 just one time — na for 2026-06-01, di first session. Di month low show for regular hours for 2026-06-29 10:17 ET, with 1 bar wey dey within one cent and 43131 trades for dat minute. Di pre-market $235 peak for 2026-06-02 04:00 ET na extended-hours print; di regular-hours high of $232.28 stand for 2026-06-02 09:59 ET. June 19 na market-wide closure (0 SPY bars dat day), so NVDA get 21 trading sessions.

Session by session

QueryDi 21 sessions: regular-hours close, close-over-close change, full-day volume
The exact SQL behind every number
SELECT
    et_date,
    close_usd,
    round(if(prev_close = 0, NULL, (close_usd / prev_close - 1) * 100), 1) AS change_pct,
    shares_m,
    dollar_bn
FROM (
    SELECT et_date, close_usd, shares_m, dollar_bn,
           lagInFrame(close_usd) OVER (ORDER BY et_date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
    FROM (
        SELECT
            toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
            round(argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199), 2) AS close_usd,
            round(toFloat64(sum(volume)) / 1e6, 1) AS shares_m,
            round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 2) AS dollar_bn
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'NVDA'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
        GROUP BY et_date
    )
)
ORDER BY et_date

Di session table dey show one steady slide. Di first close wey pass $224.43 na im set di high-water mark for 2026-06-01. Di steepest single-session drop na -6.2% wey happen for 2026-06-05 — na Friday — and e match di highest volume for di month wey be 187.3 million shares. Di main stretch na 5 straight declines, from 2026-06-22 go reach 2026-06-26, wey end for di month lowest close of $191.72; di last two sessions recover 1.7% and 2.5%. Volume heavy for front: 185.5 million shares for day one, 119.6 million for di last day.

QueryDi five-session slide, bounded: every claim for di streak sentence as checked column
The exact SQL behind every number
WITH per_session AS (
    SELECT et_date, close_usd,
           lagInFrame(close_usd) OVER (ORDER BY et_date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
    FROM (
        SELECT
            toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
            round(argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199), 2) AS close_usd
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'NVDA'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
        GROUP BY et_date
    )
)
SELECT
    toString(minIf(et_date, et_date >= toDate('2026-06-22') AND et_date <= toDate('2026-06-26'))) AS run_start,
    toString(maxIf(et_date, et_date >= toDate('2026-06-22') AND et_date <= toDate('2026-06-26'))) AS run_end,
    countIf(et_date >= toDate('2026-06-22') AND et_date <= toDate('2026-06-26')) AS sessions_in_run,
    countIf(et_date >= toDate('2026-06-22') AND et_date <= toDate('2026-06-26') AND close_usd < prev_close) AS declining_sessions,
    round(minIf(close_usd, et_date >= toDate('2026-06-22') AND et_date <= toDate('2026-06-26')), 2) AS run_low_close,
    toUInt8(min(close_usd) = minIf(close_usd, et_date >= toDate('2026-06-22') AND et_date <= toDate('2026-06-26'))) AS run_holds_month_low_close
FROM per_session

June against im trailing six months

Dis kind month for NVDA, e be like say e no normal, or e just be ordinary? Di panel wey dey below dey recalculate di same three figures for each of di last six months for one single run — di June row na exactly di same query wey produce di five before am, fresh from di warehouse for generation time.

QueryDi trailing six months, recomputed live: turnover, shares, and month return
The exact SQL behind every number
SELECT
    toString(toStartOfMonth(toDate(toTimeZone(window_start, 'America/New_York')))) AS period_start,
    round(sumIf(toFloat64(close) * toFloat64(volume), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / 1e9, 1) AS rth_dollar_bn,
    round(toFloat64(sum(volume)) / 1e9, 2) AS shares_bn,
    round((argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199)
           / argMinIf(toFloat64(open), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) - 1) * 100, 1) AS month_return_pct
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'NVDA'
  AND window_start >= toDateTime('2026-01-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
GROUP BY period_start
ORDER BY period_start

By turnover, June bin ordinary: $523 billion for regular hours sit for mid-range inside di window, e dey below May im $557.5 billion and March im $572.5 billion. Di return column na where June stand out: -7.4% na di sharpest monthly decline of di six, against -4.8% for February and gains for April and May.

