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

2026年7月1日美股行情总结

下半年首个交易日指数走势平稳但个股波动剧烈，META领涨，多数标普行业基金收绿。

2026年7月1日（星期三）——下半年首个交易日——指数表现极其平稳，但个股波动剧烈。SPY收盘涨-0.08%，QQQ涨-1.44%。然而，第二季度的记忆复合物价值缩水了十分之一，META上涨8.88%，且7的11标普行业基金仍保持上涨。前一交易日：6月30日。

市场表现

变动数据对比 7 月 1 日常规交易时段最后一根 K 线与 6 月 30 日（周二）的数据。

查询SPY / QQQ / DIA / IWM — 7月1日对比6月30日收盘（常规交易时段）
每个数字背后的完整 SQL
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 >= '2026-06-30 13:30:00' AND window_start < '2026-06-30 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 >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 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_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 AS shares_traded_m
FROM sess s
JOIN prior p ON s.ticker = p.ticker
ORDER BY s.ticker

分化是核心特征：DIA 报收 0.02%，SPY 报收 -0.08%，表现持平；而 QQQ 报收 -1.44%，IWM 报收 -0.37%。面对增长股抛售，大盘波动极小。

过去一个月内波动最平稳的指数交易日之一

查询SPY 开盘至收盘涨跌幅在过去一个月交易中的排名（排名1 = 绝对涨跌幅最大）
每个数字背后的完整 SQL
SELECT round(anyIf(oc_pct, d = toDate('2026-07-01')), 2) AS day_move_pct,
       arrayCount(x -> x > abs(anyIf(oc_pct, d = toDate('2026-07-01'))), groupArrayIf(abs(oc_pct), d != toDate('2026-07-01'))) + 1 AS abs_move_rank,
       count() AS 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('2026-06-01 00:00:00')
      AND window_start < toDateTime('2026-07-02 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
    GROUP BY d
)

从日内振幅来看（不同于收盘价对比指标），SPY 变动了 0.09%。在过去 22 个交易日中，其波动幅度绝对值排名第 20 位，仅有两个交易日的波动更小。波动率相关品种走势一致。由于该平台不提供 VIX 指数现货数据，因此我们参考 VIX 期货基金，这是衡量对冲成本最接近的交易指标：

查询7月1日波动率组合：VIX 期货 ETF 对比周二收盘，以 SPY 为基准
每个数字背后的完整 SQL
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-01 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-01 00:00:00')) AS day_close,
        maxIf(toFloat64(high), window_start >= '2026-07-01 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-07-01 00:00:00') AS day_low
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'SVXY', 'UVXY', 'VIXY', 'VXX')
      AND ((window_start >= '2026-06-30 13:30:00' AND window_start < '2026-06-30 20:00:00')
        OR (window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 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,
    round((day_high / day_low - 1) * 100, 2) AS range_pct
FROM per_name
ORDER BY ticker

最大近月 VIX 期货基金 VXX 上涨 0.72%；VIXY 上涨 0.8%；杠杆基金 UVXY 在 3.68% 范围内波动，涨幅为 1.13%；反向基金 SVXY 下跌 -0.42%。其波动幅度远小于个股表现。

