Strasmore Research
Market Recap Matt ConnorBy Matt Connor · Updated 2026-08-02

Market Recap: July 30, 2026, The Day in Numbers

July 30, 2026, measured from the tape: index scoreboard, breadth, sector spread, options flow, the quote tape, rates and calendar, every figure query-backed.

This market recap for Thursday, July 30, 2026 reads the whole session from stored queries: SPY's close-over-close change came to 1.68%, the liquid tape's advancer share to 63.5%, and the options tape printed 67.06 million contracts. Every window below is pinned to explicit dates on both ends, so re-running any panel's SQL returns these same figures.

The scoreboard

Every change compares July 30's last regular-session minute bar with Wednesday July 29's, consecutive trading sessions. Rows are alphabetical, so each ETF keeps a fixed position.

QuerySPY / QQQ / DIA / IWM: July 30 vs the July 29 close, regular hours
The exact SQL behind every number
WITH prior AS (
    SELECT ticker, argMax(close, window_start) AS prior_close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
      AND window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00'
    GROUP BY ticker
),
sess AS (
    SELECT ticker,
           argMin(open, window_start) AS day_open,
           argMax(close, window_start) AS day_close,
           max(high) AS day_high,
           min(low) AS day_low,
           round(toFloat64(sum(volume)) / 1e6, 1) AS shares_traded_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
      AND window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'
    GROUP BY ticker
)
SELECT
    s.ticker AS ticker,
    round(toFloat64(p.prior_close), 2) AS prior_close,
    round(toFloat64(s.day_open), 2) AS day_open,
    round(toFloat64(s.day_close), 2) AS day_close,
    round((toFloat64(s.day_open) / toFloat64(p.prior_close) - 1) * 100, 2) AS gap_pct,
    round((toFloat64(s.day_close) / toFloat64(s.day_open) - 1) * 100, 2) AS intraday_pct,
    round((toFloat64(s.day_close) / toFloat64(p.prior_close) - 1) * 100, 2) AS pct_change,
    round(toFloat64(s.day_high), 2) AS day_high,
    round(toFloat64(s.day_low), 2) AS day_low,
    s.shares_traded_m
FROM sess s LEFT JOIN prior p ON s.ticker = p.ticker
ORDER BY ticker
Run this yourself

DIA moved 1.19%, IWM 1.37%, QQQ 3.34%, and SPY 1.68% to a $741.73 close. Each row splits the move into its two legs: SPY opened 0.9% from Wednesday's close and moved 0.77% open to close. The overnight leg and the intraday leg do not have to agree, and the split between them is a session's first fingerprint.

Was the day unusual?

One session's number means little without the distribution behind it, so the day is ranked inside its own trailing month on identical logic.

QuerySPY day move in trailing context (open-to-close, June 30 through July 30)
The exact SQL behind every number
SELECT
    round(anyIf(oc_pct, d = toDate('2026-07-30')), 2) AS spy_open_to_close_pct,
    arrayCount(x -> x > abs(anyIf(oc_pct, d = toDate('2026-07-30'))), groupArrayIf(abs(oc_pct), d != toDate('2026-07-30'))) + 1 AS spy_abs_move_rank,
    count() AS spy_sessions_compared,
    toString(min(d)) AS first_session
FROM (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           (argMax(toFloat64(close), window_start) / argMin(toFloat64(open), window_start) - 1) * 100 AS oc_pct
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND window_start >= toDateTime('2026-06-30 13:30:00')
      AND window_start < toDateTime('2026-07-31 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
    GROUP BY d
)
Run this yourself

SPY's open-to-close move of 0.77% ranked 3 of 22 trailing sessions by absolute size, in a window reaching back to 2026-06-30. The rank counts how many other sessions in the window moved more, plus one, so first place would be the biggest move of the trailing month.

Breadth

An index level is one number. Breadth counts how many stocks travelled with it.