Di fourth-biggest ticker for di tape

QueryDi whole US tape ranked by June 2026 regular-hours dollar volume
The exact SQL behind every number
SELECT
    ticker,
    round(sum(toFloat64(volume) * toFloat64(close)) / 1e9, 1) AS regular_hours_dollar_bn,
    round(100 * sum(toFloat64(volume) * toFloat64(close)) / max(sum(toFloat64(volume) * toFloat64(close))) OVER (), 1) AS pct_of_leader,
    toUInt8(ticker = 'NVDA') AS is_nvda
FROM global_markets.delayed_stocks_minute_aggs
WHERE window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
  AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
AND ticker NOT IN ('SPCX')
GROUP BY ticker
ORDER BY regular_hours_dollar_bn DESC
LIMIT 12

NVDA rank fourth for di whole US tape by regular-hours dollar volume: $523 billion, e pass SNDK wey dey under am with $141.5 billion, na only MU ($995.7 billion), SPY ($771.5 billion), and QQQ ($672.8 billion) dey im front. Di basis na June 1–30 regular hours, dem comot one reused-symbol June listing until dem fit confam di entity — im receipts dey inside im own deep-dive. Out of di three names wey dey above NVDA, 1 na single stock — MU, anoda semiconductor name; di oda two na di big index ETFs.

QueryDi rank receipt: NVDA place, im lead over di next name, and di basis — as checked columns
The exact SQL behind every number
WITH (
    SELECT sum(toFloat64(volume) * toFloat64(close))
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'NVDA'
      AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
) AS nvda_d
SELECT
    countIf(d > nvda_d AND ticker != 'NVDA') + 1 AS nvda_rank,
    round(nvda_d / 1e9, 1) AS nvda_dollar_bn,
    round((nvda_d - maxIf(d, d < nvda_d AND ticker != 'NVDA')) / 1e9, 1) AS lead_over_next_bn,
    countIf(d > nvda_d AND ticker NOT IN ('SPY', 'QQQ', 'NVDA')) AS single_stocks_above_nvda,
    round(100 * nvda_d / max(d), 1) AS pct_of_leader
FROM (
    SELECT ticker, sum(toFloat64(volume) * toFloat64(close)) AS d
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
)

Wetin dey inside the tape

QueryDi whole NVDA tape for one row: prints, print sizes, and di quote census
The exact SQL behind every number
WITH
    (
        SELECT (round(count() / 1e6, 2),
                round(100.0 * countIf(bid_price > 0 AND ask_price > 0 AND ask_price > bid_price) / count(), 2),
                countIf(bid_price > 0 AND ask_price > 0 AND ask_price = bid_price),
                countIf(bid_price > 0 AND ask_price > 0 AND ask_price < bid_price),
                countIf(bid_price <= 0 OR ask_price <= 0))
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'NVDA'
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
    ) AS quote_census
SELECT
    round(count() / 1e6, 2) AS prints_m,
    quantileDeterministic(0.5)(toFloat64(size), toUInt64(abs(sequence_number))) AS median_print_shares,
    round(avg(toFloat64(size)), 1) AS avg_print_shares,
    round(100.0 * countIf(size < 100) / count(), 1) AS odd_lot_pct_of_prints,
    round(100.0 * countIf(toFloat64(size) != round(toFloat64(size))) / count(), 2) AS fractional_pct_of_prints,
    quote_census.1 AS nbbo_updates_m,
    quote_census.2 AS clean_two_sided_pct,
    quote_census.3 AS locked_updates,
    quote_census.4 AS crossed_updates,
    quote_census.5 AS one_sided_or_empty_updates
FROM global_markets.stocks_trades
WHERE ticker = 'NVDA'
  AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)

The texture na small-print and quote-dense: 55.64 million prints for median of 5 shares (average na 72.9 — institutional crosses dey pull the mean up), 84.6% odd lots (under 100 shares), 32.91% fractional — e dey consistent with retail apps wey dey slice orders into market-maker inventory. The quote half: 55.7 million NBBO updates, 99.35% clean two-sided; 33941 momentarily crossed (bid above ask), 329679 locked (bid equal to ask), 63 one-sided or empty. One mega-cap dey print more quotes for one month pass wetin many names fit print for ten years.

Di spread stay tight

One mega-cap like NVDA dey carry one structurally narrow bid-ask spread — di question na how stable dat tightness bin dey across one month wey get 20-point price range. Di quote census wey dey up show di month-level median; dis panel dey measure am per session.