Breadth: more fell than rose — and more made new highs than new lows

查询7月1日成交额至少 $1M 的个股：上涨、下跌及季度新高/新低数量
每个数字背后的完整 SQL
WITH per_ticker AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-01 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-01 00:00:00')) AS day_close,
        maxIf(toFloat64(high), window_start < '2026-07-01 00:00:00') AS quarter_high,
        minIf(toFloat64(low), window_start < '2026-07-01 00:00:00') AS quarter_low,
        maxIf(toFloat64(high), window_start >= '2026-07-01 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-07-01 00:00:00') AS day_low,
        sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-01 00:00:00') AS day_dollar_volume,
        countIf(window_start < '2026-07-01 00:00:00') AS quarter_bars
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ((window_start >= '2026-04-01 13:30:00' AND window_start < '2026-06-30 20:00:00')
        OR (window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00'))
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
    GROUP BY ticker
)
SELECT
    countIf(day_close > prior_close AND day_dollar_volume >= 1000000) AS advancers,
    countIf(day_close < prior_close AND day_dollar_volume >= 1000000) AS decliners,
    countIf(day_close = prior_close AND day_dollar_volume >= 1000000) AS unchanged,
    round(100.0 * countIf(day_close > prior_close AND day_dollar_volume >= 1000000)
        / countIf(day_dollar_volume >= 1000000), 1) AS advancer_pct,
    countIf(day_dollar_volume >= 1000000 AND quarter_bars >= 1000 AND day_high > quarter_high) AS new_quarter_highs,
    countIf(day_dollar_volume >= 1000000 AND quarter_bars >= 1000 AND day_low < quarter_low) AS new_quarter_lows,
    round(countIf(day_dollar_volume >= 1000000 AND quarter_bars >= 1000 AND day_high > quarter_high)
        / countIf(day_dollar_volume >= 1000000 AND quarter_bars >= 1000 AND day_low < quarter_low), 1) AS highs_per_low,
    countIf(day_dollar_volume >= 1000000 AND quarter_bars >= 1000) AS names_with_a_full_quarter,
    countIf(day_dollar_volume >= 1000000) AS liquid_tickers,
    count() AS tickers_traded_both_sessions,
    count() - countIf(day_dollar_volume >= 1000000) AS dropped_by_liquidity_filter,
    countIf(day_dollar_volume >= 1000000 AND quarter_bars < 1000) AS dropped_short_quarter_history
FROM per_ticker
WHERE prior_close > 0 AND day_close > 0

2928 advancers, 3465 decliners, 82 unchanged — 45.2% of the liquid tape rose. But scope is not severity: against the second quarter's regular-hours range, 836 of the 6217 names with a complete quarter of trading printed a new quarterly high on this red day, against 246 printing a new low — 3.4 new highs per new low. Most slipped; few broke their range. Drops: 5435 of 11910 dual-session tickers under the $1 million filter, 258 liquid names without a full quarter of bars.

板块表现：损失仅限于单一板块

11 只 SPDR 板块基金是市场最廉价的全市场横截面数据——每交易日构成相同。

查询7月1日 11 只标普行业 ETF：对比周二收盘的表现排名（由优至劣）
每个数字背后的完整 SQL
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-01 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-01 00:00:00')) AS day_close,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-01 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
      AND ((window_start >= '2026-06-30 13:30:00' AND window_start < '2026-06-30 20:00:00')
        OR (window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 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,
    round((day_close / prior_close - 1) * 100 - min((day_close / prior_close - 1) * 100) OVER (), 2) AS pct_above_worst_sector,
    sum(if(day_close > prior_close, 1, 0)) OVER (ORDER BY (day_close / prior_close) DESC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS green_so_far,
    day_dollar_bn
FROM per_name
ORDER BY pct_chg DESC

持有 META 的通信服务板块 (XLC) 领涨，涨幅为 2.41%；金融板块 (XLF) 以 2.2% 紧随其后。科技板块 (XLK) 表现最差，分别比领涨板块低 -2.56% 和 4.97 个百分点。总体而言，7 的 11 只基金收涨——包括可选消费 0.68% 和医疗保健 0.57%——而公用事业 (-1.28)、工业 (-0.98) 和能源 (-0.6) 则收跌。这意味着指数在板块波动下几乎保持不变。

