2026年6月29日股市收盘数据复盘
2026年6月29日市场数据:成长股领涨、行业分化约四个百分点,存储芯片冲高回落,0DTE、报价与利率表现如何?查看完整盘面分析。
2026年6月29日(星期一)是由成长股领涨的上涨日,但领涨范围较窄:QQQ上涨 2.57%,SPY上涨 1.62%,3968只股票上涨,对比2327只股票下跌;不过,近一半的行业篮子收跌。所有数字均来自存储的查询;展开任一面板即可查看 SQL。同期的逐笔交易层面分析:6月29日微观结构深度分析。
盘面概览
每项变动均比较6月29日常规交易时段最后一分钟K线与6月26日星期五的对应数据。
| 股票代码 | 6月26日收盘价 | 6月29日开盘价 | 6月29日收盘价 | 涨跌幅(%) | 占最佳涨跌幅(%) | 日内最高价 | 日内最低价 | 成交股数(百万) |
|---|---|---|---|---|---|---|---|---|
| DIA | 517.5 | 520.63 | 521.68 | 0.81 | 31.5 | 522.975 | 519.4 | 5.5 |
| IWM | 297.61 | 298.11 | 298.95 | 0.45 | 17.5 | 299.16 | 294.68 | 21.7 |
| QQQ | 705.84 | 713.99 | 723.95 | 2.57 | 100 | 724.58 | 705.172 | 38.5 |
| SPY | 729.09 | 736.525 | 740.88 | 1.62 | 63 | 741.56 | 732.09 | 46 |
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.tickerSPY开盘报$736.525,而星期五收盘价为$729.09,最终收于$740.88,低于盘中高点$741.56。QQQ上涨2.57%,DIA上涨0.81%,显示当天市场由成长股和科技股领涨;小盘股ETF IWM上涨0.45%。
这一天是否异常?
如果没有参照标准,涨跌幅本身意义有限。本面板按收盘价对收盘价的绝对变动幅度,将6月29日与过去一个月进行比较排名。
| QQQ收盘至收盘涨跌幅(%) | QQQ绝对变动排名 | QQQ比较交易日数 | QQQ月内最大变动(%) | QQQ上涨交易日数 | SPY收盘至收盘涨跌幅(%) | SPY绝对变动排名 | SPY比较交易日数 | SPY开盘至收盘涨跌幅(%) | 首个交易日 |
|---|---|---|---|---|---|---|---|---|---|
| 2.57 | 5 | 21 | 4.76 | 10 | 1.62 | 4 | 21 | 0.59 | May 29, 2026 |
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)涨幅较大,但并非创纪录:QQQ的2.57%变动,在自May 29, 2026以来的21个交易日中排名第5。在这一个月中,单日最大变动为4.76%;SPY的1.62%变动在21个交易日中排名第4。变动来自哪里同样重要:SPY开盘至收盘的上涨仅为0.59%,因此大部分涨幅来自隔夜跳空,在常规交易时段首根K线成交前就已出现。QQQ在其中10个交易日收盘上涨,几乎是五五开。
市场广度:本轮上涨覆盖面有多广?