QueryDi spread by session: regular-hours median and time-weighted average (bps)
The exact SQL behind every number
SELECT
    session,
    round(quantileDeterministicIf(0.5)(toFloat64(ask_price) - toFloat64(bid_price), toUInt64(toUnixTimestamp64Micro(sip_timestamp)), bid_price > 0 AND ask_price >= bid_price) * 100, 1) AS med_spread_cents,
    round(quantileDeterministicIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, toUInt64(toUnixTimestamp64Micro(sip_timestamp)), bid_price > 0 AND ask_price >= bid_price), 2) AS med_spread_bps,
    count() AS quote_updates,
    countIf(NOT (bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price)) AS invalid_dropped
FROM global_markets.cache_stocks_quotes
WHERE ticker = 'NVDA'
  AND sip_timestamp >= toDateTime64('2026-06-01 13:30:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
  AND (toHour(sip_timestamp) * 60 + toMinute(sip_timestamp)) BETWEEN 810 AND 1199
GROUP BY toDate(toTimeZone(sip_timestamp, 'America/New_York')) AS session
ORDER BY session

Di spread hold for one narrow band all through di month: 1.34 bps for 2026-06-01 and 1.01 bps for 2026-06-30 — one mega-cap spread wey barely move over 20-point price drop. Di session wey wide pass na 2026-06-05 at 0.97 bps (di -6.2% decline day), di one wey tight pass na 2026-06-15 at 0.95 bps. For scale against one thin name, check di June 29 microstructure deep-dive wey one single-ticker spread panel cover di same session.

Options: 64.64 million contracts, calls win every session

QueryNVDA options market for one row: totals, expiry structure, flagship contracts
The exact SQL behind every number
WITH
    (
        SELECT (round(sum(toFloat64(price) * size) * 100 / 1e9, 2), round(sum(size) / 1e6, 1))
        FROM global_markets.options_trades
        WHERE startsWith(ticker, 'O:AAPL') AND length(ticker) = 21
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
    ) AS aapl,
    (
        SELECT (round(sum(toFloat64(price) * size) * 100 / 1e9, 2), round(sum(size) / 1e6, 1))
        FROM global_markets.options_trades
        WHERE startsWith(ticker, 'O:TSLA') AND length(ticker) = 21
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
    ) AS tsla,
    (
        SELECT concat('$', toString(round(toFloat64(toUInt32OrZero(substring(ticker, 14, 8))) / 1000, 2)),
               if(substring(ticker, 13, 1) = 'P', ' put', ' call'),
               ', expiry 20', substring(ticker, 7, 2), '-', substring(ticker, 9, 2), '-', substring(ticker, 11, 2))
        FROM global_markets.options_trades
        WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
        GROUP BY ticker ORDER BY sum(size) DESC LIMIT 1
    ) AS busiest_name,
    (
        SELECT concat('$', toString(round(toFloat64(toUInt32OrZero(substring(ticker, 14, 8))) / 1000, 2)),
               if(substring(ticker, 13, 1) = 'P', ' put', ' call'),
               ', expiry 20', substring(ticker, 7, 2), '-', substring(ticker, 9, 2), '-', substring(ticker, 11, 2))
        FROM global_markets.options_trades
        WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
        GROUP BY ticker ORDER BY sum(toFloat64(price) * size) DESC LIMIT 1
    ) AS premium_name,
    (
        SELECT round(toFloat64(toUInt32OrZero(substring(ticker, 14, 8))) / 1000, 2)
        FROM global_markets.options_trades
        WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
        GROUP BY ticker ORDER BY sum(toFloat64(price) * size) DESC LIMIT 1
    ) AS premium_strike,
    (
        SELECT max(pc)
        FROM (
            SELECT round(toFloat64(sumIf(size, substring(ticker, 13, 1) = 'P')) / toFloat64(sumIf(size, substring(ticker, 13, 1) = 'C')), 3) AS pc
            FROM global_markets.options_trades
            WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
              AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
            GROUP BY toDate(sip_timestamp)
        )
    ) AS max_daily_pc
SELECT
    formatDateTime(toTimeZone(min(sip_timestamp), 'America/New_York'), '%Y-%m-%d %H:%i:%S') AS first_print_et,
    uniqExact(toDate(sip_timestamp)) AS option_sessions,
    round(count() / 1e6, 2) AS prints_m,
    uniqExact(ticker) AS distinct_contracts,
    round(sum(size) / 1e6, 2) AS contracts_traded_m,
    round(sum(toFloat64(price) * size) * 100 / 1e9, 2) AS premium_notional_busd,
    round(toFloat64(sumIf(size, substring(ticker, 13, 1) = 'P')) / toFloat64(sumIf(size, substring(ticker, 13, 1) = 'C')), 2) AS month_put_call_ratio,
    max_daily_pc AS max_session_put_call_ratio,
    uniqExact(substring(ticker, 7, 6)) AS expiries_traded,
    concat('20', substring(max(substring(ticker, 7, 6)), 1, 2), '-', substring(max(substring(ticker, 7, 6)), 3, 2), '-', substring(max(substring(ticker, 7, 6)), 5, 2)) AS longest_expiry,
    round(100 * toFloat64(sumIf(size, substring(ticker, 7, 6) = '260618')) / toFloat64(sum(size)), 1) AS jun18_expiry_share_pct,
    busiest_name AS busiest_contract,
    premium_name AS top_premium_contract,
    premium_strike AS top_premium_strike_usd,
    aapl.1 AS aapl_premium_bn,
    aapl.2 AS aapl_contracts_m,
    tsla.1 AS tsla_premium_bn,
    tsla.2 AS tsla_contracts_m
FROM global_markets.options_trades
WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
  AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)