QueryLiquid-tape breadth: July 30 close vs July 29 close, $1M-traded filter
The exact SQL behind every number
SELECT
    countIf(c30 > c29 AND liquid) AS advancers,
    countIf(c30 < c29 AND liquid) AS decliners,
    countIf(c30 = c29 AND liquid) AS unchanged,
    countIf(liquid) AS liquid_tickers,
    countIf(NOT liquid) AS dropped_by_liquidity_filter,
    round(100.0 * countIf(c30 > c29 AND liquid) / countIf(liquid), 1) AS advancer_pct
FROM (
    SELECT ticker, c29, c30, dv30 >= 1000000 AS liquid
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS c29,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00') AS c30,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-30 13:30:00') AS dv30
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-30 20:00:00'
        GROUP BY ticker
        HAVING c29 > 0 AND c30 > 0
    )
)
Run this yourself

Across 5960 names clearing a one-million-dollar regular-hours turnover bar, 3782 closed above Wednesday's close and 2122 closed below, an advancer share of 63.5%. The filter set aside 5492 thinner names, counted here rather than quietly discarded.

The mega-cap shelf

The same eight mega-cap names appear here every session, alphabetical so each keeps its row. A fixed basket is the point: the reader learns the rows, and no editorial hand picks winners after the fact.

QueryEight mega-caps: change vs July 29 and regular-hours dollars, July 30
The exact SQL behind every number
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-30 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-30 00:00:00')) AS day_close,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-30 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL', 'AMZN', 'AVGO', 'GOOGL', 'META', 'MSFT', 'NVDA', 'TSLA')
      AND ((window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00')
        OR (window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(prior_close, 2) AS prior_close,
    round(day_close, 2) AS day_close,
    round((day_close / prior_close - 1) * 100, 2) AS pct_chg,
    day_dollar_bn
FROM per_name
ORDER BY ticker
Run this yourself

AAPL moved -1.24%, META -8.17%, MSFT 15.06%, and NVDA 2.67% on 19.63 billion dollars of regular-hours turnover, with TSLA at 3.53%. The dollar column shows how much of the tape these eight names carry on their own; the breadth panel above is the check on how far the rest of the market travelled with them.

The day's movers

Both boards require five million dollars of regular-hours turnover, exclude any name whose split executed between the two closes they measure, and exclude one reused symbol under the house ambiguity guard described in the notes.

QueryBiggest gainers and decliners: July 30 close vs July 29 close, $5M+ traded, splits excluded
The exact SQL behind every number
SELECT ticker, board, day_pct, day_dollar_m
FROM (
    SELECT 'gainers' AS board, ticker, round((c30 / c29 - 1) * 100, 1) AS day_pct, round(dv / 1e6, 1) AS day_dollar_m
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS c29,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00') AS c30,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-30 13:30:00') AS dv
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker NOT IN ('SPCX')
          AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits WHERE execution_date > '2026-07-29' AND execution_date <= '2026-07-30')
          AND window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-30 20:00:00'
        GROUP BY ticker
        HAVING c29 > 0 AND c30 > 0 AND dv >= 5000000
    )
    ORDER BY day_pct DESC
    LIMIT 8
    UNION ALL
    SELECT 'decliners' AS board, ticker, round((c30 / c29 - 1) * 100, 1) AS day_pct, round(dv / 1e6, 1) AS day_dollar_m
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00') AS c29,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00') AS c30,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-30 13:30:00') AS dv
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker NOT IN ('SPCX')
          AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits WHERE execution_date > '2026-07-29' AND execution_date <= '2026-07-30')
          AND window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-30 20:00:00'
        GROUP BY ticker
        HAVING c29 > 0 AND c30 > 0 AND dv >= 5000000
    )
    ORDER BY day_pct ASC
    LIMIT 8
)
ORDER BY board DESC, abs(day_pct) DESC
Run this yourself

The top gainer on the board, CYCU, moved 489.5% on 474.4 million dollars traded. The steepest decliner, IREZ, printed -61.2% on 90.8 million. This page records the sizes and the receipts; it attaches no story to them.

Sector dispersion

The eleven SPDR select-sector funds, July 30's close over July 29's, ranked best to worst. The basket is declared and fixed, not a vendor classification.