今日亮点：存储股走低，META 反向走高

市场呈现显著分化，涉及八只股票：四只第二季度领涨的存储股，以及与其相关的四只芯片与平台股。本文报告了它们的联动性、波动幅度及时间点，并不解释原因。

查询记忆组合与巨头股：对比周二收盘的涨跌幅、区间时机及成交金额
每个数字背后的完整 SQL
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-01 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-01 00:00:00')) AS day_close,
        maxIf(toFloat64(high), window_start >= '2026-07-01 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-07-01 00:00:00') AS day_low,
        argMinIf(window_start, (toFloat64(low), toInt64(toUnixTimestamp(window_start))), window_start >= '2026-07-01 00:00:00') AS low_bar,
        argMaxIf(window_start, (toFloat64(high), -toInt64(toUnixTimestamp(window_start))), window_start >= '2026-07-01 00:00:00') AS high_bar,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-01 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AMD', 'META', 'MRVL', 'MU', 'NVDA', 'SNDK', 'STX', 'WDC')
      AND ((window_start >= '2026-06-30 13:30:00' AND window_start < '2026-06-30 20:00:00')
        OR (window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 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,
    formatDateTime(toTimeZone(high_bar, 'America/New_York'), '%H:%i') AS day_high_et,
    formatDateTime(toTimeZone(low_bar, 'America/New_York'), '%H:%i') AS day_low_et,
    toHour(toTimeZone(low_bar, 'America/New_York')) * 60 + toMinute(toTimeZone(low_bar, 'America/New_York')) AS low_minute_et,
    round((day_high / day_low - 1) * 100, 2) AS range_pct,
    day_dollar_bn
FROM per_name
ORDER BY ticker

SanDisk (-10.45%) 和 Micron (-10.23%) 跌幅均超过 10%；Marvell -8.68%、AMD -6.87%、Western Digital -6.3% 和 Seagate -5.11% 亦有波动。MU 成交额达 $42.94 billion，超过了 SPY 全天的成交额。MU、AMD 和 Marvell 分别在收盘前几分钟创下全天低点 (15:59, 15:57, 15:58 ET) —— 抛售发生在 收盘竞价 阶段，而非早盘后的反弹缺口（MU 深度分析 显示该季度已结束）。

META 的走势截然相反：成交额为 8.88%，金额为 $22.72 billion，全天低点出现在 09:30 ET（开盘时），且随后未再触及。NVDA 波动极小，仅为 -1.09% (NVDA 六月行情)。

资讯摘要

本页面不判定因果关系。我们的授权新闻源仅作为数据记录——以下是当日内容：

查询当日提及 MU, SNDK, STX, WDC 或 META 的最后 12 篇新闻（授权数据源）
每个数字背后的完整 SQL
SELECT published_et, publisher, names_tagged, headline
FROM (
    SELECT
        published_utc,
        formatDateTime(toTimeZone(published_utc, 'America/New_York'), '%H:%i') AS published_et,
        JSONExtractString(publisher, 'name') AS publisher,
        arrayStringConcat(arrayFilter(x -> x IN ('MU', 'SNDK', 'STX', 'WDC', 'META'), tickers), ' ') AS names_tagged,
        substring(title, 1, 72) AS headline
    FROM global_markets.stocks_news
    WHERE published_utc >= '2026-07-01 04:00:00' AND published_utc < '2026-07-02 04:00:00'
      AND hasAny(tickers, ['MU', 'SNDK', 'STX', 'WDC', 'META'])
      AND NOT has(tickers, 'SPCX')
      AND position(title, 'SPCX') = 0
    ORDER BY published_utc DESC
    LIMIT 12
)
ORDER BY published_utc ASC

The Motley Fool 在午后发布的关于 Micron (14:11 ET) 的文章标题为“Why Micron Stock Is Plummeting Today”。其晚间总结 (17:12 ET) 以及四分钟后的后续报道 (17:16 ET) 均将 META 的股价上涨归因于据称将其多余的 AI 算力作为云业务出售的计划。这仅是该媒体的解读，而非市场行情本身。12 来自两家出版商的文章仅代表单一新闻源的关注点，并不代表市场趋势。

资金流向

查询成交量领头羊：成交金额前 6 名及成交股数前 4 名（排除待核实重复代码）
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 >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 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(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 >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00'
      AND ticker NOT IN ('SPCX')
    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

MU 以 $42.94 十亿 的成交额领涨 —— SPY 以 $27.76 十亿位居第二，META 以 $22.72 十亿位居第四 —— 一只下跌的股票成交额超过了该指数基金的 1.5 倍。市场波动最剧烈的股票也是成交最活跃的股票。股票成交量呈现出极端的分布：3 倍做空半导体 ETF (SOXS) 以 553.1 百万股的成交量领跑 —— 这是芯片板块走弱时最活跃的品种 —— 而一只低价股以 189.8 百万股、价值 $319 百万的成交量收尾（相对成交量 准确反映了这一点）。数据基准：7 月 1 日常规交易时段，已排除重复代码的上市项目 (详情)。