For 21 option sessions: 8.9 million prints across 5163 different contracts, 64.64 million contracts and $24.43 billion in premium (price times the 100-share multiplier). The month put/call ratio na 0.55 — calls out-trade puts for every single session; the daily ratio reach im highest point for 0.782 and e no ever touch 1.0. 38 expiries trade, from the June 18 weekly go reach 2028-12-15 LEAPS; the June 18 expiry alone carry 10.5% of month volume. The contract wey busy pass: $210 call, expiry 2026-06-18. Premium magnet: $0.5 call, expiry 2026-12-18 — na deep-in-the-money $0.5 strike. To compare, AAPL options collect $8.84 billion on 27 million contracts for June; TSLA collect $33.68 billion on 58 million.

QueryOptions session by session: contracts, di call/put split, and di put/call ratio
The exact SQL behind every number
SELECT
    toDate(sip_timestamp) AS session,
    count() AS prints,
    toUInt64(sum(size)) AS contracts_traded,
    toUInt64(sumIf(size, substring(ticker, 13, 1) = 'C')) AS call_contracts,
    toUInt64(sumIf(size, substring(ticker, 13, 1) = 'P')) AS put_contracts,
    round(toFloat64(sumIf(size, substring(ticker, 13, 1) = 'P')) / toFloat64(sumIf(size, substring(ticker, 13, 1) = 'C')), 2) AS put_call_ratio,
    round(100 * toFloat64(sum(size)) / max(toFloat64(sum(size))) OVER (), 1) AS pct_of_busiest_session
FROM global_markets.options_trades
WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
  AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
GROUP BY session
ORDER BY session

The busiest options session na 2026-06-05 — 5298529 contracts, 100% of the month peak — the same -6.2% decline day wey post the highest equity volume. The put/call ratio rise as the price dey fall: e bottom for 0.33 on 2026-06-02 (near the price peak) and e climb go 0.61 by 2026-06-12 (the session wey the stock first close below $205.14). Put volume grow as the price fall — hedging activity or directional bets; the data show the co-movement, no be the intent.

QueryWhere di contracts land: call and put volume by strike bucket
The exact SQL behind every number
SELECT
    concat('$', toString(toUInt32(bucket))) AS strike_bucket,
    call_contracts,
    put_contracts,
    round(100.0 * put_contracts / (call_contracts + put_contracts), 1) AS put_share_pct,
    round(100 * (call_contracts + put_contracts) / max(call_contracts + put_contracts) OVER (), 1) AS pct_of_biggest_bucket
FROM (
    SELECT
        least(floor(toFloat64(toUInt32OrZero(substring(ticker, 14, 8))) / 1000 / 25) * 25, 450) AS bucket,
        toUInt64(sumIf(size, substring(ticker, 13, 1) = 'C')) AS call_contracts,
        toUInt64(sumIf(size, substring(ticker, 13, 1) = 'P')) AS put_contracts
    FROM global_markets.options_trades
    WHERE startsWith(ticker, 'O:NVDA') AND length(ticker) = 21
      AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
    GROUP BY bucket
)
ORDER BY toUInt32OrZero(substring(strike_bucket, 2))

The strike map na barbell around the $200 trading range. The $200 bucket — at the money — carry the most volume, split 32.7% puts (two-way traffic). Below the range puts dey dominate (88.3% of the $150 bucket); above am calls own the book (6.3% puts at $225, 120039 contracts at the $450 ceiling). The structure na textbook: protective puts for down, speculative calls for up, two-way market for at the money.