QuerySector ETFs, July 30 close vs July 29 close, ranked
The exact SQL behind every number
SELECT sector, day_pct, round(max(day_pct) OVER () - day_pct, 2) AS points_behind_best
FROM (
    SELECT multiIf(ticker = 'XLK', 'Technology', ticker = 'XLC', 'Communications', ticker = 'XLE', 'Energy',
                   ticker = 'XLF', 'Financials', ticker = 'XLI', 'Industrials', ticker = 'XLB', 'Materials',
                   ticker = 'XLP', 'Staples', ticker = 'XLRE', 'Real Estate', ticker = 'XLU', 'Utilities',
                   ticker = 'XLV', 'Health Care', 'Consumer Discretionary') AS sector,
           round((c30 / c29 - 1) * 100, 2) AS day_pct
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start < '2026-07-30 00:00:00') AS c29,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-30 00:00:00') AS c30
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
          AND ((window_start >= '2026-07-29 13:30:00' AND window_start < '2026-07-29 20:00:00')
            OR (window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'))
        GROUP BY ticker
        HAVING c29 > 0 AND c30 > 0
    )
)
ORDER BY day_pct DESC
Run this yourself

Technology topped the board at 5.52%, and Communications sat at the bottom at -2.69%, 8.21 percentage points behind. That spread is the day's sector dispersion: a tape where all eleven land within a point reads very differently from one stretched across several.

Where the dollars traded

QueryTop 6 by dollars traded, top 4 by shares traded: July 30 regular hours
The exact SQL behind every number
SELECT leaderboard, ticker, dollar_volume_bn, shares_m,
    round(1000 * dollar_volume_bn / shares_m, 2) AS implied_avg_price
FROM (
    SELECT
        'by dollars traded' AS leaderboard,
        ticker,
        round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 2) AS dollar_volume_bn,
        round(sum(toFloat64(volume)) / 1e6, 1) AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
    ORDER BY dollar_volume_bn DESC
    LIMIT 6
    UNION ALL
    SELECT
        'by shares traded' AS leaderboard,
        ticker,
        round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 2) AS dollar_volume_bn,
        round(sum(toFloat64(volume)) / 1e6, 1) AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
    ORDER BY shares_m DESC
    LIMIT 4
)
ORDER BY leaderboard, if(leaderboard = 'by shares traded', shares_m, dollar_volume_bn) DESC
Run this yourself

MU led the dollar board at 43.29 billion of regular-hours turnover, with MSFT at 42.56 billion behind it. The share board answers a different question: CYCU topped it at 544 million shares with an implied average price of $0.86. Dollar volume finds the market's attention, share volume finds its churn, and the per-name version of this measure is relative volume.

The options tape

QueryOptions tape: contracts, call share, same-day share vs Wednesday, busiest SPY contract
The exact SQL behind every number
WITH
    (
        SELECT (strike, typ, vol_m, is_0dte)
        FROM (
            SELECT toFloat64(any(strike_price)) AS strike, any(option_type) AS typ,
                   round(toFloat64(sum(size)) / 1e6, 2) AS vol_m,
                   if(substring(ticker, length(ticker) - 14, 6) = '260730', 1, 0) AS is_0dte
            FROM global_markets.options_trades
            WHERE sip_timestamp >= '2026-07-30 00:00:00' AND sip_timestamp < '2026-07-31 00:00:00'
              AND underlying_symbol = 'SPY'
            GROUP BY ticker
            ORDER BY vol_m DESC, strike ASC
            LIMIT 1
        )
    ) AS top_spy,
    (
        SELECT round(toFloat64(argMax(close, window_start)), 2)
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY' AND window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00'
    ) AS spy_regular_close,
    (
        SELECT round(toFloat64(sum(size)) / 1e6, 2)
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00'
    ) AS jul29_contracts_m,
    (
        SELECT round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260729') / sum(size), 1)
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-30 00:00:00'
    ) AS jul29_pct_0dte
SELECT
    round(count() / 1e6, 2) AS option_prints_m,
    round(toFloat64(sum(size)) / 1e6, 2) AS contracts_m,
    jul29_contracts_m,
    round(100.0 * sumIf(size, option_type = 'C') / sum(size), 1) AS call_pct_of_volume,
    round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260730') / sum(size), 1) AS pct_0dte,
    jul29_pct_0dte,
    spy_regular_close,
    top_spy.1 AS top_spy_strike,
    top_spy.2 AS top_spy_type,
    top_spy.3 AS top_spy_contracts_m,
    top_spy.4 AS top_spy_is_0dte,
    round(top_spy.1 - spy_regular_close, 2) AS top_spy_strike_minus_close
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-07-30 00:00:00' AND sip_timestamp < '2026-07-31 00:00:00'
Run this yourself