期权交易盘面

查询当日期权概览：成交量、当日到期、看跌/看涨偏度及最活跃合约
每个数字背后的完整 SQL
WITH
    (
        SELECT (any(underlying_symbol), any(toFloat64(strike_price)), any(option_type),
                sum(size), round(avg(toFloat64(price)), 3),
                any(if(substring(ticker, length(ticker) - 14, 6) = '260701', 1, 0)))
        FROM global_markets.options_trades
        WHERE sip_timestamp >= '2026-07-01 00:00:00' AND sip_timestamp < '2026-07-02 00:00:00'
        GROUP BY ticker
        ORDER BY sum(size) DESC, ticker ASC
        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 >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 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(sumIf(toFloat64(size), option_type = 'P') / sumIf(toFloat64(size), option_type = 'C'), 2) AS put_call_ratio,
    round(100.0 * sumIf(size, substring(ticker, length(ticker) - 14, 6) = '260701') / 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,
    round(toFloat64(sumIf(size, underlying_symbol = 'SPY' AND option_type = 'P')) / 1e6, 2) AS spy_put_contracts_m,
    round(100.0 * sumIf(size, underlying_symbol = 'SPY' AND option_type = 'P' AND toFloat64(strike_price) < spy_regular_close * 0.98)
        / sumIf(size, underlying_symbol = 'SPY' AND option_type = 'P'), 1) AS spy_puts_2pct_below_pct,
    round(100.0 * sumIf(size, underlying_symbol = 'SPY' AND option_type = 'C' AND toFloat64(strike_price) > spy_regular_close * 1.02)
        / sumIf(size, underlying_symbol = 'SPY' AND option_type = 'C'), 1) AS spy_calls_2pct_above_pct,
    round(sumIf(toFloat64(size), underlying_symbol = 'MU' AND option_type = 'P')
        / sumIf(toFloat64(size), underlying_symbol = 'MU' AND option_type = 'C'), 2) AS mu_put_call_ratio,
    round(sumIf(toFloat64(size), underlying_symbol = 'META' AND option_type = 'P')
        / sumIf(toFloat64(size), underlying_symbol = 'META' AND option_type = 'C'), 2) AS meta_put_call_ratio,
    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_volume,
    reverse(arrayStringConcat(extractAll(reverse(toString(assumeNotNull(top_contract.4))), '\\d{1,3}'), ',')) AS top_contract_volume_fmt,
    round(top_contract.5, 3) AS top_contract_avg_price,
    top_contract.6 AS top_contract_is_same_day,
    round(top_contract.2 - spy_regular_close, 2) AS top_strike_minus_spy_close,
    spy_regular_close AS spy_close
FROM global_markets.options_trades
WHERE sip_timestamp >= '2026-07-01 00:00:00' AND sip_timestamp < '2026-07-02 00:00:00'

期权成交量为 11.22 百万份，成交笔数为 68.46 百万份。其中看涨期权占成交量的 58.6%，当日到期期权占 33.5%。市场并未对指数进行对冲：全市场看跌期权与看涨期权比例为 0.71；在 SPY 的 5.76 百万份看跌期权中，仅有 11.6% 的行权价低于其 745.69 收盘价超过 2% —— 即仅在市场崩盘时才行权的深度虚值期权。个股偏斜（skew）特征明显：Micron 的看跌期权数量超过看涨期权（比例为 1.11）；而 META 的看涨期权与看跌期权比例超过 2:1 (0.39)。交易最活跃的合约是当日到期的 SPY $748 看涨期权 —— 成交 878,947 份，平均价格为 $0.707 —— 到期时处于价外 2.31 美元，正如周二的情况。全天未见 7 月 3 日到期的期权成交（0 笔 —— 该周五已休市）；周四到期的周权成交量为 14.43 百万份。