Wetin dey happen

QueryDi information flow for one row: volume, composition, and co-tags
The exact SQL behind every number
WITH
    (
        SELECT (toString(d), n)
        FROM (
            SELECT toDate(toTimeZone(published_utc, 'America/New_York')) AS d, count() AS n
            FROM global_markets.stocks_news
            WHERE has(tickers, 'NVDA')
              AND published_utc >= toDateTime('2026-06-01 00:00:00')
              AND published_utc < toDateTime('2026-07-01 04:00:00')
            GROUP BY d ORDER BY n DESC, d ASC LIMIT 1
        )
    ) AS peak_day,
    (
        SELECT (JSONExtractString(any(publisher), 'name'), count())
        FROM global_markets.stocks_news
        WHERE has(tickers, 'NVDA')
          AND published_utc >= toDateTime('2026-06-01 00:00:00')
          AND published_utc < toDateTime('2026-07-01 04:00:00')
        GROUP BY JSONExtractString(publisher, 'name') ORDER BY count() DESC LIMIT 1
    ) AS top_pub
SELECT
    count() AS june_articles,
    uniqExact(JSONExtractString(publisher, 'name')) AS publishers,
    toString(min(toDate(toTimeZone(published_utc, 'America/New_York')))) AS first_tagged_day,
    peak_day.1 AS peak_day_date,
    peak_day.2 AS peak_day_articles,
    top_pub.1 AS top_publisher,
    top_pub.2 AS top_publisher_articles,
    round(100.0 * top_pub.2 / count(), 0) AS top_publisher_pct,
    countIf(has(tickers, 'TSLA')) AS tsla_co_articles,
    countIf(has(tickers, 'AMD')) AS amd_co_articles,
    countIf(has(tickers, 'MSFT')) AS msft_co_articles,
    countIf(has(tickers, 'AAPL')) AS aapl_co_articles
FROM global_markets.stocks_news
WHERE has(tickers, 'NVDA')
  AND published_utc >= toDateTime('2026-06-01 00:00:00')
  AND published_utc < toDateTime('2026-07-01 04:00:00')

642 articles wey get NVDA tag for June from 4 publishers. Make you read those numbers with sense: The Motley Fool alone write 63% — na one feed attention, no be say the whole world media dey talk am. The day wey the matter hot pass na 2026-06-01 with 45 articles. Co-tags show how dem dey frame the story: MSFT dey inside 169, AAPL dey inside 134, AMD dey inside 101, TSLA dey inside 69 — this feed cover NVDA as mega-cap tech story, no be semiconductor story.

Short side

QueryFINRA off-exchange short volume by session: marked-short share and reported volume
The exact SQL behind every number
SELECT
    date,
    round(toFloat64(any(short_volume)) / 1e6, 2) AS short_shares_m,
    round(toFloat64(any(total_volume)) / 1e6, 2) AS offexchange_total_m,
    round(100 * toFloat64(any(short_volume)) / toFloat64(any(total_volume)), 1) AS short_pct_of_offexchange
FROM global_markets.stocks_short_volume
WHERE ticker = 'NVDA' AND date >= toDate('2026-06-01') AND date <= toDate('2026-06-30')
GROUP BY date
ORDER BY date

Off-exchange short volume na di share of FINRA-reported volume wey dem mark as short; plenty of am na market makers dey sell short to fill customer buy orders — na normal plumbing, no be short interest. NVDA marked-short share bin dey between 29.3% for 2026-06-01 and 48.1% for 2026-06-30. Off-exchange reported volume reach im highest for 86.77 million shares on 2026-06-02 and im lowest for 43.31 million on 2026-06-16, across di 20 sessions wey dey on file.