The options tape printed 10.93 million trades for 67.06 million contracts, beside Wednesday's 66.84 million. Calls took 54.6% of contract volume. Contracts expiring the same session, the zero-days-to-expiry crowd, took 25.3% against Wednesday's 33.1%, a rhythm set by expiration timing. The busiest SPY contract was the 740 C at 0.56 million contracts, its strike sitting -1.73 dollars from SPY's $741.73 regular close, measured strike minus close.

The quote tape

The quote data is this desk's scarcest dataset, and it is measured every session. Ordinary days go on the record too.

QueryStocks NBBO update count: July 30 vs July 29, with named-ticker updates (millions)
The exact SQL behind every number
SELECT
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-30')) / 1e6, 2) AS jul30_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-29')) / 1e6, 2) AS jul29_updates_m,
    round((countIf(toDate(sip_timestamp) = toDate('2026-07-30')) / countIf(toDate(sip_timestamp) = toDate('2026-07-29')) - 1) * 100, 1) AS day_over_day_pct,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-30') AND ticker = 'SPY') / 1e6, 2) AS jul30_spy_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-30') AND ticker = 'QQQ') / 1e6, 2) AS jul30_qqq_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-30') AND ticker = 'NVDA') / 1e6, 2) AS jul30_nvda_updates_m
FROM global_markets.cache_stocks_quotes
WHERE sip_timestamp >= '2026-07-29 00:00:00' AND sip_timestamp < '2026-07-31 00:00:00'
Run this yourself

The stock-quote tape carried 560.75 million NBBO updates against 722.65 million on Wednesday, a day-over-day change of -22.4%. SPY logged 5.55 million updates, QQQ 6.75 million, NVDA 2.39 million.

QuerySeven names: RTH median quoted spread in basis points, with quote-quality counts, July 30
The exact SQL behind every number
SELECT ticker,
       round(quantileExactIf(0.5)(10000 * (toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2), bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price), 2) AS median_spread_bps,
       round(count() / 1e6, 2) AS quote_updates_m,
       countIf(bid_price <= 0 OR ask_price <= 0) AS one_sided_quote_count,
       countIf(bid_price > ask_price AND bid_price > 0 AND ask_price > 0) AS crossed_quote_count
FROM global_markets.cache_stocks_quotes
WHERE ticker IN ('AAPL', 'DIA', 'IWM', 'NVDA', 'QQQ', 'SPY', 'TSLA')
  AND sip_timestamp >= '2026-07-30 13:30:00' AND sip_timestamp < '2026-07-30 20:00:00'
GROUP BY ticker
HAVING countIf(bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price) > 0
ORDER BY ticker
Run this yourself

SPY's regular-hours median quoted spread measured 0.27 basis points of mid, QQQ's 0.59 and NVDA's 1.03. The last two columns are the disclosure: one-sided and crossed quotes are counted per name and set aside from the median rather than silently dropped. A crossed quote, bid above ask, is an ordinary artifact of a consolidated feed stitched from many venues at nanosecond resolution.

