Strasmore Research
Deep Dives · Matt ConnorBy Matt Connor · · Updated 2026-07-23

MU: Micron 2026年6月成交额

Micron 6月常规交易额达 995.7 十亿美元，位列市场 first 名，且调价幅度降至 14，点击查看详细数据。

Micron 在 2026 年上半年经历了剧烈的价格重估。6 月份股价趋于平稳，但成交量依然活跃。MU 6 月收盘价为 $1151.01，上涨 14%。这是自 2 月以来最平稳的月份，此前 5 月份波动幅度为 89.8%。其常规交易时段成交额达 $995.7 billion，在全美股市中排名 first，甚至超过了 SPY。若按日计算，6 月也是上半年波动最大的月份（年化波动率 127.6%）：表面的平静只是假象。以下是 6 月份的数据汇总——包括成交量、财报周备案、波动率、同行表现、价差、期权、空头以及半年趋势。每个数字均可查询；点击任何面板即可查看其 SQL 语句。

月度走势概览

查询MU 6月概览：开盘、收盘、极值、成交额及收益
每个数字背后的完整 SQL
WITH
    (
        SELECT count() FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'SPY'
          AND window_start >= toDateTime('2026-06-19 00:00:00') AND window_start < toDateTime('2026-06-20 00:00:00')
    ) AS spy_jun19,
    (
        SELECT max(toFloat64(high)) FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'MU'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    ) AS hi,
    (
        SELECT min(toFloat64(low)) FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'MU'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    ) AS lo
SELECT
    round(argMinIf(toFloat64(open), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199), 2) AS month_open,
    round(argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199), 2) AS month_close,
    round((argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / argMinIf(toFloat64(open), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) - 1) * 100, 1) AS month_return_pct,
    round(hi, 2) AS month_high,
    formatDateTime(toTimeZone(minIf(window_start, toFloat64(high) >= hi - 0.011), 'America/New_York'), '%Y-%m-%d %H:%i') AS month_high_first_bar_et,
    round(lo, 2) AS month_low,
    formatDateTime(toTimeZone(minIf(window_start, toFloat64(low) <= lo + 0.011), 'America/New_York'), '%Y-%m-%d %H:%i') AS month_low_first_bar_et,
    round(minIf(toFloat64(low), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) - lo, 2) AS rth_minus_extended_low,
    round(toFloat64(sum(volume)) / 1e9, 2) AS month_shares_bn,
    round(sumIf(toFloat64(close) * toFloat64(volume), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / 1e9, 1) AS rth_dollar_bn,
    uniqExact(toDate(toTimeZone(window_start, 'America/New_York'))) AS session_days_observed,
    spy_jun19 AS spy_bars_june19
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'MU'
  AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')

对于该规模的股票而言，月内波动范围极大：最低为 $850.1 (2026-06-05 16:34 ET)，最高为 $1255 (2026-06-25 09:35 ET) —— 波动幅度超过了大多数股票的全年区间。由于 6 月 19 日市场全天休市 (0 SPY bars)，本月共交易了 21 个交易日。

六月走势与半年趋势对比

+14% 的涨幅大吗？对于该股票而言，本半年度表现平稳。面板每月实时重新计算。

查询MU 月度实时重算：收益率、收盘价及常规交易时段成交额
每个数字背后的完整 SQL
SELECT toString(toStartOfMonth(toDate(toTimeZone(window_start, 'America/New_York')))) AS period_start,
    round((argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) / argMinIf(toFloat64(open), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) - 1) * 100, 1) AS month_return_pct,
    round(argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959), 2) AS month_close,
    round(sumIf(toFloat64(close) * toFloat64(volume), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) / 1e9, 1) AS rth_dollar_bn
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'MU'
  AND window_start >= toDateTime('2026-01-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
GROUP BY period_start
ORDER BY period_start

趋势走势：一月为 40.5，二月持平于 0，三月为 -15.9，并创下半年收盘低点 ($337.62)，随后四月为 48.4，五月为 89.8 —— 半年内价格区间为 $414.73 至 $1151.01。成交额随之上升：从一月的 235.8 十亿美元增至六月的 995.7 十亿美元。六月的价格波动是自二月以来的最小波动；但六月的成交量是该周期内的最大值。

价格走势趋于平缓，但波动率并未消退。

14% 的月份会平稳吗？从年化后的日收盘价波动率来看，6 月的表现截然不同。

查询MU 月度数据：年化实现波动率、平均日内振幅及最大单日波动
每个数字背后的完整 SQL
SELECT
    toString(toStartOfMonth(et_date)) AS period_start,
    round(stddevSamp(log_ret) * sqrt(252) * 100, 1) AS realized_vol_ann_pct,
    round(100 * stddevSamp(log_ret) / max(stddevSamp(log_ret)) OVER (), 1) AS pct_of_peak_vol,
    round(avg(day_range_pct), 1) AS avg_day_range_pct,
    round(max(abs(day_move_pct)), 1) AS biggest_day_move_pct,
    count() AS return_days
FROM (
    SELECT et_date, day_range_pct,
        if(prev_close > 0, ln(close_usd / prev_close), NULL) AS log_ret,
        if(prev_close > 0, (close_usd / prev_close - 1) * 100, NULL) AS day_move_pct
    FROM (
        SELECT et_date, close_usd, day_range_pct,
            lagInFrame(close_usd) OVER (ORDER BY et_date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
        FROM (
            SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
                argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS close_usd,
                (maxIf(toFloat64(high), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) / minIf(toFloat64(low), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) - 1) * 100 AS day_range_pct
            FROM global_markets.delayed_stocks_minute_aggs
            WHERE ticker = 'MU'
              AND window_start >= toDateTime('2026-01-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
            GROUP BY et_date
        )
    )
)
WHERE isFinite(log_ret) AND log_ret IS NOT NULL
GROUP BY period_start
ORDER BY period_start

