Strasmore Research
市场回顾 Matt Connor作者： Matt Connor · 更新于 2026-07-23

2026年6月29日市场行情回顾

回顾2026年6月29日市场表现：指数在增长驱动下走高，板块分化显著，涵盖0DTE及微观结构数据。

2026年6月29日，市场在增长驱动下走高，但领涨板块范围较窄：QQQ上涨 2.57%，SPY上涨 1.62%，3968 活跃股走高，而 2327 股票下跌——尽管如此，近半数行业板块收盘下跌。所有数据均来自存储查询；如需查看 SQL，请展开相应面板。当日逐笔交易数据详情请见：6月29日微观结构深度解析。

市场表现

所有变动均基于 6 月 29 日常规交易时段最后一分钟的 K 线与 6 月 26 日（周五）进行对比。

查询SPY / QQQ / DIA / IWM — 6月29日对比6月26日收盘（常规交易时段）
每个数字背后的完整 SQL
WITH friday AS (
    SELECT ticker, argMax(close, window_start) AS friday_close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
      AND window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00'
    GROUP BY ticker
),
monday AS (
    SELECT ticker,
           argMin(open, window_start) AS monday_open,
           argMax(close, window_start) AS monday_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 >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'
    GROUP BY ticker
)
SELECT
    m.ticker AS ticker,
    toFloat64(f.friday_close) AS jun26_close,
    toFloat64(m.monday_open) AS jun29_open,
    toFloat64(m.monday_close) AS jun29_close,
    round((toFloat64(m.monday_close) / toFloat64(f.friday_close) - 1) * 100, 2) AS pct_change,
    round(100 * (toFloat64(m.monday_close) / toFloat64(f.friday_close) - 1) / max(toFloat64(m.monday_close) / toFloat64(f.friday_close) - 1) OVER (), 1) AS pct_of_best_change,
    toFloat64(m.day_high) AS day_high,
    toFloat64(m.day_low) AS day_low,
    m.shares_traded_m AS shares_traded_m
FROM monday m
JOIN friday f ON m.ticker = f.ticker
ORDER BY m.ticker

SPY 开盘价为 $736.525（周五收盘价为 $729.09），收盘价为 $740.88，低于当日 $741.56 的高点。QQQ 上涨 2.57，而 DIA 上涨 0.81，显示市场呈现增长与科技股行情；小盘股 IWM 上涨了 0.45%。

当日走势是否异常？

若缺乏参照标准，百分比变动的意义有限。本面板根据收盘价的绝对变动幅度，将 6 月 29 日的表现与过去一个月进行对比。

查询QQQ 与 SPY：6月29日对比过去一个月交易日排名（排名1为绝对涨跌幅最大）
每个数字背后的完整 SQL
SELECT
    round(anyIf(cc_pct, ticker = 'QQQ' AND d = toDate('2026-06-29')), 2) AS qqq_close_over_close_pct,
    arrayCount(x -> x > abs(anyIf(cc_pct, ticker = 'QQQ' AND d = toDate('2026-06-29'))), groupArrayIf(abs(cc_pct), ticker = 'QQQ' AND d != toDate('2026-06-29'))) + 1 AS qqq_abs_move_rank,
    countIf(ticker = 'QQQ') AS qqq_sessions_compared,
    round(max(if(ticker = 'QQQ', abs(cc_pct), 0)), 2) AS qqq_biggest_move_of_month_pct,
    countIf(ticker = 'QQQ' AND cc_pct > 0) AS qqq_up_sessions,
    round(anyIf(cc_pct, ticker = 'SPY' AND d = toDate('2026-06-29')), 2) AS spy_close_over_close_pct,
    arrayCount(x -> x > abs(anyIf(cc_pct, ticker = 'SPY' AND d = toDate('2026-06-29'))), groupArrayIf(abs(cc_pct), ticker = 'SPY' AND d != toDate('2026-06-29'))) + 1 AS spy_abs_move_rank,
    countIf(ticker = 'SPY') AS spy_sessions_compared,
    round(anyIf(oc_pct, ticker = 'SPY' AND d = toDate('2026-06-29')), 2) AS spy_open_to_close_pct,
    concat(monthName(min(d)), ' ', toString(toDayOfMonth(min(d))), ', ', toString(toYear(min(d)))) AS first_session
FROM (
    SELECT ticker, d,
           (close_px / lagInFrame(close_px) OVER (PARTITION BY ticker ORDER BY d) - 1) * 100 AS cc_pct,
           oc_pct
    FROM (
        SELECT ticker,
               toDate(toTimeZone(window_start, 'America/New_York')) AS d,
               argMax(toFloat64(close), window_start) AS close_px,
               (argMax(toFloat64(close), window_start) / argMin(toFloat64(open), window_start) - 1) * 100 AS oc_pct
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker IN ('SPY', 'QQQ')
          AND window_start >= toDateTime('2026-05-28 00:00:00')
          AND window_start < toDateTime('2026-06-30 00:00:00')
          AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
        GROUP BY ticker, d
    )
)
WHERE isFinite(cc_pct)