QuerySPY's median spread ranked against every July session, tightest first
The exact SQL behind every number
SELECT round(anyIf(spread_bps, d = toDate('2026-07-30')), 2) AS jul30_median_spread_bps,
       arrayCount(x -> x < anyIf(spread_bps, d = toDate('2026-07-30')), groupArrayIf(spread_bps, d != toDate('2026-07-30'))) + 1 AS rank_tightest,
       count() AS sessions_compared,
       toString(min(d)) AS first_session
FROM (
    SELECT toDate(sip_timestamp) AS d,
           quantileExactIf(0.5)(10000 * (toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2), bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price) AS spread_bps
    FROM global_markets.cache_stocks_quotes
    WHERE ticker = 'SPY'
      AND sip_timestamp >= '2026-07-01 13:30:00' AND sip_timestamp < '2026-07-30 20:00:00'
      AND (toHour(sip_timestamp) * 60 + toMinute(sip_timestamp)) BETWEEN 810 AND 1199
    GROUP BY d
    HAVING countIf(bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price) > 0
)
Run this yourself

Ranked against every July session on identical logic, the day's SPY median spread of 0.27 basis points came in 19 of 21, counted from the tightest, in a window starting 2026-07-01. On a quiet tape that sentence is the point of the panel: an ordinary day for liquidity is a finding, published and bounded.

QueryOptions NBBO tape: total updates vs the stock tape, plus the SPY root slice, July 30
The exact SQL behind every number
WITH
    (SELECT count() FROM global_markets.cache_options_quotes WHERE sip_timestamp >= '2026-07-30 00:00:00' AND sip_timestamp < '2026-07-31 00:00:00') AS jul30_options_rows,
    (SELECT count() FROM global_markets.cache_stocks_quotes WHERE sip_timestamp >= '2026-07-30 00:00:00' AND sip_timestamp < '2026-07-31 00:00:00') AS jul30_stock_quote_rows
SELECT
    round(jul30_options_rows / 1e9, 2) AS jul30_options_bn,
    round(jul30_options_rows / jul30_stock_quote_rows, 1) AS options_to_stock_ratio,
    round((SELECT count() FROM global_markets.cache_options_quotes WHERE ticker >= 'O:SPY26' AND ticker < 'O:SPY27' AND sip_timestamp >= '2026-07-30 13:30:00' AND sip_timestamp < '2026-07-30 20:00:00') / 1e6, 0) AS jul30_spy_options_m
Run this yourself

The options NBBO tape ran 10.32 billion updates, 18.4 times the stock quote tape, with the SPY root alone at 441 million regular-hours updates.

Rates

QueryTreasury curve prints on file, July 27 through July 30
The exact SQL behind every number
SELECT toString(date) AS date,
       round(toFloat64(yield_2_year), 2) AS yield_2y_pct,
       round(toFloat64(yield_10_year), 2) AS yield_10y_pct,
       round(toFloat64(yield_30_year), 2) AS yield_30y_pct,
       round((toFloat64(yield_10_year) - toFloat64(yield_2_year)) * 100) AS spread_2s10s_bp
FROM global_markets.treasury_yields
WHERE date >= '2026-07-27' AND date <= '2026-07-30'
ORDER BY date
Run this yourself

Treasury's file runs about a session behind the tape, so this panel reports the prints it holds: 4 dated rows in the window. The latest, dated 2026-07-30, put the two-year at 4.23%, the ten-year at 4.68% and the thirty-year at 5.21%, a two-to-ten-year spread of 45 basis points.