利率：整体走高，短端走低

收益率对比 6 月 30 日收盘价。

查询国债收益率曲线：7月1日收盘对比6月30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 jul1_yield_pct,
    round((t.2 - t.3) * 100) AS one_day_change_bp
FROM (
    SELECT arrayJoin([
        ('1 month',  toFloat64(d.yield_1_month),  toFloat64(p.yield_1_month)),
        ('3 month',  toFloat64(d.yield_3_month),  toFloat64(p.yield_3_month)),
        ('1 year',   toFloat64(d.yield_1_year),   toFloat64(p.yield_1_year)),
        ('2 year',   toFloat64(d.yield_2_year),   toFloat64(p.yield_2_year)),
        ('5 year',   toFloat64(d.yield_5_year),   toFloat64(p.yield_5_year)),
        ('10 year',  toFloat64(d.yield_10_year),  toFloat64(p.yield_10_year)),
        ('30 year',  toFloat64(d.yield_30_year),  toFloat64(p.yield_30_year)),
        ('2s10s spread', toFloat64(d.yield_10_year - d.yield_2_year), toFloat64(p.yield_10_year - p.yield_2_year))
    ]) AS t
    FROM (SELECT * FROM global_markets.treasury_yields WHERE date = '2026-07-01') AS d,
         (SELECT * FROM global_markets.treasury_yields WHERE date = '2026-06-30') AS p
)

10 年期国债收益率上涨 4 bp 至 4.48%，30 年期上涨 6 bp；1 个月期国库券变动 -3 bp。中长期收益率上升，短端收益率下降。2s10s 利差收于 0.31 个百分点：该指标表现与股票离散度并不匹配。

The calendar behind the day

查询7月1日公司日历与信息流
每个数字背后的完整 SQL
WITH
    (
        SELECT (count(), uniqExact(publisher))
        FROM global_markets.stocks_news
        WHERE toDate(toTimeZone(published_utc, 'America/New_York')) = '2026-07-01'
    ) AS news,
    (
        SELECT (argMax(t, n), 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')) = '2026-07-01'
            )
            WHERE t != 'SPCX'
            GROUP BY t
        )
    ) AS top_news
SELECT
    (SELECT count() FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-01') AS ex_dividend_records,
    (SELECT countIf(frequency = 12) FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-01') AS monthly_payers,
    (SELECT countIf(frequency = 4) FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-01') AS quarterly_payers,
    (SELECT round(100.0 * countIf(frequency = 12) / count(), 1) FROM global_markets.stocks_dividends WHERE ex_dividend_date = '2026-07-01') AS monthly_payer_pct,
    (SELECT count() FROM global_markets.stocks_splits WHERE execution_date = '2026-07-01') AS splits_executed,
    (SELECT count() FROM global_markets.stocks_ipos WHERE listing_date = '2026-07-01') AS ipos_listed,
    (SELECT uniqExact(accession_number) FROM global_markets.stocks_sec_edgar_index WHERE filing_date = '2026-07-01') AS sec_filings,
    (SELECT uniqExactIf(accession_number, form_type = '4') FROM global_markets.stocks_sec_edgar_index WHERE filing_date = '2026-07-01') AS insider_form4_filings,
    (SELECT uniqExactIf(accession_number, form_type = '8-K') FROM global_markets.stocks_sec_edgar_index WHERE filing_date = '2026-07-01') AS filings_8k,
    (SELECT arrayStringConcat(groupArray(concat(ticker, ' — ', issuer_name)), '; ') FROM (
        SELECT ticker, issuer_name FROM global_markets.stocks_ipos WHERE listing_date = '2026-07-01' ORDER BY ticker
    )) AS ipo_names,
    news.1 AS news_articles,
    news.2 AS news_publishers,
    top_news.1 AS most_covered_ticker,
    top_news.2 AS most_covered_articles

The quarter turn crested here: 745 dividend records went ex-dividend — the wave the June recap watched building — alongside 9 splits and 3 listings (BSP — Bending Spoons S.p.A.; ITG — ITG Inc.; LIME — Neutron Holdings Inc.). The filing feed revived: 4282 filings, 1072 of them Form 4s and 248 8-Ks, after June 30's near-empty index day (the month-end gap note). Our feed carried 201 articles, most-covered MSFT at 16.