波动剧烈但非纪录：QQQ 的 2.57% 涨幅在 May 29, 2026 以来 21 个交易日中排名 5；该月最大单日波动为 4.76%。SPY 的 1.62% 涨幅在 21 个交易日中排名 4。涨幅来源至关重要：SPY 的日内涨幅仅为 0.59%，因此大部分涨幅来自于常规交易时段开始前的隔夜跳空。在过去一个月中，QQQ 有 10 的交易日收高，涨跌概率各占一半。

市场宽度：涨幅覆盖面如何？

在成交额至少为 100 万美元的股票中，若周一收盘价高于周五收盘价，则视为上涨个股。该筛选条件剔除了 11,475 个交易日内波动不大的个股，占比为 5,108。

查询6月29日成交额至少 $1M 的个股：上涨 vs 下跌数量
每个数字背后的完整 SQL
WITH per_ticker AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')) AS friday_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')) AS monday_close,
        sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-06-29 00:00:00') AS monday_dollar_volume
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE (window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
       OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00')
    GROUP BY ticker
)
SELECT
    countIf(monday_close > friday_close AND monday_dollar_volume >= 1000000) AS advancers,
    countIf(monday_close < friday_close AND monday_dollar_volume >= 1000000) AS decliners,
    countIf(monday_close = friday_close AND monday_dollar_volume >= 1000000) AS unchanged,
    countIf(monday_dollar_volume >= 1000000) AS liquid_tickers,
    count() AS tickers_traded_both_sessions,
    reverse(arrayStringConcat(extractAll(reverse(toString(count())), '[0-9]{1,3}'), ',')) AS tickers_traded_both_sessions_label,
    count() - countIf(monday_dollar_volume >= 1000000) AS dropped_by_liquidity_filter,
    reverse(arrayStringConcat(extractAll(reverse(toString(count() - countIf(monday_dollar_volume >= 1000000))), '[0-9]{1,3}'), ',')) AS dropped_by_liquidity_filter_label,
    round(100.0 * countIf(monday_close > friday_close AND monday_dollar_volume >= 1000000)
        / countIf(monday_dollar_volume >= 1000000), 1) AS advancer_pct
FROM per_ticker
WHERE friday_close > 0 AND monday_close > 0

3968 个上涨，2327 个下跌，72 个持平：共有 62.3% 的高流动性股票上涨。按个股逐一统计，今日市场涨幅覆盖面较广。

板块表现：涨幅比表面看起来更窄

一种观点是按股票数量平摊权重；另一种则是按公司市值加权。11 只 SPDR 板块 ETF 代表了后者——即标普 500 指数中每个板块对应的市值加权篮子。这些数据与涨跌家数所反映的市场广度存在显著差异。

