Strasmore Research
Market recaps wey dey break am down Matt ConnorBy Matt Connor · Updated 2026-08-02

Market Recap July 28 2026: Di Day for Numbers

July 28, 2026 recap from di tape: index scoreboard, breadth, sector spread, options flow, quote tape, rates and calendar, with every figure query-backed.

Dis market recap for Tuesday, July 28, 2026 dey read the whole session from stored queries: SPY close-over-close change na 0.24%, liquid tape advancer share na 58.6%, and options tape print 59.28 million contracts. Every window wey dey below get clear dates for both ends, so if you run any panel SQL again, e go return these same figures.

Di scoreboard

Every change na comparison between July 28 last regular-session minute bar and Monday July 27 own, for consecutive trading sessions. Rows dey alphabetical order, so every ETF dey keep the same position.

QuerySPY / QQQ / DIA / IWM: July 28 versus July 27 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 >= '2026-07-27 13:30:00' AND window_start < '2026-07-27 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 >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 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 move 1.07%, IWM 0.15%, QQQ -0.96%, and SPY 0.24% reach $740.76 close. Every row dey split the move into two legs: SPY open 0.02% from Monday close, then move 0.21% from open to close. Overnight leg and intraday leg no need agree, and how dem split between both na the session first fingerprint.

Di day unusual?

One session number no mean much if you no look the distribution wey dey behind am. So, dem rank the day inside im own trailing month with the same logic.

QuerySPY day move for trailing context (open-to-close, June 29 reach July 28)
The exact SQL behind every number
SELECT
    round(anyIf(oc_pct, d = toDate('2026-07-28')), 2) AS spy_open_to_close_pct,
    arrayCount(x -> x > abs(anyIf(oc_pct, d = toDate('2026-07-28'))), groupArrayIf(abs(oc_pct), d != toDate('2026-07-28'))) + 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('2026-06-29 13:30:00')
      AND window_start < toDateTime('2026-07-29 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
    GROUP BY d
)
Run this yourself

SPY open-to-close move of 0.21% rank 14 out of 21 trailing sessions by absolute size, for window wey reach back to 2026-06-29. The rank count how many other sessions for the window move pass am, then add one. So, first place go mean say na the biggest move for the trailing month.

Breadth

Index level na one number. Breadth dey count how many stocks move together wit am.

QueryLiquid-tape breadth: July 28 close versus July 27 close, $1M-traded filter
The exact SQL behind every number
SELECT
    countIf(c28 > c27 AND liquid) AS advancers,
    countIf(c28 < c27 AND liquid) AS decliners,
    countIf(c28 = c27 AND liquid) AS unchanged,
    countIf(liquid) AS liquid_tickers,
    countIf(NOT liquid) AS dropped_by_liquidity_filter,
    round(100.0 * countIf(c28 > c27 AND liquid) / countIf(liquid), 1) AS advancer_pct
FROM (
    SELECT ticker, c27, c28, dv28 >= 1000000 AS liquid
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-27 20:00:00') AS c27,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS c28,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-28 13:30:00') AS dv28
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-28 20:00:00'
        GROUP BY ticker
        HAVING c27 > 0 AND c28 > 0
    )
)
Run this yourself

Among 6008 names wey pass one-million-dollar regular-hours turnover bar, 3519 close above Monday close, while 2423 close below am. Advancer share na 58.6%. Filter no include 5346 thinner names for the main count, but e still count dem here instead of quietly throwing dem away.

Di mega-cap shelf

Na every session, na these same eight mega-cap names dey show here. Dem arrange am alphabetically so each one fit keep im row. The point na to use fixed basket: reader go sabi the rows, and no editor fit choose winners after everything don happen.

QueryEight mega-caps: change versus July 27 and regular-hours dollars, July 28
The exact SQL behind every number
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-28 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-28 00:00:00')) AS day_close,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-28 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 >= '2026-07-27 13:30:00' AND window_start < '2026-07-27 20:00:00')
        OR (window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 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 move 0.97%, META -0.05%, MSFT 1.12%, and NVDA 0.23% for 21.11 billion dollars regular-hours turnover, while TSLA dey at -0.57%. The dollar column dey show how much of the tape these eight names carry by themselves. The breadth panel for top dey check how far the rest of the market move together with dem.