上涨股是指周一收盘价高于周五收盘价的股票,统计对象为成交额至少达到100万美元的股票。该筛选条件剔除了5,108的双日交易股票,占11,475。
| 上涨股票数 | 下跌股票数 | 平盘股票数 | 高流动性股票代码 | 两个交易日均交易的股票代码 | 两个交易日均交易的股票代码标签 | 因流动性筛选被剔除 | 因流动性筛选被剔除标签 | 上涨股票占比(%) |
|---|---|---|---|---|---|---|---|---|
| 3968 | 2327 | 72 | 6367 | 11475 | 11,475 | 5108 | 5,108 | 62.3 |
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上涨股3968只,下跌股2327只,持平股72只:按单只股票计,流动性较高的股票中有62.3%上涨,市场上涨覆盖面广。
逐行业看:涨势的覆盖面没有表面上那么广
等权统计股票数量是一种观察方式;按公司规模加权则是另一种。SPDR的11只行业ETF是后者的简写形式:每只ETF代表标普500指数中一个行业、按市值加权的一篮子股票。两种方法对市场广度的判断存在明显差异。
| 行业 | 股票代码 | 涨跌幅(%) | 振幅(%) | 成交额(百万) | 高于最差行业(%) |
|---|---|---|---|---|---|
| Technology | XLK | 2.52 | 3.91 | 2075 | 4.34 |
| Consumer discretionary | XLY | 2.37 | 2.17 | 1142 | 4.2 |
| Communication services | XLC | 1.66 | 0.78 | 669 | 3.48 |
| Industrials | XLI | 0.89 | 1.23 | 1231 | 2.72 |
| Financials | XLF | 0.28 | 0.64 | 1593 | 2.1 |
| Health care | XLV | 0.26 | 0.76 | 1897 | 2.08 |
| Utilities | XLU | -0.32 | 1.18 | 759 | 1.5 |
| Consumer staples | XLP | -0.38 | 1.23 | 764 | 1.44 |
| Energy | XLE | -0.52 | 1.51 | 1098 | 1.3 |
| Real estate | XLRE | -0.64 | 1.45 | 217 | 1.18 |
| Materials | XLB | -1.82 | 2.28 | 626 | 0 |
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科技行业领涨,位于2.52%;非必需消费品行业紧随其后,位于2.37%;材料行业收于-1.82%,房地产行业收于-0.64%。当日行业表现的离散度,即最佳行业与最差行业之差,为4.34个百分点。在指数上涨的当天,公用事业、必需消费品、能源、房地产和材料行业均下跌。按股票数量计算,涨势覆盖面较广;按权重计算,涨势却较为集中。这正是指数基金上涨可能掩盖的情况。
当日焦点:内存与存储
四只股票交易的是同一主题,但结果大不相同。数据仅显示它们的联动性和波动幅度,并未说明原因。
| 股票代码 | 6月26日收盘价 | 6月29日收盘价 | 涨跌幅 | 日内最高价 | 日内最高价(ET) | 日内最低价 | 日内最低价(ET) | 振幅(%) |
|---|---|---|---|---|---|---|---|---|
| MU | 1122.92 | 1145 | 1.97 | 1148.79 | 15:59 | 1023.65 | 10:18 | 12.22 |
| SNDK | 2091.08 | 2051.29 | -1.9 | 2090.71 | 09:30 | 1895 | 10:18 | 10.33 |
| STX | 895.27 | 968.4 | 8.17 | 987.57 | 14:48 | 880.01 | 10:00 | 12.22 |
| WDC | 586.32 | 651.7 | 11.15 | 652.98 | 15:59 | 590 | 09:31 | 10.67 |
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 tickerMU是“区间波动”与“净变动”差异的典型案例:盘中波动区间为12.22%,在10:18 ET触及$1023.65低点,在15:59触及$1148.79高点,但收盘价仍较周五高1.97%。Western Digital上涨11.15%,Seagate上涨8.17%;SanDisk在10.33%的区间内收盘上涨-1.9%,是这一组股票中唯一收盘下跌的股票。单看收盘价,无法反映持有者盘中的实际经历。
Where the money traded
By dollars traded, Micron (MU) towered over everything, index funds included: $58.47 billion against SPY's $33.97 billion. By share count, a different day.
| 股票代码 | 排行榜 | 美元成交额(十亿) | 美元金额(百万) | 股数(百万) | 占榜首百分比 |
|---|---|---|---|---|---|
| MU | by dollars traded | 58.47 | None | 53.5 | 100 |
| SPY | by dollars traded | 33.97 | None | 46 | 58.1 |
| QQQ | by dollars traded | 27.67 | None | 38.5 | 47.3 |
| NVDA | by dollars traded | 21.66 | None | 111.8 | 37 |
| TSLA | by dollars traded | 20.66 | None | 51.4 | 35.3 |
| SNDK | by dollars traded | 19.73 | None | 10 | 33.7 |
| SOXS | by shares traded | 2.84 | None | 695.3 | 100 |
| INLF | by shares traded | 0.02 | 23 | 353.8 | 50.9 |
| TZA | by shares traded | 1.3 | None | 330.7 | 47.6 |
| BITO | by shares traded | 2.03 | None | 250.7 | 36.1 |
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) DESCShare-count boards mislead: the share leaders were SOXS, a 3x-leveraged inverse semiconductor ETF (695.3 million shares), and INLF, a penny stock whose 353.8 million shares were worth about $23 million all day. Dollar volume shows where money moved; relative volume shows whether a name's activity is unusual for itself.