查询11 个板块篮子：6月29日对比6月26日收盘（常规交易时段）
每个数字背后的完整 SQL
WITH per_etf AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')) AS friday_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')) AS monday_close,
        maxIf(toFloat64(high), window_start >= '2026-06-29 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-06-29 00:00:00') AS day_low,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-06-29 00:00:00') / 1e6, 0) AS dollar_volume_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
      AND ((window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
        OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'))
    GROUP BY ticker
)
SELECT
    sector,
    ticker,
    round((monday_close / friday_close - 1) * 100, 2) AS pct_change,
    round((day_high / day_low - 1) * 100, 2) AS range_pct,
    dollar_volume_m,
    round((monday_close / friday_close - 1) * 100 - min((monday_close / friday_close - 1) * 100) OVER (), 2) AS pct_above_worst_sector
FROM (
    SELECT *,
        multiIf(ticker = 'XLB', 'Materials',
                ticker = 'XLC', 'Communication services',
                ticker = 'XLE', 'Energy',
                ticker = 'XLF', 'Financials',
                ticker = 'XLI', 'Industrials',
                ticker = 'XLK', 'Technology',
                ticker = 'XLP', 'Consumer staples',
                ticker = 'XLRE', 'Real estate',
                ticker = 'XLU', 'Utilities',
                ticker = 'XLV', 'Health care',
                'Consumer discretionary') AS sector
    FROM per_etf
)
ORDER BY pct_change DESC

科技板块领涨，涨幅为 2.52%；可选消费板块紧随其后，涨幅为 2.37%；材料板块下跌 -1.82%，房地产板块下跌 -0.64%。当日板块离散度（表现最好与最差板块之差）为 4.34 个百分点。在指数上涨之际，公用事业、必需消费、能源、房地产和材料板块均录得下跌。按股票数量计算，市场涨幅广泛；但按权重计算，涨幅其实很窄：这正是指数基金收益可能掩盖的真相。

今日亮点：存储与内存

四家公司受同一主题驱动，但走势迥异——仅表现为同涨跌或涨跌幅差异，数据并未说明原因。

查询记忆/存储板块：对比周五收盘及日内波动范围
每个数字背后的完整 SQL
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')) AS friday_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')) AS monday_close,
        maxIf(toFloat64(high), window_start >= '2026-06-29 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-06-29 00:00:00') AS day_low,
        argMinIf(window_start, toFloat64(low), window_start >= '2026-06-29 00:00:00') AS low_bar,
        argMaxIf(window_start, toFloat64(high), window_start >= '2026-06-29 00:00:00') AS high_bar
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('MU', 'SNDK', 'STX', 'WDC')
      AND ((window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
        OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(friday_close, 2) AS jun26_close,
    round(monday_close, 2) AS jun29_close,
    round((monday_close / friday_close - 1) * 100, 2) AS pct_chg,
    round(day_high, 2) AS day_high,
    formatDateTime(toTimeZone(high_bar, 'America/New_York'), '%H:%i') AS day_high_et,
    round(day_low, 2) AS day_low,
    formatDateTime(toTimeZone(low_bar, 'America/New_York'), '%H:%i') AS day_low_et,
    round((day_high / day_low - 1) * 100, 2) AS range_pct
FROM per_name
ORDER BY ticker

MU 是研究振幅与净涨跌差异的典型案例：日内振幅达 12.22%——10:18 ET 时触及 1023.65 低点，15:59 时触及 1148.79 高点——但收盘价较周五上涨 1.97%。Western Digital 上涨 11.15，Seagate 上涨 8.17；SanDisk 在 10.33% 的振幅内收跌 -1.9，是该板块中唯一收跌的公司。收盘价掩盖了持仓者经历的剧烈波动。

资金流向

按成交金额计算，Micron (MU) 的规模远超其他所有标的（包括指数基金）：SPY 的成交额为 $33.97 billion，而 MU 则高达 $58.47 billion。若按成交股数计算，情况则完全不同。

查询成交量领头羊（两种方式）：成交额前6名，成交量前4名
每个数字背后的完整 SQL
SELECT ticker, leaderboard, dollar_volume_bn, if(dollar_volume_bn < 1, dollar_volume_m, NULL) AS dollar_value_m, shares_m,
    round(100 * if(leaderboard = 'by dollars traded', dollar_volume_bn, shares_m)
        / max(if(leaderboard = 'by dollars traded', dollar_volume_bn, shares_m)) OVER (PARTITION BY leaderboard), 1) AS pct_of_board_leader
FROM (
    SELECT
        'by dollars traded' AS leaderboard,
        ticker,
        round(sum(toFloat64(close) * toFloat64(volume)) / 1e9, 2) AS dollar_volume_bn,
        round(sum(toFloat64(close) * toFloat64(volume)) / 1e6, 0) AS dollar_volume_m,
        round(sum(toFloat64(volume)) / 1e6, 1) AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'
    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(close) * toFloat64(volume)) / 1e6, 0) AS dollar_volume_m,
        round(sum(toFloat64(volume)) / 1e6, 1) AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'
    GROUP BY ticker
    ORDER BY shares_m DESC
    LIMIT 4
)
ORDER BY leaderboard ASC, if(leaderboard = 'by dollars traded', dollar_volume_bn, shares_m) DESC