Di movers for today

Both boards need five million dollars regular-hours turnover. Dem no dey count any name wey im split execute between the two closes wey dem dey measure. Dem also no dey count one reused symbol under the house ambiguity guard wey notes describe.

QueryBiggest gainers and decliners: July 28 close versus July 27 close, $5M+ traded, splits no dey inside
The exact SQL behind every number
SELECT ticker, board, day_pct, day_dollar_m
FROM (
    SELECT 'gainers' AS board, ticker, round((c28 / c27 - 1) * 100, 1) AS day_pct, round(dv / 1e6, 1) AS day_dollar_m
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-27 20:00:00') AS c27,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS c28,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-28 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 > '2026-07-27' AND execution_date <= '2026-07-28')
          AND window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-28 20:00:00'
        GROUP BY ticker
        HAVING c27 > 0 AND c28 > 0 AND dv >= 5000000
    )
    ORDER BY day_pct DESC
    LIMIT 8
    UNION ALL
    SELECT 'decliners' AS board, ticker, round((c28 / c27 - 1) * 100, 1) AS day_pct, round(dv / 1e6, 1) AS day_dollar_m
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-27 20:00:00') AS c27,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS c28,
               sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-28 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 > '2026-07-27' AND execution_date <= '2026-07-28')
          AND window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-28 20:00:00'
        GROUP BY ticker
        HAVING c27 > 0 AND c28 > 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 for the board, STKH, move 166.7% on 113.2 million dollars traded. The steepest decliner, YYAI, print -71% on 18.3 million. This page record the sizes and receipts; e no attach any story to dem.

Sector dispersion

The eleven SPDR select-sector funds, July 28 close compared with July 27 close, ranked from best to worst. The basket don fixed, and e no be vendor classification.

QuerySector ETFs, July 28 close versus July 27 close, rank am
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((c28 / c27 - 1) * 100, 2) AS day_pct
    FROM (
        SELECT ticker,
               argMaxIf(toFloat64(close), window_start, window_start < '2026-07-28 00:00:00') AS c27,
               argMaxIf(toFloat64(close), window_start, window_start >= '2026-07-28 00:00:00') AS c28
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
          AND ((window_start >= '2026-07-27 13:30:00' AND window_start < '2026-07-27 20:00:00')
            OR (window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00'))
        GROUP BY ticker
        HAVING c27 > 0 AND c28 > 0
    )
)
ORDER BY day_pct DESC
Run this yourself

Health Care lead the table at 2.36%, while Technology dey for bottom at -1.9%, 4.26 percentage points behind. That spread na the sector dispersion for the day. If all eleven land within one point, the tape go look very different from one wey spread across several points.

Wey dollar trades happen

QueryTop 6 by dollars traded, top 4 by shares traded: July 28 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 >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 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 >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 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 lead dollar market with 39.21 billion regular-hours turnover. QQQ follow am with 29.98 billion. Share market dey answer different question: ONFO top am with 182.4 million shares, with implied average price of $0.16. Dollar volume show where market attention dey, share volume show how much trading dey happen, and the per-name version of this measure na relative volume.

Di options tape

QueryOptions tape: contracts, call share, same-day share versus Monday, 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) = '260728', 1, 0) AS is_0dte
            FROM global_markets.options_trades
            WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 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 >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00'
    ) AS spy_regular_close,
    (
        SELECT round(toFloat64(sum(size)) / 1e6, 2)
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-27 00:00:00' AND sip_timestamp < '2026-07-28 00:00:00'
    ) AS jul27_contracts_m,
    (
        SELECT round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260727') / sum(size), 1)
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-27 00:00:00' AND sip_timestamp < '2026-07-28 00:00:00'
    ) AS jul27_pct_0dte
SELECT
    round(count() / 1e6, 2) AS option_prints_m,
    round(toFloat64(sum(size)) / 1e6, 2) AS contracts_m,
    jul27_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) = '260728') / sum(size), 1) AS pct_0dte,
    jul27_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 >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00'
Run this yourself