In 30-minute New York buckets, June 29 traces the classic volume "smile":
| ET时间 | 股数(十亿) | 占最大类别百分比 | 高于谷值百分比 |
|---|---|---|---|
| 09:30 | 2.3 | 96.3 | 199.9 |
| 10:00 | 1.9 | 79.5 | 147.6 |
| 10:30 | 1.42 | 59.4 | 85.1 |
| 11:00 | 1.23 | 51.6 | 60.6 |
| 11:30 | 1.1 | 46.2 | 43.7 |
| 12:00 | 0.91 | 38.2 | 18.8 |
| 12:30 | 0.85 | 35.4 | 10.4 |
| 13:00 | 0.92 | 38.6 | 20.2 |
| 13:30 | 0.77 | 32.1 | 0 |
| 14:00 | 0.82 | 34.2 | 6.4 |
| 14:30 | 0.82 | 34.5 | 7.4 |
| 15:00 | 1.05 | 44 | 36.9 |
| 15:30 | 2.39 | 100 | 211.4 |
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_time2.3 billion shares in the opening half hour, a 0.77 billion trough at 13:30, and the biggest bucket, 2.39 billion, in the closing half hour, where closing auctions and index-tracking flows concentrate.
期权成交明细
期权成交 66.33百万张合约,共计 11.04百万笔成交。
| 期权成交笔数(百万) | 合约数(百万) | 看涨期权成交量占比 | 当日期权到期占比 | 7月2日(周四)到期合约数(百万) | 7月3日(周五)成交笔数 | 盘前成交笔数 | 盘前成交笔数标签 | 盘前非指数成交笔数 | 盘前标的数 | SPY合约数(百万) | QQQ合约数(百万) | 最高成交合约标的 | 最高成交合约行权价 | 最高成交合约类型 | 最高成交合约到期日 | 最高成交合约成交量 | 最高成交合约成交量标签 | 最高成交合约平均价格 | 最高行权价减SPY收盘价 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11.04 | 66.33 | 55.7 | 35.8 | 10.73 | 0 | 40621 | 40,621 | 0 | RUTW, SPX, SPXW, VIX, VIXW, XSP | 12.01 | 7.32 | SPY | 741 | C | 2026-06-29 | 788133 | 788,133 | 0.474 | 0.12 |
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'看涨期权占合约成交量的 55.7%,当日成交合约中有 35.8% 于同一个周一到期,即零日期权(0DTE)占比。全市场最活跃的单一合约是当日到期的 SPY $741 看涨期权:成交 788,133张,平均权利金为 $0.474。SPY 收盘价低于行权价 0.12美元,因此当日成交量最大的期权最终价外到期。SPY 作为标的成交 12.01百万张合约;QQQ 为 7.32百万张。
该面板还统计了美股9:30开盘前的 40,621 笔期权成交,其中每一笔的标的都是现金结算的指数代码(RUTW, SPX, SPXW, VIX, VIXW, XSP);同期股票或 ETF 期权仅成交 0 笔。这是交易规则,并非偶然:指数期权设有延长的全球交易时段,股票和 ETF 期权则随股票市场开盘。股票可以在盘前和盘后交易,其期权不能。
全天没有任何合约成交带有7月3日(周五)的到期代码(0笔),因为市场当日休市;7月2日(周四)到期的合约成交了 10.73百万张。
报价带:当天交易成本是多少
本页的每个价格都来自报价流,即全国最佳买卖报价(NBBO)。该报价会针对每只上市证券持续重新发布。最佳买价与最佳卖价之间的差额,即买卖价差,就是订单成交时为跨越价差所支付的成本。
| 股票代码 | NBBO更新次数(百万) | 价差中位数(基点) | 开盘30分钟价差(基点) | 午盘价差(基点) | 开盘减午盘基点 | 已剔除无效报价 |