But a count says nothing about who was paying. Ranked by the money that traded in them, the board is not corporate at all:

查询7月1日除权个股：按当日成交金额排名
每个数字背后的完整 SQL
WITH divs AS (
    SELECT ticker, max(toFloat64(cash_amount)) AS cash, max(frequency) AS freq
    FROM global_markets.stocks_dividends
    WHERE ex_dividend_date = '2026-07-01' AND distribution_type = 'recurring'
    GROUP BY ticker
),
tape AS (
    SELECT ticker,
           sum(toFloat64(close) * toFloat64(volume)) AS dollar_volume,
           toFloat64(argMax(close, (window_start, close))) AS day_close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00'
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
)
SELECT
    d.ticker AS ticker,
    round(d.cash, 4) AS cash_per_share,
    d.freq AS payments_per_year,
    round(t.day_close, 2) AS day_close,
    round(100 * d.cash / t.day_close, 2) AS pct_of_price,
    round(100 * d.cash * d.freq / t.day_close, 2) AS annualized_yield_pct,
    round(t.dollar_volume / 1e9, 2) AS day_dollar_bn
FROM divs d JOIN tape t ON d.ticker = t.ticker
WHERE t.dollar_volume > 0 AND d.cash > 0
ORDER BY t.dollar_volume DESC
LIMIT 8

Every one of the eight busiest ex-dividend tickers is a fund paying 12 times a year: SGOV, a Treasury-bill fund, went ex on $0.2958 a share (0.29% of price, 3.54% annualized) on $3.5 billion traded; HYG, a high-yield bond fund, on $0.3688. That is a first-of-month wave: 572 of the day's 745 records (76.8%) are monthly payers — funds distributing interest — against 123 quarterly payers. The ex-date drop is mechanical, not a loss.

季度首日会是平庸的一天吗？

市场逻辑通常认为：新季度意味着新资金和新配置。但数据库的数据并不支持这一观点。

查询2004年以来每个季度开盘交易日：SPY 开盘至收盘涨跌幅（含7月1日排名）
每个数字背后的完整 SQL
SELECT
    count() AS quarter_opens_measured,
    round(quantileExact(0.5)(oc_pct), 2) AS median_quarter_open_pct,
    countIf(oc_pct > 0) AS quarter_opens_green,
    countIf(oc_pct <= 0) AS quarter_opens_red,
    round(anyIf(oc_pct, first_day = toDate('2026-07-01')), 2) AS jul1_oc_pct,
    arrayCount(x -> x < anyIf(oc_pct, first_day = toDate('2026-07-01')), groupArrayIf(oc_pct, first_day != toDate('2026-07-01'))) + 1 AS jul1_rank_worst_to_best,
    round(min(oc_pct), 2) AS worst_quarter_open_pct,
    round(max(oc_pct), 2) AS best_quarter_open_pct,
    concat(monthName(min(first_day)), ' ', toString(toYear(min(first_day)))) AS first_measured
FROM (
    SELECT f.first_day AS first_day, d.oc_pct AS oc_pct
    FROM (
        SELECT toStartOfQuarter(d) AS q, min(d) AS first_day
        FROM (
            SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d
            FROM global_markets.delayed_stocks_minute_aggs
            WHERE ticker = 'SPY'
              AND window_start >= toDateTime('2004-01-01 00:00:00')
              AND window_start < toDateTime('2026-07-02 00:00:00')
              AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
            GROUP BY d
        )
        GROUP BY q
    ) f
    INNER JOIN (
        SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
               (argMax(toFloat64(close), (window_start, close)) / argMin(toFloat64(open), (window_start, open)) - 1) * 100 AS oc_pct
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY'
          AND window_start >= toDateTime('2004-01-01 00:00:00')
          AND window_start < toDateTime('2026-07-02 00:00:00')
          AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
        GROUP BY d
    ) d ON d.d = f.first_day
)

自 January 2004 以来，在 91 个季度开盘交易日中，SPY 在季度首日的日内中位数涨跌幅为 0.01% —— 其中 46 个交易日上涨，45 个交易日下跌，涨跌幅区间为 -2.28% 至 3.18%：这几乎等同于抛硬币。7 月 1 日的表现为 0.09%，在 91 个样本中排名第 50（从最差到最好排序）—— 处于中位数水平。统计依据：数据库中 SPY 每一季度的常规交易时段开盘至收盘数据。