Di options tape record 10.07 million trades for 59.28 million contracts, compared with Monday wey get 64.69 million. Calls carry 55% of contract volume. Contracts wey expire for the same session, di zero-days-to-expiry crowd, carry 29%, compared with Monday wey get 39.3%. Expiration timing na wetin set dis rhythm. Di SPY contract wey trade pass na 742 C, with 0.72 million contracts. Its strike dey 1.24 dollars from SPY regular close of $740.76, measured as strike minus close.

The quote tape

Quote data na the scarcest dataset for this desk, and dem dey measure am every session. Even ordinary days dey enter record.

QueryStocks NBBO update count: July 28 versus July 27, with named-ticker updates (millions)
The exact SQL behind every number
SELECT
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-28')) / 1e6, 2) AS jul28_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-27')) / 1e6, 2) AS jul27_updates_m,
    round((countIf(toDate(sip_timestamp) = toDate('2026-07-28')) / countIf(toDate(sip_timestamp) = toDate('2026-07-27')) - 1) * 100, 1) AS day_over_day_pct,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-28') AND ticker = 'SPY') / 1e6, 2) AS jul28_spy_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-28') AND ticker = 'QQQ') / 1e6, 2) AS jul28_qqq_updates_m,
    round(countIf(toDate(sip_timestamp) = toDate('2026-07-28') AND ticker = 'NVDA') / 1e6, 2) AS jul28_nvda_updates_m
FROM global_markets.cache_stocks_quotes
WHERE sip_timestamp >= '2026-07-27 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00'
Run this yourself

Stock-quote tape carry 536.07 million NBBO updates, compared with 553.53 million on Monday. Na day-over-day change of -3.2%. SPY record 4.72 million updates, QQQ 5.94 million, and NVDA 3.01 million.

QuerySeven names: RTH median quoted spread for basis points, with quote-quality counts, July 28
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 >= '2026-07-28 13:30:00' AND sip_timestamp < '2026-07-28 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 regular-hours median quoted spread measure 0.27 basis points of mid. QQQ measure 0.74, while NVDA measure 1.52. The last two columns na the disclosure: dem count one-sided and crossed quotes for each name, then dem keep dem aside from the median instead of quietly removing dem. Crossed quote, where bid dey above ask, na normal artifact of consolidated feed wey dem stitch together from many venues at nanosecond resolution.

QuerySPY median spread ranked against every July session, tightest first
The exact SQL behind every number
SELECT round(anyIf(spread_bps, d = toDate('2026-07-28')), 2) AS jul28_median_spread_bps,
       arrayCount(x -> x < anyIf(spread_bps, d = toDate('2026-07-28')), groupArrayIf(spread_bps, d != toDate('2026-07-28'))) + 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 < '2026-07-28 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

When dem rank am against every July session with the same logic, the day’s SPY median spread of 0.27 basis points come out as 16 of 19, counting from the tightest, inside window wey start for 2026-07-01. For quiet tape, na this be the panel’s point: ordinary liquidity day na finding wey dem publish and define clearly.

QueryOptions NBBO tape: total updates versus stock tape, plus the SPY root slice, July 28
The exact SQL behind every number
WITH
    (SELECT count() FROM global_markets.cache_options_quotes WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00') AS jul28_options_rows,
    (SELECT count() FROM global_markets.cache_stocks_quotes WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00') AS jul28_stock_quote_rows
SELECT
    round(jul28_options_rows / 1e9, 2) AS jul28_options_bn,
    round(jul28_options_rows / jul28_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 >= '2026-07-28 13:30:00' AND sip_timestamp < '2026-07-28 20:00:00') / 1e6, 0) AS jul28_spy_options_m
Run this yourself

Options NBBO tape run 10.23 billion updates. Na 19.1 times the stock quote tape, while SPY root alone record 382 million regular-hours updates.