|---|---|---|---|---|---|---|
| MU | 0.75 | 4.6 | 6.55 | 4.11 | 2.44 | 414 |
| QQQ | 4.42 | 0.84 | 0.98 | 0.55 | 0.42 | 629 |
| SPY | 3.98 | 0.41 | 0.41 | 0.27 | 0.14 | 1544 |
| WDC | 0.12 | 8.2 | 14.33 | 7.55 | 6.78 | 43 |
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 tickerSPY的报价中位价差为其中间价的0.41个基点;QQQ为0.84个基点,Micron为4.6个基点,Western Digital为8.2个基点,是四者中最宽的。1万美元订单每一个基点对应1美元,因此,按报价买入并立即卖出1万美元的SPY,成本约为0.41美元;Western Digital的同样往返交易成本为8.2美元。交易规模没有变化,变化的是证券本身。
四只证券在开盘后半小时的报价价差都宽于午间对照时段。SPY分别为0.41个基点和0.27个基点,Western Digital分别为14.33个基点和7.55个基点;对于价差最宽的证券,开盘溢价为6.78个基点。交易活跃度是另一面:正常交易时段内,SPY的NBBO更新达到3.98百万次,QQQ为4.42百万次。
利率:收益率曲线几乎未变
美国国债市场周一交投平静。收益率为每日收盘数据,变动均为相对于6月26日的变化。
| 曲线点 | 6月29日收益率(%) | 单日变动(基点) |
|---|---|---|
| 1 month | 3.71 | 1 |
| 3 month | 3.87 | 4 |
| 1 year | 3.97 | 3 |
| 2 year | 4.1 | 3 |
| 5 year | 4.14 | 2 |
| 10 year | 4.38 | 0 |
| 30 year | 4.86 | -1 |
| 2s10s spread | 0.28 | -3 |
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年期国债收益率收于4.38%,日内持平(0个基点);3个月期国库券收益率上升4个基点,至3.87%。2s10s利差(10年期减去2年期)收于0.28个百分点(-3个基点):仍为正值,但略有趋平。
日历背后的交易日
| 6月29日除息记录 | 知名公司除息数 | 已检查知名公司数 | 窗口期最繁忙除息日 | 最繁忙除息日记录数 | 已执行拆股 | 6月29日上市IPO数 | 7月1日IPO首发数 | SEC申报文件 | 424B2招股说明书申报数 | 内部人士Form 4申报数 | 8-K申报数 | 新闻文章数 | 新闻发布商数 | NVDA文章数 | 次高关注度文章数 | NVDA减次高关注度数 | HON拆股记录数 | HON拆股前 | HON拆股后 | HON 6月26日收盘价 | HON 6月29日收盘价 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 449 | 0 | 25 | July 1 | 746 | 23 | 0 | BSP, ITG, LIME | 6173 | 1473 | 1277 | 231 | 170 | 3 | 14 | 12 | 2 | 1 | 2 | 1 | 231.3 | 227.71 |
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_jun29449笔股息记录于6月29日除息。您必须在该股票的除息日前持有,否则无权获得股息。但在我们核查的25个知名公司中,只有0个出现了相关记录。季度末行情在July 1的746笔记录处达到峰值。当天完成了23次拆股并有0家公司完成IPO上市;周三的新股首日交易(BSP, ITG, LIME)已列入日历。
SEC记录了日期为6月29日的6173份申报文件:1473份结构性产品定价补充文件(表格424B2)和1277份内部人交易报告(表格4),数量远超受到新闻关注的231份8-K文件。我们的新闻源收录了来自3家出版机构的170篇文章;排除一个标签含义不明确且在查询中剔除的重复代码后,报道最多的公司是NVDA,共有14篇文章;排名第二的公司为12篇。
数据说明
异常数据会单独注明,不会被默默剔除。以下四点会影响标题数据。
- 行业篮子采用预先声明的方法。“行业”指11只SPDR行业ETF,按市值加权。每个交易时段使用相同的成分,而不是供应商按ticker划分的分类。