市场总结

指数波动了 0.09%；八只核心权重股的涨跌幅介于 -10.45% 至 8.88% 之间；避险情绪几乎没有变化。这是高度集中型市场的常态——指数的净值是由单一板块的亏损与 7 的 11 其他板块的涨幅相互抵消后得出的。从指数层面看，市场并无异常；但从持仓层面看，单日内某项持仓的价值缩水了十分之一。

交易时段核实

查询交易时段检查：SPY 观察到的分钟线跨度
每个数字背后的完整 SQL
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,
    count() AS spy_minute_bars,
    countIf(window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00') AS regular_session_bars,
    uniqExactIf(toDate(toTimeZone(window_start, 'America/New_York')), window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00') AS day_sessions,
    (SELECT count() FROM global_markets.stocks_market_holidays WHERE date = '2026-07-01') AS jul1_holiday_rows
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY' AND window_start >= '2026-07-01 00:00:00' AND window_start < '2026-07-02 00:00:00'

SPY 的 K 线运行于纽约时间 04:00 至 19:59。该日期包含 390 根常规交易时段 K 线和 0 根节假日行——已根据行情数据核实，交易时段完整。

常见问题

2026年7月1日股市上涨还是下跌了？

这取决于您观察的标的。SPY 收盘上涨 -0.08%，DIA 上涨 0.02%，QQQ 上涨 -1.44%；3465 类型的股票下跌，而 2928 上涨——尽管 7 的 11 板块基金收盘上涨，但科技板块表现异常。

为什么 Micron、SanDisk、Western Digital 和 Seagate 在2026年7月1日全部下跌？

此数据反映的是联动性而非原因：这四家公司跌幅在 -5.11% 至 -10.45% 之间。The Motley Fool 当日关于 Micron 的文章（见本页新闻面板，14:11 ET）标题为“Why Micron Stock Is Plummeting Today”。这是该媒体的观点，并非市场行情本身得出的结论。

为什么 META 在2026年7月1日上涨？

META 收盘上涨 8.88%，其最低点出现在开盘柱线。The Motley Fool 的报道将其归因于公司计划将多余的 AI 计算能力作为云业务出售的传闻：这是该媒体的归因，而非我们的结论。

季度第一个交易日通常是强势交易日吗？

没有显著特征。自 January 2004 以来的 91 个季度开盘日中，SPY 的中位数日内涨跌幅为 0.01%：46 为上涨，45 为下跌——概率相当；7月1日的表现处于中等水平。

期权市场在2026年7月1日对抛售进行了对冲吗？

在指数层面并未对冲：全市场看跌期权与看涨期权的比例为 0.71，且 SPY 仅有 11.6% 的看跌期权成交量在收盘价基础上下跌超过了 2%。

数据说明

  • 美元成交量为每分钟代理指标 —— 指常规交易时间内，每分钟 K 线收盘价乘以成交量之和。显示的极值已与相邻 K 线进行核对；若数值相同，以最早出现的为准。
  • 此处不提供现货 VIX 指数。 波动率面板显示的是上市的 VIX 期货基金，这些基金持有并滚动期货合约 —— 与指数相关，但并不等同。
  • 季度新高/新低 —— 将 7 月 1 日的常规交易时段极值，与具有完整季度数据的流动性股票在 4 月 1 日至 6 月 30 日期间的波动范围进行比较；跌幅显示在面板中。
  • 板块面板为精选篮子（包含 11 只 SPDR ETF，而非供应商字段）；新闻面板为单一授权数据源（包含 12 篇文章，来自两家发行商）；排行榜已排除一个重复代码的列表（查看其收据）。

方法论

  • 一个交易时段（根据观察到的 K 线和节假日表验证的 1 时段，绝不凭空假设）。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根分钟 K 线，日期变更以 6 月 30 日为准。
  • 在进行比例运算前，将小数转换为 64 位浮点数；期权到期日根据 OCC 代码重新解析。面板在撰写时通过受限的只读路径运行一次。数据仓库状态截至 2026 年 7 月 13 日；本版本新增了板块、波动率、新闻、股息和季度历史面板。

每个面板均为存储的查询结果——图表、表格和 SQL 是同一个对象。您可以将任何内容粘贴到 Strasmore 终端。下一期：7 月 2 日；本周主题：节假日周。

#recap#daily#spy#qqq#market structure