Rates

QueryTreasury curve prints wey dey on file, July 23 reach July 28
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 >= '2026-07-23' AND date <= '2026-07-28'
ORDER BY date
Run this yourself

Treasury file dey about one session behind the tape, so this panel dey show the prints wey e hold: 4 rows wey get date for the window. The latest one, dated 2026-07-28, put the two-year at 4.26%, the ten-year at 4.61% and the thirty-year at 5.09%, with two-to-ten-year spread of 35 basis points.

Kalenda wey dey behind the day

QueryEx-dividends, splits, listings, news, and the July 28 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 >= '2026-07-28 04:00:00' AND published_utc < '2026-07-29 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 = '2026-07-28'
    ) AS fil
SELECT
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-28') AS ex_dividend_records,
    (SELECT countIf(toFloat64(split_from) > toFloat64(split_to)) FROM global_markets.stocks_splits WHERE execution_date = '2026-07-28') AS reverse_splits,
    (SELECT countIf(toFloat64(split_to) > toFloat64(split_from)) FROM global_markets.stocks_splits WHERE execution_date = '2026-07-28') AS forward_splits,
    (SELECT count() FROM global_markets.stocks_ipos WHERE listing_date = '2026-07-28') AS listings,
    (SELECT count() FROM global_markets.stocks_news WHERE published_utc >= '2026-07-28 04:00:00' AND published_utc < '2026-07-29 04:00:00') AS news_articles,
    (SELECT uniqExact(JSONExtractString(publisher, 'name')) FROM global_markets.stocks_news WHERE published_utc >= '2026-07-28 04:00:00' AND published_utc < '2026-07-29 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

147 dividend records go ex-dividend for July 28, 3 reverse splits and 0 forward splits execute, and 2 new listings enter the tape. News feed carry 161 articles from 2 publishers, with MSFT na the ticker wey dem cover pass for this feed window, at 9 articles. EDGAR daily index get 5915 filings for the date from 2898 different filers: 856 insider Form 4 reports, 315 8-K current reports, 900 424B2 pricing supplements and 100 10-Q quarterly reports. That index dey follow im own schedule, and this panel dey report wetin e hold when dem generate am.

For the rest of the week

For the rest of the week, make you read from the same tables, while you deliberately look beyond the period.

QueryJuly 29 reach 31 for 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 >= '2026-07-29' AND date <= '2026-07-31' AND status != 'open') AS closures_rest_of_week,
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date >= '2026-07-29' AND ex_dividend_date <= '2026-07-31') AS exdiv_records_rest_of_week,
    (SELECT countIf(ticker IN ('AAPL', 'MSFT', 'JPM', 'KO', 'JNJ', 'XOM', 'CVX', 'PG', 'WMT', 'HD')) FROM global_markets.stocks_dividends WHERE ex_dividend_date >= '2026-07-29' AND ex_dividend_date <= '2026-07-31') AS household_exdivs,
    (SELECT count() FROM global_markets.stocks_splits WHERE execution_date >= '2026-07-29' AND execution_date <= '2026-07-31') AS splits_rest_of_week,
    round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260731') / sum(size), 1) AS jul31_expiry_pct_of_tue_volume,
    (SELECT toString(max(settlement_date)) FROM global_markets.stocks_short_interest WHERE settlement_date <= '2026-07-28') AS latest_short_interest_settlement
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-07-28 00:00:00' AND sip_timestamp < '2026-07-29 00:00:00'
Run this yourself

The holiday table show 0 closures from Wednesday July 29 reach Thursday July 30 and Friday July 31. 696 dividend records go ex-dividend across those three sessions. 0 of them dey among the ten household names we check. 15 splits dey scheduled to execute. From Tuesday option volume, 17.5% already dey inside contracts wey go expire on Friday, July 31. The latest short-interest settlement wey dey on file na 2026-07-15. The file dey publish with enough delay to get one explainer for itself.