- 美元成交额是每分钟的代理指标。计算方法为每分钟K线的收盘价乘以成交量后求和。该结果接近逐笔成交金额之和,但并不完全相同。
- 报价价差已排除无效报价。包括单边报价或交叉NBBO记录。面板会统计剔除数量(SPY为1544)。报价中位宽度是报价统计指标,并不代表每笔交易成本,因为订单通常会在报价区间内成交。
- 单笔成交可能污染一分钟K线的最高价或最低价。QQQ在美国东部时间11:07的K线录得709.58美元的低点,而相邻K线均未跌破715.09美元;其中一笔成交比同期市场价格低5.51美元。上文所有最高价和最低价均已与相邻K线交叉核对。QQQ的705.172美元盘中低点通过了核验。
:::details完整数据说明
- 国债期限覆盖范围少于数据结构设定。所列四个期限,即6个月、3年、7年和20年,从未填充过数据。收益率曲线展示现有的七个期限,另加2s10s一行。
- Honeywell的一次“反向拆股”与交易记录不符。数据源载有一条日期为6月29日的HON记录1:反向拆股将2股旧股转换为1股新股。HON周五收于231.3美元,周一收于227.71美元,并未翻倍。因此我们未应用该调整。
- 新闻数量来自一家供应商的资讯源。统计的是我们收录的3家媒体,而不是“所有市场新闻”。
- **逐笔成交记录、交叉报价、虚假成交量修正、截断的FINRA卖空成交量文件,以及成交量不等于卖空权益的原因,均见深度解析。
:::
本交易时段已核实
| 7月3日SPY柱数 | SPY首根柱(美东时间) | SPY 最后一根K线(美东时间) | SPY 分钟K线 | 常规交易时段K线 | QQQ 1107 单独低点 | QQQ 1107 相邻K线低点 | QQQ 低于相邻价位的单独成交 |
|---|---|---|---|---|---|---|---|
| 0 | 04:00 | 19:59 | 897 | 390 | 709.58 | 715.09 | 5.51 |
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线覆盖纽约时间04:00至19:59,常规交易时段恰好包含390根K线,构成完整交易时段;这一点已根据逐笔成交记录核实(交易所日历数据集仅记录即将到来的休市日)。当年7月3日星期五全天休市,0根SPY K线全天均无成交;7月4日为星期六。这一周仅有四个交易日,另有专门回顾。
常见问题
2026年6月29日股市表现如何?
成长股领涨,市场上涨:QQQ较周五收盘上涨2.57%,SPY上涨1.62%,DIA上涨0.81%,IWM上涨0.45%。成交额达到100万美元或以上的股票中,有62.3%收高。
2026年6月29日是纳斯达克的重要交易日吗?
表现强劲,但并不异常:QQQ较前一交易日收盘的2.57%涨跌幅,按绝对值计算,在过去一个月的21个交易日中排名5。该月最大单日涨跌幅为4.76%。
2026年6月29日哪些板块领涨?
科技板块(2.52%)和非必需消费品板块(2.37%)领涨十一只SPDR行业篮子。材料板块垫底,涨跌幅为-1.82%,板块间差距为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低。
2026年6月29日的期权成交量中,有多少是0DTE?
当日到期的期权占成交的35.8%。当日共成交66.33百万张合约。单一合约中成交最活跃的是一份当日到期的SPY $741看涨期权,共成交788,133张,最终价外到期。
为什么期权会在股市开盘前交易?
只有指数期权会这样交易。6月29日美国东部时间上午9:30前的全部40,621笔期权成交,均对应现金结算的指数根(RUTW, SPX, SPXW, VIX, VIXW, XSP)。这些指数根提供延长的全球交易时段。同期股票和ETF期权成交笔数为0。
方法
- 时间戳以 UTC 存储,并在查询中转换为纽约时间。“收盘价”指常规交易时段最后一分钟的K线,不包括集合竞价成交价;日变化比较6月29日与6月26日,交易时段已根据观测到的K线时间跨度完成核验。
- 过去一个月的排名采用常规交易时段的收盘价对收盘价涨跌幅,并剔除首个交易时段,因为窗口内没有其前一收盘价。期权到期日会根据 OCC ticker 重新解析,因为表格自身的到期日列存在错误。
- 每个面板均在撰写时通过受限只读路径读取一次。数据仓库状态截至2026年7月13日。
每个面板都是一个存储查询结果对象,其中包含图表、表格和 SQL。您可以将其中任何一个粘贴到 Strasmore 终端。下一交易时段:6月30日。