成交股数榜单具有误导性：成交量领先的标的是 SOXS（一种 3 倍做空半导体 ETF，成交 695.3 million 股）以及 INLF（一种仙股，全天 353.8 million 股的市值仅约 $23 million）。成交金额反映了资金流向；相对成交量 则反映了某标的的交易活跃度是否异常。

在纽约时间 30 分钟交易时段内，6 月 29 日呈现了典型的成交量“微笑曲线”：

查询每30分钟成交股数，常规交易时段（单位：十亿）
每个数字背后的完整 SQL
SELECT
    formatDateTime(toStartOfInterval(toTimeZone(window_start, 'America/New_York'), INTERVAL 30 MINUTE), '%H:%i') AS et_time,
    round(sum(toFloat64(volume)) / 1e9, 2) AS shares_bn,
    round(100 * sum(toFloat64(volume)) / max(sum(toFloat64(volume))) OVER (), 1) AS pct_of_biggest_bucket,
    round(100 * (sum(toFloat64(volume)) / min(sum(toFloat64(volume))) OVER () - 1), 1) AS pct_above_trough
FROM global_markets.delayed_stocks_minute_aggs
WHERE window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'
GROUP BY et_time
ORDER BY et_time

开盘半小时成交 2.3 billion 股；13:30 时段出现 0.77 billion 股的低谷；最后在收盘半小时达到峰值，成交量达 2.39 billion 股。该时段集中了 收盘竞价 和指数追踪资金。

The options tape

Options traded 66.33 million contracts across 11.04 million prints.

查询全天期权数据：成交量、0DTE、节假日调整后的周数据
每个数字背后的完整 SQL
WITH
    (
        SELECT (any(underlying_symbol), any(toFloat64(strike_price)), any(option_type),
                any(toDateOrNull(concat('20', substring(ticker, length(ticker) - 14, 6)))),
                sum(size), count(), round(avg(toFloat64(price)), 3))
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'
        GROUP BY ticker
        ORDER BY sum(size) DESC
        LIMIT 1
    ) AS top_contract,
    (
        SELECT round(toFloat64(argMax(close, window_start)), 2)
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY' AND window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'
    ) AS spy_regular_close
SELECT
    round(count() / 1e6, 2) AS option_prints_m,
    round(toFloat64(sum(size)) / 1e6, 2) AS 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) = '260629') / sum(size), 1) AS same_day_expiry_pct,
    round(toFloat64(sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260702')) / 1e6, 2) AS thu_jul2_expiry_contracts_m,
    countIf(substring(ticker, length(ticker) - 14, 6) = '260703') AS fri_jul3_expiry_prints,
    countIf(sip_timestamp < '2026-06-29 13:30:00') AS premarket_prints,
    reverse(arrayStringConcat(extractAll(reverse(toString(countIf(sip_timestamp < '2026-06-29 13:30:00'))), '[0-9]{1,3}'), ',')) AS premarket_prints_label,
    countIf(sip_timestamp < '2026-06-29 13:30:00'
        AND underlying_symbol NOT IN ('SPX', 'SPXW', 'XSP', 'RUTW', 'VIX', 'VIXW')) AS premarket_non_index_prints,
    arrayStringConcat(arraySort(groupUniqArrayIf(underlying_symbol, sip_timestamp < '2026-06-29 13:30:00')), ', ') AS premarket_underlyings,
    round(toFloat64(sumIf(size, underlying_symbol = 'SPY')) / 1e6, 2) AS spy_contracts_m,
    round(toFloat64(sumIf(size, underlying_symbol = 'QQQ')) / 1e6, 2) AS qqq_contracts_m,
    top_contract.1 AS top_contract_underlying,
    top_contract.2 AS top_contract_strike,
    top_contract.3 AS top_contract_type,
    top_contract.4 AS top_contract_expiry,
    top_contract.5 AS top_contract_volume,
    reverse(arrayStringConcat(extractAll(reverse(toString(assumeNotNull(top_contract.5))), '[0-9]{1,3}'), ',')) AS top_contract_volume_label,
    round(top_contract.7, 3) AS top_contract_avg_price,
    round(top_contract.2 - spy_regular_close, 2) AS top_strike_minus_spy_close
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-06-29 00:00:00' AND sip_timestamp < '2026-06-30 00:00:00'