Session wey dem don verify

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 >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS regular_session_bars,
    uniqExactIf(toDate(toTimeZone(window_start, 'America/New_York')), window_start >= '2026-07-28 13:30:00' AND window_start < '2026-07-28 20:00:00') AS day_sessions,
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date = '2026-07-28') AS jul28_holiday_rows,
    (SELECT toString(min(date)) FROM global_markets.stocks_market_holidays WHERE date > '2026-07-28' AND date <= '2026-12-31' AND status = 'closed') AS next_closure_date,
    (SELECT argMin(name, date) FROM global_markets.stocks_market_holidays WHERE date > '2026-07-28' 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 >= '2026-07-28 00:00:00' AND window_start < '2026-07-29 00:00:00'
Run this yourself

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

FAQ

How stock market do on Tuesday, July 28, 2026?

SPY change 0.24% close over close reach $740.76, while QQQ dey -0.96%, DIA dey 1.07% and IWM dey 0.15%. Among liquid names, 3519 rise and 2423 fall.

Which sector top the board on July 28, 2026?

Health Care, at 2.36%, based on the eleven SPDR select-sector funds. The weakest among the eleven, Technology, print -1.9%.

How busy options market be on July 28, 2026?

59.28 million contracts trade, compared with 64.69 million for the previous session. Same-day contracts make up 29% of the volume, while calls make up 55%.

Which stock trade the highest dollar volume on July 28, 2026?

MU, with 39.21 billion regular-hours dollar volume, ahead of QQQ with 29.98 billion.

Data notes

This edition dey fill the July 28 gap for the daily series: the July 27 daily cover the session before am, the July 29 daily cover the session after am, and the weekly recap carry the week before both. Dem arrange the named per-ticker panels alphabetically so prose references go point to fixed rows. Dem arrange leaderboards and mover boards by value, and every positional claim wey dem carry get encoded as a sanity bound. The mega-cap basket and the eleven-fund sector basket na declared, fixed sets; dem no be vendor classifications. The mover boards use five-million-dollar regular-hours turnover bar. Dem exclude any name wey split execute between the two closes wey dem measure. Dem also exclude one reused symbol under the house ambiguity guard, so any callout go always lead to a name wey person fit verify. The quote panels count one-sided and crossed quotes for each name instead of silently dropping dem. Treasury file and the EDGAR daily index dey arrive on their own schedules, so those panels report wetin dem get instead of assuming say dem don arrive. No implied-volatility index dey here. Those series no get license enter this warehouse, so volatility dey come from the tape through ranges, same-day options share and quote behavior.

Methodology

  • Market data source: consolidated tape. delayed_stocks_minute_aggs na for prices and volumes, options_trades na for the options tape, while cache_stocks_quotes and cache_options_quotes na for the NBBO panels.
  • Close: na the last regular-session minute bar, never be assumed 16:00 print and never extended-hours print.
  • Time zone handling: all stored timestamps dey UTC; WHERE clauses dey use raw UTC literals, and toTimeZone dey appear only for SELECT lists wey get ET labels.
  • Session verification: e come from holiday table plus bars wey dem observe, never from calendar assumption.
  • Prior-session comparisons: dem calculate am inside query from July 27, never carry am over from previous post.
  • Decimals: dem cast price, size and volume columns to Float64 before any division or product.
  • Deterministic aggregates: exact quantiles and tuple-keyed tie-breaks dey apply throughout; every ordering or sign claim for prose get sanity bound wey encode am.
  • Warehouse as-of date: August 1, 2026, four days after the session, and e don pass tape usual one-to-two-day ingest lag; the bounded receipts above go hold the post if dem find say dataset dey miss when dem generate am.

Cross-links: the July 27 daily recap, the July 29 daily recap, when options expire, wetin bid-ask spread be, and the two-to-ten-year spread.

Every query above dey run unchanged for the Strasmore terminal if you wan point one window again to another session.

#recap#daily#spy#qqq#market structure