Strasmore Research
市場回顧 Matt Connor作者： Matt Connor · 更新於 2026-07-25

2026年6月29日市場回顧

成長股領漲但漲勢集中，近半板塊收跌。記憶體股劇烈波動，0DTE交易與極窄報價利差值得關注。

2026年6月29日週一，市場在成長股帶動下收漲，但領漲族群狹窄：QQQ 上漲 2.57%，SPY 上漲 1.62%，3968 檔流動性較高的個股上漲，2327 檔下跌——然而近半數的板塊籃子仍以跌勢作收。所有數據均取自預存查詢；展開任一區塊即可查看對應的 SQL。同日逐筆微觀結構分析請見：6月29日微觀結構深度解析。

記分板

所有變動皆以6月29日最後一根常規交易時段分鐘K線，對比6月26日週五的收盤。

查詢SPY / QQQ / DIA / IWM — 6月29日 vs 6月26日收盤，正常交易時段
每個數據背後的精確 SQL 語法
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以$736.525開盤，週五收盤價為$729.09，終場收在$740.88，低於盤中高點$741.56。QQQ上漲2.57%，DIA上漲0.81%，顯示今天是成長股與科技股領漲的一天；小型股IWM則上漲0.45%。

這一天算不尋常嗎？

沒有比較基準，單純的漲跌幅百分比意義不大。以下面板將6月29日的表現，與過去一個月內每日收盤價變動的絕對值進行排序比較。

查詢QQQ 與 SPY：6月29日在過去一個月交易日中的排名（排名1 = 絕對波動最大）
每個數據背後的精確 SQL 語法
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)

幅度不小，但並非紀錄：QQQ 的 2.57% 漲跌幅，在 May 29, 2026 以來的 21 個交易日中排名第 5 ，而該月最大的單日波動幅度為 4.76% ；SPY 的 1.62% 漲跌幅則在 21 個交易日中排名第 4 。漲幅的來源也很關鍵：SPY 從開盤到收盤的實際走勢僅有 0.59% ，這意味著大部分漲幅是在常規交易時段開始前，透過隔夜跳空缺口完成的。QQQ 在這些交易日中有 10 的比例收高：這一個月走勢就像擲硬幣一樣難以捉摸。

廣度：漲勢有多廣泛？

上漲股定義為週一收盤價高於上週五、且當日成交金額至少達100萬美元的股票——此篩選條件剔除了11,475檔雙盤交易股票中的5,108檔。

查詢6月29日成交金額至少100萬美元之標的，漲跌家數比
每個數據背後的精確 SQL 語法
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檔上漲、2327檔下跌、72檔持平：流動性較高的股票中有62.3%收漲——以單一個股逐一計算，屬於廣泛上漲的一天。

逐個板塊檢視：漲勢比表面上看起來更集中

將每檔股票一視同仁是一種觀點；按公司規模加權則是另一種。十一檔 SPDR 板塊 ETF 正是後者的縮影——每個標普 500 板塊各有一檔市值加權的投資組合。它們的表現與廣度統計大相逕庭。

查詢十一大類股指數：6月29日 vs 6月26日收盤，正常交易時段
每個數據背後的精確 SQL 語法
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

科技股以 2.52% 領漲，非必需消費品以 2.37% 緊隨其後；原物料收盤 -1.82%，房地產則 -0.64%。最佳與最差板塊之間的差距——當日的板塊離散度——達到 4.34 個百分點，公用事業、必需消費品、能源、原物料及房地產在大盤上漲的日子裡全數收黑。以個股數量來看是廣度不足，以權重來看則是集中上漲：這正是指數型基金漲幅所能掩蓋的真相。

今日焦點：記憶體與儲存

四檔股票圍繞同一主題交易，結果卻大相逕庭——此處僅呈現聯動性與漲跌幅；數據本身不說明原因。

查詢記憶體/儲存類股：相較上週五收盤之變動及盤中波動區間
每個數據背後的精確 SQL 語法
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

美光（MU）是區間漲跌與淨變動對比的教科書案例：盤中波動區間達 12.22%，低點 $1023.65 出現在美東時間 10:18，高點 $1148.79 出現在 15:59，收盤卻較上週五 1.97%。威騰（Western Digital）上漲 11.15%，希捷（Seagate）上漲 8.17%；晟碟（SanDisk）在 10.33% 的波動區間內收盤 -1.9%——是該族群中唯一收低的個股。收盤價掩蓋了持股人當日實際經歷的起伏。

資金流向何處

以成交金額來看，美光（Micron，MU）的規模壓倒一切，甚至超越指數基金：$58.47 億，對比 SPY 的 $33.97 億。若以成交股數來看，則是另一番景象。

查詢成交量領先者雙向觀察：成交金額前6名、成交股數前4名
每個數據背後的精確 SQL 語法
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