Calls took 55.7% of contract volume, and 35.8% of everything that traded expired that same Monday — the zero-days-to-expiry (0DTE) share. The busiest single contract anywhere was the same-day SPY $741 call: 788,133 contracts at an average premium of $0.474, with SPY's close landing 0.12 dollars below the strike — the day's most-traded option finished out of the money. SPY traded 12.01 million contracts as an underlying; QQQ 7.32 million.

The panel also counts 40,621 option prints before the 9:30 equity open — every one on a cash-settled index root (RUTW, SPX, SPXW, VIX, VIXW, XSP), with 0 stock or ETF option prints in the same window. That is the rule, not an accident: index options list extended global-trading-hours sessions, options on stocks and ETFs open with the stock market. The stocks trade early — see premarket and after-hours trading — their options do not.

No contract with a Friday, July 3 expiration code printed all day (0 prints) — the market is closed that Friday — while the Thursday, July 2 expiry traded 10.73 million.

报价行情：当日交易成本

本页面的所有价格均源自报价流——即针对每只上市股票持续发布的全国最佳买卖报价（NBBO）。最佳买价与最佳卖价之间的差额，即 买卖价差，是订单成交需支付的成本。

查询买卖价差成本：NBBO 更新及中位数报价宽度，常规交易时段
每个数字背后的完整 SQL
SELECT
    ticker,
    round(count() / 1e6, 2) AS nbbo_updates_m,
    round(quantileExactIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, bid_price > 0 AND ask_price >= bid_price), 2) AS median_spread_bps,
    round(quantileExactIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, bid_price > 0 AND ask_price >= bid_price AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 14:00:00'), 2) AS open_30min_spread_bps,
    round(quantileExactIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, bid_price > 0 AND ask_price >= bid_price AND sip_timestamp >= '2026-06-29 17:00:00' AND sip_timestamp < '2026-06-29 17:30:00'), 2) AS midday_spread_bps,
    round(quantileExactIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, bid_price > 0 AND ask_price >= bid_price AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 14:00:00')
        - quantileExactIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, bid_price > 0 AND ask_price >= bid_price AND sip_timestamp >= '2026-06-29 17:00:00' AND sip_timestamp < '2026-06-29 17:30:00'), 2) AS open_minus_midday_bps,
    countIf(NOT (bid_price > 0 AND ask_price >= bid_price)) AS dropped_invalid_quotes
FROM global_markets.cache_stocks_quotes
WHERE ticker IN ('SPY', 'QQQ', 'MU', 'WDC')
  AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 20:00:00'
GROUP BY ticker
ORDER BY ticker

SPY 的报价中值宽度为中间价的 0.41 个基点，相比之下，QQQ 为 0.84，Micron 为 4.6，Western Digital 为 8.2，后者是四者中价差最宽的。对于 10,000 美元的订单，1 个基点等于 1 美元，因此以报价买入并立即卖出 10,000 美元的 SPY，成本约为 0.41 美元；而 Western Digital 的同等往返交易成本为 8.2 美元。交易规模未变，变化的是标的股票。

这四只股票在开盘前半小时的报价宽度均高于午间对照窗口：SPY 为 0.41 bps 对比 0.27，Western Digital 为 14.33 对比 7.55，价差最宽的股票在开盘时溢价 6.78 bp。另一项指标是换手率：SPY 在常规交易时段有 3.98 百万次 NBBO 更新，QQQ 为 4.42 百万次。