QueryDi off-exchange peak and trough, bounded (deduped per session)
The exact SQL behind every number
SELECT
    toString(argMax(date, offex_m)) AS peak_date,
    max(offex_m) AS peak_offex_m,
    toString(argMin(date, offex_m)) AS trough_date,
    min(offex_m) AS trough_offex_m,
    round(max(offex_m) - min(offex_m), 2) AS peak_minus_trough_m,
    count() AS sessions_on_file
FROM (
    SELECT date, round(toFloat64(any(total_volume)) / 1e6, 2) AS offex_m
    FROM global_markets.stocks_short_volume
    WHERE ticker = 'NVDA' AND date >= toDate('2026-06-01') AND date <= toDate('2026-06-30')
    GROUP BY date
)
QueryDi mid-June short-interest print
The exact SQL behind every number
SELECT
    toString(max(settlement_date)) AS settlement,
    round(toFloat64(argMax(short_interest, settlement_date)) / 1e6, 2) AS shares_short_m,
    round(toFloat64(argMax(avg_daily_volume, settlement_date)) / 1e6, 2) AS avg_daily_volume_m,
    argMax(days_to_cover, settlement_date) AS reported_days_to_cover,
    round(toFloat64(argMax(short_interest, settlement_date)) / toFloat64(argMax(avg_daily_volume, settlement_date)), 2) AS implied_days_to_cover,
    count() AS june_settlements
FROM global_markets.stocks_short_interest
WHERE ticker = 'NVDA'
  AND settlement_date >= toDate('2026-06-01') AND settlement_date <= toDate('2026-06-30')

Actual short interest print for 2026-06-30 na: 310.13 million shares short against average day of 155.99 million shares. Di vendor report days-to-cover as 1.99; di raw ratio na 1.99 — shorts need roughly two average days to cover. Both settlements for di month dey on file (2 June prints); di figures wey dey up so carry di month-end June 30 settlement.

Data notes

QueryNVDA fundamentals census: balance sheets, income statements, cash flows
The exact SQL behind every number
SELECT
    (SELECT count() FROM global_markets.stocks_balance_sheets WHERE has(tickers, 'NVDA')) AS balance_sheet_rows,
    (SELECT count() FROM global_markets.stocks_income_statements WHERE has(tickers, 'NVDA')) AS income_statement_rows,
    (SELECT count() FROM global_markets.stocks_cash_flow_statements WHERE has(tickers, 'NVDA')) AS cash_flow_rows
Full data notes
  • Entity. NVDA na NVIDIA Corporation, CIK 0001045810, wey dey list for Nasdaq. No wahala about symbol reuse; no entity-boundary caveats dey apply. The warehouse get 83 balance-sheet rows, 152 income-statement rows, and 152 cash-flow-statement rows for NVDA — the fundamentals dey, no be like the June 29 microstructure deep-dive case.
  • Month extremes. The $235 high show for 2026-06-02 04:00 ET for pre-market (4:00 AM); the regular-hours high of $232.28 show for 2026-06-02 09:59 ET. Both the month low and regular-hours low na $189.8 — the low na regular-hours print.
  • June 19 closure. The warehouse carry 0 SPY bars for June 19 (Juneteenth), so NVDA June span 21 sessions. The holidays table no reach back to June 2026 (e start from July 3 2026); the zero-bar observation na the receipt.
  • Dollar volume. Minute close times minute volume, wey dem sum — na close-weighted proxy for notional. The regular-hours figure ($523 billion) no include pre-market and post-close bars; the full-day figure ($571.9 billion) include dem.
  • Options parsing. Expiry, type, and strike dem re-parse from the OCC ticker (the table expiration_date column no too reliable); premium notional dey assume the 100-share multiplier.
  • Per-update spread statistics dey weight each NBBO update equally; time-weighted statistics dey for the panel. Medians dey use deterministic quantiles.
  • No tick-dump guard tripped. All tick-table queries na aggregated; the tape-texture query sum the full trade table into one row.
  • Short-volume gap on June 29. The FINRA off-exchange short-volume file for June 29 cut short for the whole market (the source file end for middle of alphabet); NVDA sort before the cutoff so im row no dey. The short-volume panel carry 20 sessions; the June 29 row no dey (na market-wide file truncation cause am), no be say e zero. The June 29 deep-dive carry the market-wide probe receipt.

How We Do Am

  • We dey store timestamps for UTC and filter dem with raw UTC bounds; June 2026 na entirely EDT, so regular hours na 13:30–20:00 UTC (9:30 am–4:00 pm ET). toTimeZone dey show only for SELECT lists.
  • Session close na di last regular-hours minute bar. Dollar volume na minute close times minute volume, wey we sum up.
  • We dey re-parse options expiry, type and strike from di OCC ticker. Premium notional dey assume di 100-share multiplier.
  • Generation na batch-only through di gated read-only path; di public page no dey ever query live. Di warehouse dey keep full tick history with no rolling expiry, so dis analysis fit reproduce from di same tables any time. Warehouse state as of July 4, 2026.

Every panel na one stored object — chart, table and SQL. Carry any query further for di Strasmore terminal.

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