成交股數排行榜具有誤導性：股數領先的是 SOXS，一支 3 倍槓桿反向半導體 ETF（695.3 萬股），以及 INLF，一支細價股，其 353.8 萬股的成交股數，全天價值僅約 $23 萬。成交金額顯示資金實際移動的方向；相對成交量則顯示某檔標的的交易活動相對於自身是否異常。

以紐約時間 30 分鐘為區間，6 月 29 日描繪出典型的成交量「微笑曲線」：

查詢每30分鐘區間成交股數，正常交易時段（十億股）
每個數據背後的精確 SQL 語法
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

開盤前半小時成交 2.3 億股，在 13:30 時出現 0.77 億股的低谷，而最大區間——2.39 億股——出現在收盤前半小時，此處正是收盤競價與指數追蹤資金流集中之處。

選擇權交易實況

選擇權交易量達 66.33 萬口合約，分佈於 11.04 萬筆成交紀錄。

查詢選擇權全日摘要列：成交量、0DTE、受假期影響之週
每個數據背後的精確 SQL 語法
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'

買權佔合約交易量的 55.7%，而 35.8% 的所有交易合約都在同一個週一到期——即零日到期 (0DTE) 佔比。全市場最活躍的單一合約是當日到期的 SPY $741 買權：788,133 口合約，平均權利金為 $0.474，而 SPY 收盤價較履約價低 0.12 美元——當日交易量最大的選擇權最終以價外結算。SPY 作為標的物交易了 12.01 萬口合約；QQQ 則為 7.32 萬口。

該面板亦統計了美股上午 9:30 開盤前的 40,621 筆選擇權成交紀錄——每一筆都來自現金結算的指數選擇權類別 (RUTW, SPX, SPXW, VIX, VIXW, XSP)，而同一時段內僅有 0 筆股票或 ETF 選擇權成交。這是規則使然，並非偶然：指數選擇權提供延長的全球交易時段，而股票和 ETF 的選擇權則與股票市場同步開盤。股票本身有盤前交易——參見盤前與盤後交易——但其選擇權則無。

全天沒有任何一筆帶有 7 月 3 日星期五到期代碼的合約成交（0 筆成交紀錄）——因為該週五市場休市——而 7 月 2 日星期四到期的合約則交易了 10.73 萬口。

報價紀錄：當日交易成本

本頁每個價格都來自報價串流——全國最佳買賣價，針對每檔掛牌標的持續重新發布。最佳買價與最佳賣價之間的差距，即買賣價差，就是委託單跨越時所支付的成本。

查詢跨越價差之成本：NBBO更新次數與中位報價寬度，正常交易時段
每個數據背後的精確 SQL 語法
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 的中位數報價寬度為中價的 0.41 個基點，QQQ 為 0.84，美光為 4.6，威騰電子為 8.2——四者中最寬。一筆 10,000 美元訂單的一個基點等於一美元，因此以報價買入並立即賣出 10,000 美元的 SPY，成本約為 $0.41；同樣的威騰電子往返交易成本為 $8.2。交易規模沒有改變；改變的是標的。

四者在開盤前半小時的報價寬度均高於中午對照時段——SPY 為 0.41 個基點對 0.27，威騰電子為 14.33 對 7.55，最寬標的的開盤溢價為 6.78 個基點。交易活躍度是另一半：SPY 在常規交易時段有 3.98 百萬次 NBBO 更新，QQQ 則有 4.42 百萬次。

利率：殖利率曲線幾乎紋風不動

美國公債週一表現平淡。以下殖利率皆為每日收盤價，變動幅度係與6月26日相比。

查詢美國公債殖利率曲線，6月29日收盤 vs 6月26日（僅顯示有報價之年期）
每個數據背後的精確 SQL 語法
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年期公債收在 4.38%，當日持平（0個基點），而3個月期國庫券上升4個基點至3.87%。2年期與10年期利差——即10年期減去2年期——收在0.28個百分點（-3個基點）：仍為正值，曲線略微趨平。

這一天背後的日曆

查詢6月29日公司行事曆與資訊流，單列摘要
每個數據背後的精確 SQL 語法
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 筆除息紀錄於 6 月 29 日除息——必須在除息日前持有該股票，否則股息不歸你所有——然而在我們查核的 25 家知名企業中，出現了 0。季末浪潮在 July 1 達到 745 筆紀錄的高峰。執行了 23 次股票分割，並有 0 檔 IPO 掛牌，週三的新上市股票（BSP, ITG, LIME）已在日曆上。

SEC 記錄了日期為 6 月 29 日的 6173 份申報文件：1473 份結構型產品定價補充文件（424B2 表格）和 1277 份內部人交易報告（Form 4），讓登上頭條的 231 份 8-K 表格相形見絀。我們的新聞推送包含了來自 3 家發布商的 170 篇文章；排除一個標記模糊的重複使用代碼（在查詢中已剔除）後，報導最多的名稱是 NVDA，有 14 篇文章，而第二名則有 12 篇。