利率：收益率曲线几乎没有波动

美债周一走势平稳。以下为每日收盘收益率及其较 6 月 26 日的变动情况。

查询国债收益率曲线，6月29日收盘对比6月26日（仅限已公布期限）
每个数字背后的完整 SQL
SELECT
    t.1 AS curve_point,
    round(t.2, 2) AS jun29_yield_pct,
    round((t.2 - t.3) * 100) AS one_day_change_bp
FROM (
    SELECT arrayJoin([
        ('1 month',  toFloat64(mon.yield_1_month),  toFloat64(fri.yield_1_month)),
        ('3 month',  toFloat64(mon.yield_3_month),  toFloat64(fri.yield_3_month)),
        ('1 year',   toFloat64(mon.yield_1_year),   toFloat64(fri.yield_1_year)),
        ('2 year',   toFloat64(mon.yield_2_year),   toFloat64(fri.yield_2_year)),
        ('5 year',   toFloat64(mon.yield_5_year),   toFloat64(fri.yield_5_year)),
        ('10 year',  toFloat64(mon.yield_10_year),  toFloat64(fri.yield_10_year)),
        ('30 year',  toFloat64(mon.yield_30_year),  toFloat64(fri.yield_30_year)),
        ('2s10s spread', toFloat64(mon.yield_10_year - mon.yield_2_year), toFloat64(fri.yield_10_year - fri.yield_2_year))
    ]) AS t
    FROM (SELECT * FROM global_markets.treasury_yields WHERE date = '2026-06-29') AS mon,
         (SELECT * FROM global_markets.treasury_yields WHERE date = '2026-06-26') AS fri
)

10 年期美债收于 4.38%，当日持平 (0 bp)；3 个月期美债收益率上升 4 bp，至 3.87%。2s10s 利差（10 年期减去 2 年期）收于 0.28 个百分点 (-3 bp)：仍为正值，但曲线略微平坦化。

日历背后的数据

查询6月29日公司日历与信息流汇总
每个数字背后的完整 SQL
WITH
    (
        SELECT (argMax(d, n), max(n))
        FROM (
            SELECT ex_dividend_date AS d, count() AS n
            FROM global_markets.stocks_dividends
            WHERE ex_dividend_date BETWEEN '2026-06-22' AND '2026-07-02'
            GROUP BY d
        )
    ) AS peak_ex_div,
    (
        SELECT (countIf(form_type = '424B2'), countIf(form_type = '4'), countIf(form_type = '8-K'), count())
        FROM global_markets.stocks_sec_edgar_index
        WHERE filing_date = '2026-06-29'
    ) AS filings,
    (
        SELECT (count(), uniqExact(publisher), countIf(has(tickers, 'NVDA')))
        FROM global_markets.stocks_news
        WHERE toDate(toTimeZone(published_utc, 'America/New_York')) = '2026-06-29'
    ) AS news,
    (
        SELECT max(n)
        FROM (
            SELECT t, count() AS n
            FROM (
                SELECT arrayJoin(tickers) AS t
                FROM global_markets.stocks_news
                WHERE toDate(toTimeZone(published_utc, 'America/New_York')) = '2026-06-29'
            )
            WHERE t NOT IN ('NVDA', 'SPCX')
            GROUP BY t
        )
    ) AS runner_up_articles,
    (
        SELECT (any(split_from), any(split_to), count())
        FROM global_markets.stocks_splits
        WHERE ticker = 'HON' AND execution_date = '2026-06-29'
    ) AS hon_split,
    (
        SELECT (
            round(toFloat64(argMaxIf(close, window_start, window_start < '2026-06-27 00:00:00')), 2),
            round(toFloat64(argMaxIf(close, window_start, window_start >= '2026-06-29 00:00:00')), 2)
        )
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'HON'
          AND ((window_start >= '2026-06-26 13:30:00' AND window_start < '2026-06-26 20:00:00')
            OR (window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'))
    ) AS hon_close
SELECT
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-06-29') AS ex_dividend_records_jun29,
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-06-29'
        AND ticker IN ('AAPL', 'MSFT', 'NVDA', 'AMZN', 'GOOGL', 'GOOG', 'META', 'TSLA', 'JPM', 'JNJ',
                       'XOM', 'KO', 'PG', 'V', 'MA', 'HD', 'WMT', 'CVX', 'MRK', 'PEP',
                       'SPY', 'QQQ', 'DIA', 'IWM', 'VTI')) AS household_name_ex_dividends,
    length(['AAPL', 'MSFT', 'NVDA', 'AMZN', 'GOOGL', 'GOOG', 'META', 'TSLA', 'JPM', 'JNJ',
            'XOM', 'KO', 'PG', 'V', 'MA', 'HD', 'WMT', 'CVX', 'MRK', 'PEP',
            'SPY', 'QQQ', 'DIA', 'IWM', 'VTI']) AS household_names_checked,
    concat(monthName(peak_ex_div.1), ' ', toString(toDayOfMonth(peak_ex_div.1))) AS busiest_ex_div_day_of_window,
    peak_ex_div.2 AS busiest_ex_div_day_records,
    (SELECT count() FROM global_markets.stocks_splits WHERE execution_date = '2026-06-29') AS splits_executed,
    (SELECT count() FROM global_markets.stocks_ipos WHERE listing_date = '2026-06-29') AS ipos_listed_jun29,
    (SELECT arrayStringConcat(groupArray(ticker), ', ') FROM (
        SELECT ticker FROM global_markets.stocks_ipos WHERE listing_date = '2026-07-01' ORDER BY ticker
    )) AS jul1_ipo_debuts,
    filings.4 AS sec_filings,
    filings.1 AS prospectus_424b2_filings,
    filings.2 AS insider_form4_filings,
    filings.3 AS filings_8k,
    news.1 AS news_articles,
    news.2 AS news_publishers,
    news.3 AS nvda_articles,
    runner_up_articles AS next_most_covered_articles,
    news.3 - runner_up_articles AS nvda_minus_next_most_covered,
    hon_split.3 AS hon_split_records,
    hon_split.1 AS hon_split_from,
    hon_split.2 AS hon_split_to,
    hon_close.1 AS hon_close_jun26,
    hon_close.2 AS hon_close_jun29