The calendar behind the day

QueryEx-dividends, splits, listings, news, and the July 30 SEC filing mix
The exact SQL behind every number
WITH
    (
        SELECT (argMax(t, (n, t)), max(n))
        FROM (
            SELECT t, count() AS n
            FROM (
                SELECT arrayJoin(tickers) AS t
                FROM global_markets.stocks_news
                WHERE published_utc >= '2026-07-30 04:00:00' AND published_utc < '2026-07-31 04:00:00'
            )
            WHERE t != 'SPCX'
            GROUP BY t
        )
    ) AS top_news,
    (
        SELECT (count(), uniqExact(cik), countIf(form_type = '4'), countIf(form_type = '8-K'), countIf(form_type = '424B2'), countIf(form_type = '10-Q'))
        FROM global_markets.stocks_sec_edgar_index
        WHERE filing_date = '2026-07-30'
    ) AS fil
SELECT
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-30') AS ex_dividend_records,
    (SELECT countIf(toFloat64(split_from) > toFloat64(split_to)) FROM global_markets.stocks_splits WHERE execution_date = '2026-07-30') AS reverse_splits,
    (SELECT countIf(toFloat64(split_to) > toFloat64(split_from)) FROM global_markets.stocks_splits WHERE execution_date = '2026-07-30') AS forward_splits,
    (SELECT count() FROM global_markets.stocks_ipos WHERE listing_date = '2026-07-30') AS listings,
    (SELECT count() FROM global_markets.stocks_news WHERE published_utc >= '2026-07-30 04:00:00' AND published_utc < '2026-07-31 04:00:00') AS news_articles,
    (SELECT uniqExact(JSONExtractString(publisher, 'name')) FROM global_markets.stocks_news WHERE published_utc >= '2026-07-30 04:00:00' AND published_utc < '2026-07-31 04:00:00') AS news_publishers,
    top_news.1 AS top_news_ticker,
    top_news.2 AS top_news_n,
    fil.1 AS fil_total,
    fil.2 AS fil_filers,
    fil.3 AS fil_form4,
    fil.4 AS fil_8k,
    fil.5 AS fil_424b2,
    fil.6 AS fil_10q
Run this yourself

200 dividend records went ex-dividend on July 30, 3 reverse and 3 forward splits executed, and 3 new listings hit the tape. The news feed carried 173 articles from 2 publishers, with NVDA the most-covered ticker in this one feed's window at 11 articles. The EDGAR daily index holds 5544 filings for the date from 2743 distinct filers: 745 insider Form 4 reports, 460 8-K current reports, 1426 424B2 pricing supplements and 252 10-Q quarterly reports. That index lands on its own schedule, and this panel reports whatever it holds at generation time.

On deck

The next two sessions, read from the same tables, deliberately looking past the period. Friday, July 31 is the last trading date of the month on the calendar.

QueryJuly 31 and August 3 on the calendar: closures, ex-dividends, splits, the Friday expiry, and the short-interest lag
The exact SQL behind every number
SELECT
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date >= '2026-07-31' AND date <= '2026-08-03' AND status != 'open') AS closures_next_two_sessions,
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date >= '2026-07-31' AND ex_dividend_date <= '2026-08-03') AS exdiv_records_next_two_sessions,
    (SELECT countIf(ticker IN ('AAPL', 'MSFT', 'JPM', 'KO', 'JNJ', 'XOM', 'CVX', 'PG', 'WMT', 'HD')) FROM global_markets.stocks_dividends WHERE ex_dividend_date >= '2026-07-31' AND ex_dividend_date <= '2026-08-03') AS household_exdivs,
    (SELECT count() FROM global_markets.stocks_splits WHERE execution_date >= '2026-07-31' AND execution_date <= '2026-08-03') AS splits_next_two_sessions,
    round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260731') / sum(size), 1) AS jul31_expiry_pct_of_thu_volume,
    (SELECT toString(max(settlement_date)) FROM global_markets.stocks_short_interest WHERE settlement_date <= '2026-07-30') AS latest_short_interest_settlement
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-07-30 00:00:00' AND sip_timestamp < '2026-07-31 00:00:00'
Run this yourself

The holiday table shows 0 closures across Friday July 31 and Monday August 3. 544 dividend records go ex-dividend across those two sessions, 0 of them among ten checked household names, and 14 splits are scheduled to execute. Of Thursday's option volume, 26.7% already sat in contracts dated to expire Friday, July 31. The newest short-interest settlement on file is 2026-07-15, a file that publishes on a lag long enough to have its own explainer.