資料說明

異常數據會附上說明，絕不默默剔除——以下四點直接影響標題數字。

  • 板塊籃子是公開的方法論。「板塊」指的是十一檔 SPDR 板塊 ETF，以市值加權計算——每節皆為相同名稱，而非供應商按個股分類的結果。
  • 成交金額是每分鐘的近似值——以收盤價 × 每分鐘 K 棒累計成交量計算，接近但不等於逐筆成交價值的加總。
  • 報價價差排除無效報價（單邊或交叉的 NBBO 紀錄）；面板統計了剔除的數量（SPY 為 1544）。中位數報價寬度是報價統計數據，而非每筆交易的實際成本——訂單常在報價內成交。
  • 單筆異常成交可能汙染某分鐘 K 棒的最高或最低價。QQQ 在美東時間 11:07 的 K 棒出現 $709.58 的低點，而前後 K 棒均未跌破 $715.09——該筆成交比當時市場價格低了 $5.51。以上所有高、低點均已與相鄰 K 棒交叉比對；QQQ 的 $705.172 盤中低點通過檢驗。
完整資料說明
  • 公債涵蓋範圍比表定規格窄。四個公告的期限（6 個月、3 年、7 年及 20 年期）從未填入數據；曲線顯示實際存在的七個期限，外加 2s10s 利差列。
  • 一筆 Honeywell 的「反向分割」與行情帶矛盾。資料饋送帶有日期為 6 月 29 日的 1 HON 紀錄：一筆反向分割將 2 舊股轉換為 1 新股。HON 週五收盤 $231.3，週一收盤 $227.71——並未倍增。我們未套用該筆分割。
  • 新聞計數來自單一供應商的饋送，即我們所涵蓋的 3 家發布機構——並非「全市場新聞」。
  • 逐筆接收資料——交叉報價、虛假成交量修正、被截斷的 FINRA 放空成交量檔案（以及為何放空成交量不等於放空餘額）——詳見深入探討。

該交易時段，已驗證

查詢交易時段檢視：SPY觀察到之分鐘K線跨度，以及磁帶上的週五收盤
每個數據背後的精確 SQL 語法
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 棒涵蓋紐約時間 04:00 至 19:59，精確包含 390 根常規交易時段 K 棒——此為經由逐筆成交資料驗證的完整交易時段（交易所日曆資料集僅記錄即將到來的休市日）。該週五，即 7 月 3 日，為全日休市——0 根 SPY K 棒全天均有列印，因 7 月 4 日適逢週六；該四個交易日的週別已有其獨立回顧。

常見問題

2026年6月29日股市表現如何？

成長股帶動的漲勢：QQQ收盤較上週五上漲2.57%，SPY上漲1.62%，DIA上漲0.81%，IWM上漲0.45%，而成交金額達一百萬美元以上的標的中，有62.3%收高。

2026年6月29日對那斯達克來說是重要的一天嗎？

重要，但非異常：QQQ的2.57%收盤變動幅度，在過去一個月21個交易日中，按絕對值排名第5，當月最高變動幅度為4.76%。

2026年6月29日哪些板塊領漲？

科技（2.52%）和非必需消費（2.37%）在十一檔SPDR板塊ETF中領漲；原物料表現最差，為-1.82%，兩者差距達4.34個百分點。公用事業、必需消費、能源和房地產也收低。

2026年6月29日的選擇權成交量中，0DTE佔比多少？

當日到期的選擇權佔總成交量66.33百萬口的35.8%；交易最活絡的單一合約，是一檔當日到期的SPY741美元買權（788,133口），最終價外失效。

為什麼選擇權在股市開盤前就開始交易？

只有指數選擇權如此。6月29日東部時間上午9:30之前的所有40,621筆選擇權成交，均為現金結算的指數類別（RUTW, SPX, SPXW, VIX, VIXW, XSP），這類產品提供延長的全球交易時段；在此期間，股票和ETF選擇權的成交筆數為0。

方法論

  • 時間戳記以 UTC 儲存，查詢時轉換為紐約時間。「收盤」指的是常規交易時段最後一分鐘的 K 線，而非盤後撮合價格；日別變動比較的是 6 月 29 日 與 6 月 26 日，並從觀察到的 K 線區間驗證該交易時段。
  • 滾動月份的排名使用常規交易時段的收盤價對收盤價變動，並剔除第一個交易時段（因該時段在觀察窗口內無前收盤價）。選擇權到期日則從 OCC 代碼重新解析（表格自有的到期日欄位為破損狀態）。
  • 每張面板均在撰稿當下，透過受控的唯讀路徑讀取一次。倉庫狀態截至 2026 年 7 月 13 日。

每張面板皆為一份已儲存的查詢結果——將圖表、表格及 SQL 整合於同一物件中。將任一結果貼入 Strasmore 終端機即可。下一交易時段：6 月 30 日。

#recap#daily#spy#qqq#market structure