449 股息记录已于 6 月 29 日除息。若要在 除息日 前持有该股票，方可获得派息。在我们查询的 25 个知名品牌中，仅有 0 个符合条件。季度末的申报高峰出现在 July 1，共有 745 份记录。23 次拆股已执行，0 家公司完成 IPO 上市，周三的上市计划（BSP, ITG, LIME）已列入日程。

SEC 在 6 月 29 日共记录了 6173 份文件：1473 份结构化产品定价补充文件 (form 424B2) 以及 1277 份 内部交易报告 (Form 4)，其数量远超引起关注的 231 份 8-K 文件。我们的新闻源涵盖了来自 3 家出版商的 170 篇文章；在排除一个标签模糊的重复代码后，被报道最多的股票是 NVDA，共有 14 篇文章，而排名第二的股票仅有 12 篇。

数据说明

异常数据会进行标注，绝不直接剔除 —— 以下四项会影响核心指标。

  • 板块篮子采用既定方法。 “板块”指 11 只 SPDR 板块 ETF，按市值加权 —— 每日名称一致，而非供应商的个股分类。
  • 成交金额为分钟级代理指标 —— 即每分钟收盘价乘以成交量之和，与成交总额接近但不完全相同。
  • 报价价差不包含无效报价（单边或交叉的 NBBO 记录）；面板会统计剔除的数量（如 SPY 的 1544）。报价中值是报价统计数据，而非单笔交易成本 —— 订单执行价格通常优于报价。
  • 单笔成交可能污染分钟线的最高价或最低价。 QQQ 在东部时间 11:07 的分钟线最低价为 $709.58，而周边分钟线均未跌破 $715.09 —— 仅因一笔成交比同期市场低 $5.51。所有高于此水平的最高价和最低价均已与相邻分钟线进行了交叉核对；QQQ 的当日最低价 $705.172 已通过校验。
完整数据说明
  • 国债覆盖范围窄于架构定义。 宣传的四种期限（6个月、3年、7年和20年）从未填充数据；收益率曲线显示的是现有的七种期限，外加 2s10s 行。
  • Honeywell 的“反向拆股”记录存在矛盾。 数据源包含 6 月 29 日的 1 HON 记录：一次将 2 股旧股转换为 1 股新股的反向拆股。HON 周五收于 $231.3，周一收于 $227.71 —— 价格并未翻倍。我们未对其进行调整。
  • 新闻计数仅为单一供应商的数据，即我们接入的 3 家出版商 —— 并非“全市场新闻”。
  • 逐笔数据详情 —— 包括交叉报价、虚假成交量修正、截断的 FINRA 短仓成交文件（以及为什么 短仓成交不等于短仓头寸） —— 请参阅 深度解析。