The session, verified

QuerySession verification: first/last SPY bar ET, regular-bar count, holiday receipts, next closure
The exact SQL behind every number
SELECT
    formatDateTime(min(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS first_spy_bar_et,
    formatDateTime(max(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS last_spy_bar_et,
    countIf(window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00') AS regular_session_bars,
    uniqExactIf(toDate(toTimeZone(window_start, 'America/New_York')), window_start >= '2026-07-30 13:30:00' AND window_start < '2026-07-30 20:00:00') AS day_sessions,
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date = '2026-07-30') AS jul30_holiday_rows,
    (SELECT toString(min(date)) FROM global_markets.stocks_market_holidays WHERE date > '2026-07-30' AND date <= '2026-12-31' AND status = 'closed') AS next_closure_date,
    (SELECT argMin(name, date) FROM global_markets.stocks_market_holidays WHERE date > '2026-07-30' AND date <= '2026-12-31' AND status = 'closed') AS next_closure_name
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY' AND window_start >= '2026-07-30 00:00:00' AND window_start < '2026-07-31 00:00:00'
Run this yourself

A full regular session: first SPY bar 04:00 ET, last 19:59 ET, 390 regular-hours bars, 1 session in the window, and 0 holiday rows for the date. The next scheduled closure is Labor Day on 2026-09-07.

FAQ

How did the stock market do on Thursday, July 30, 2026?

SPY changed 1.68% close over close to $741.73, with QQQ at 3.34%, DIA at 1.19% and IWM at 1.37%. Among liquid names, 3782 rose and 2122 fell.

Which sector topped the board on July 30, 2026?

Technology, at 5.52%, measured across the eleven SPDR select-sector funds. The weakest of the eleven, Communications, printed -2.69%.

How busy was the options market on July 30, 2026?

67.06 million contracts traded, beside 66.84 million the prior session. Same-day contracts took 25.3% of volume and calls took 54.6%.

Which stock traded the most dollars on July 30, 2026?

MU, at 43.29 billion of regular-hours dollar volume, ahead of MSFT at 42.56 billion.

Data notes

The previous daily edition is July 29, 2026, and the weekly recap carries the week before this one. Named per-ticker panels are ordered alphabetically so prose references point at fixed rows; leaderboards and mover boards are value-ordered, and every positional claim they carry is encoded as a sanity bound. The mega-cap basket and the eleven-fund sector basket are declared, fixed sets, not vendor classifications. The mover boards apply a five-million-dollar regular-hours turnover bar, exclude any name whose split executed between the two closes they measure, and exclude one reused symbol under the house ambiguity guard, so a callout always falls through to a verifiable name. The quote panels count one-sided and crossed quotes per name instead of dropping them silently. Treasury's file and the EDGAR daily index land on their own schedules, so those panels report what they hold rather than assuming arrival. No implied-volatility index appears here: those series are not licensed into this warehouse, so volatility is read off the tape through ranges, same-day options share and quote behavior.

Methodology

  • Market data source: consolidated tape. delayed_stocks_minute_aggs for prices and volumes, options_trades for the options tape, cache_stocks_quotes and cache_options_quotes for the NBBO panels.
  • Close: the last regular-session minute bar, never an assumed 16:00 print and never an extended-hours print.
  • Time zone handling: all stored timestamps are UTC; WHERE clauses use raw UTC literals, and toTimeZone appears only in SELECT lists for ET labels.
  • Session verification: from the holiday table plus observed bars, never assumed from the calendar.
  • Prior-session comparisons: computed in-query from July 29, never carried over from a previous post.
  • Decimals: price, size and volume columns are cast to Float64 before any division or product.
  • Deterministic aggregates: exact quantiles and tuple-keyed tie-breaks throughout; every ordering or sign claim in prose is encoded as a sanity bound.
  • Warehouse as-of date: August 1, 2026, two days after the session, at the edge of the tape's usual one-to-two-day ingest lag; the bounded receipts above hold the post if a dataset is found missing at generation.

Cross-links: the previous daily recap, the weekly recap, when options expire, what a bid-ask spread is, and the two-to-ten-year spread.

Every query above runs unchanged on the Strasmore terminal if you want to re-point a window at a different session.

#recap#daily#spy#qqq#market structure