交易时段验证

查询交易时段检查：SPY 分钟线跨度及周五收盘记录
每个数字背后的完整 SQL
WITH
    (
        SELECT (
            round(toFloat64(minIf(low, formatDateTime(toTimeZone(window_start, 'America/New_York'), '%H:%i') = '11:07')), 2),
            round(toFloat64(minIf(low, formatDateTime(toTimeZone(window_start, 'America/New_York'), '%H:%i') IN ('11:04', '11:05', '11:06', '11:08', '11:09', '11:10'))), 2)
        )
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'QQQ' AND window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00'
    ) AS qqq_lone
SELECT
    (SELECT count() FROM global_markets.delayed_stocks_minute_aggs
     WHERE ticker = 'SPY' AND window_start >= '2026-07-03 00:00:00' AND window_start < '2026-07-04 00:00:00') AS jul3_spy_bars,
    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,
    count() AS spy_minute_bars,
    countIf(window_start >= '2026-06-29 13:30:00' AND window_start < '2026-06-29 20:00:00') AS regular_session_bars,
    qqq_lone.1 AS qqq_1107_lone_low,
    qqq_lone.2 AS qqq_1107_adjacent_bars_low,
    round(qqq_lone.2 - qqq_lone.1, 2) AS qqq_lone_print_below_adjacent
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY' AND window_start >= '2026-06-29 00:00:00' AND window_start < '2026-06-30 00:00:00'

SPY 的 K 线在纽约时间 04:00 至 19:59 运行，包含 390 根常规窗口 K 线。这是完整的交易时段，已通过行情数据验证（交易所日历数据集仅包含即将到来的收盘时间）。那个 7 月 3 日（周五）是完整交易日 —— 0 SPY 全天都有 K 线，而 7 月 4 日是周六；这四个交易日的周回顾请见 此处。

常见问题

2026年6月29日股市表现如何？

增长股领涨，市场收涨：QQQ收盘上涨 2.57%，SPY上涨 1.62%，DIA上涨 0.81%，IWM上涨 0.45%。62.3% 的个股成交额在100万美元以上且收涨。

2026年6月29日对纳斯达克来说是重要的一天吗？

波动剧烈但并非极端：QQQ的收盘涨跌幅为 2.57%，按绝对值计算，在过去一个月 21 个交易日中排名第 5，该月最大波动幅度为 4.76%。

2026年6月29日哪些板块领涨？

在11个SPDR板块指数中，科技板块 (2.52%) 和非必需消费品板块 (2.37%) 领涨；材料板块表现最差，跌幅为 -1.82%，与领先板块差距为 4.34 个百分点。公用事业、必需消费品、能源和房地产板块也收跌。

2026年6月29日当日到期期权（0DTE）的成交量占比是多少？

当日到期期权占总成交量 66.33 百万份合约的 35.8%。交易最活跃的单份合约是当日到期的 SPY $741 看涨期权（成交 788,133 份），最终处于价外。

为什么期权在股市开盘前就有交易？

仅指数期权如此。6月29日东部时间上午9:30之前，所有 40,621 份期权成交记录均属于现金结算的指数期权 (RUTW, SPX, SPXW, VIX, VIXW, XSP)，这些期权包含全球延长交易时段；该时段内的股票和ETF期权成交量为 0。

方法论

  • 时间戳以 UTC 存储，并在查询中转换为纽约时间。“收盘价”指常规交易时段的最后一分钟 K 线，而非集合竞价价格；日期变更对比的是 6 月 29 日与 6 月 26 日；交易时段已通过观测到的 K 线跨度进行验证。
  • 滚动月排名采用常规交易时段的收盘价涨跌幅，并剔除了首个交易日（因为窗口期内没有前一交易日的收盘价）。期权到期日已根据 OCC 代码重新解析（原表中的到期日列存在错误）。
  • 所有面板均在编写时通过受限的只读路径读取一次。数据仓库状态截至 2026 年 7 月 13 日。

每个面板都是存储的查询结果——包含图表、表格和 SQL。您可以将其中任何内容粘贴到 Strasmore 终端。下一交易日：6 月 30 日。

#recap#daily#spy#qqq#market structure