按此指标衡量，6 月是上半年波动最剧烈的月份：年化波动率达 127.6%，甚至超过了 5 月的 101.4%；日均波动幅度为 7.6%，且出现了一次 15.8% 的收盘价变动。期初与期末表现平稳，但期中波动剧烈。

交易时段回顾

查询21个交易日：收盘价、环比变化及全天成交量
每个数字背后的完整 SQL
SELECT et_date, close_usd,
    round(if(prev_close = 0, NULL, (close_usd / prev_close - 1) * 100), 1) AS change_pct,
    shares_m
FROM (
    SELECT et_date, close_usd, shares_m,
           lagInFrame(close_usd) OVER (ORDER BY et_date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
    FROM (
        SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
            round(argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199), 2) AS close_usd,
            round(toFloat64(sum(volume)) / 1e6, 1) AS shares_m
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'MU'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
        GROUP BY et_date
    )
)
ORDER BY et_date

行情波动剧烈——从 2026-06-01 的 $1034.83 震荡至最后一个交易日的 $1151.01。这掩盖了“月涨幅 +14%”背后的真相：仅在期初和期末表现平稳。接下来的财报周将迎来最剧烈的波动。

股价波动是否与财报发布时间吻合？

通过对比 SEC 指数中的 MU 六月文件与当时的交易记录，可以确定具体日期。

查询MU 6月SEC文件及季度8-K公告后的交易日
每个数字背后的完整 SQL
WITH
    (
        SELECT maxIf(filing_date, form_type = '8-K')
        FROM global_markets.stocks_sec_edgar_index
        WHERE cik = '0000723125' AND filing_date >= toDate('2026-06-01') AND filing_date <= toDate('2026-06-30')
    ) AS last_8k_date,
    (
        SELECT minIf(filing_date, form_type = '10-Q')
        FROM global_markets.stocks_sec_edgar_index
        WHERE cik = '0000723125' AND filing_date >= toDate('2026-06-01') AND filing_date <= toDate('2026-06-30')
    ) AS tenq_date,
    (
        SELECT (count(), countIf(form_type = '8-K'), countIf(form_type = '4'))
        FROM global_markets.stocks_sec_edgar_index
        WHERE cik = '0000723125' AND filing_date >= toDate('2026-06-01') AND filing_date <= toDate('2026-06-30')
    ) AS filing_census,
    (
        SELECT toDate(toTimeZone(min(window_start), 'America/New_York'))
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE ticker = 'MU'
          AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
          AND toFloat64(high) >= (
              SELECT max(toFloat64(high)) FROM global_markets.delayed_stocks_minute_aggs
              WHERE ticker = 'MU' AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
          ) - 0.011
    ) AS month_high_date
SELECT
    toString(last_8k_date) AS earnings_8k_filed,
    if(last_8k_date = toDate('2026-06-24'), 1, 0) AS eightk_on_jun24,
    toString(tenq_date) AS tenq_filed,
    dateDiff('day', last_8k_date, tenq_date) AS days_8k_to_10q,
    filing_census.1 AS june_filings_total,
    filing_census.2 AS eightk_filings,
    filing_census.3 AS form4_filings,
    round((anyIf(close_usd, et_date = toDate('2026-06-25')) / anyIf(close_usd, et_date = toDate('2026-06-24')) - 1) * 100, 1) AS next_session_move_pct,
    round(anyIf(shares_m, et_date = toDate('2026-06-25')), 1) AS next_session_shares_m,
    round(anyIf(shares_m, et_date = toDate('2026-06-25')) - maxIf(shares_m, et_date != toDate('2026-06-25')), 1) AS shares_lead_over_next_busiest_m,
    if(month_high_date = toDate('2026-06-25'), 1, 0) AS month_high_on_jun25
FROM (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
        argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) AS close_usd,
        toFloat64(sum(volume)) / 1e6 AS shares_m
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'MU'
      AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    GROUP BY et_date
)

MU 的季度 8-K 文件日期为 2026-06-24，随后的 10-Q 文件日期为 1（8 月份文件，3 内部人士 Form 4 文件）。在 8-K 发布后的交易日，成交量达到 77.2 百万股，收盘上涨 15.8%。这是该月交易量最大的交易日，比次高交易量多出 5.9 百万股。该交易日也创下了当月 1255 美元的最高价（2026-06-25 09:35 ET；成交量列数据证实了这一点）。本文仅指出这一时间吻合的现象。

是美光的故事，还是存储板块的故事？

存储与闪存板块的其他股票也随之波动了吗？参考篮子已确定：将 6 月份与 MU 关联度最高的三个存储股作为核心，并以 NVDA 和 SPY 作为基准。

查询6月对比：存储板块、NVDA 及 SPY
每个数字背后的完整 SQL
WITH (
    SELECT (argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / argMinIf(toFloat64(open), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) - 1) * 100
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'MU' AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
) AS mu_return
SELECT ticker,
    round(june_return_pct, 1) AS june_return_pct,
    round(june_return_pct - mu_return, 1) AS vs_mu_pct_points,
    rth_dollar_bn,
    round(100 * rth_dollar_bn / max(rth_dollar_bn) OVER (), 1) AS pct_of_top_turnover
FROM (
    SELECT ticker,
        (argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / argMinIf(toFloat64(open), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) - 1) * 100 AS june_return_pct,
        round(sumIf(toFloat64(close) * toFloat64(volume), (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199) / 1e9, 1) AS rth_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('MU', 'SNDK', 'WDC', 'INTC', 'NVDA', 'SPY')
      AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
    GROUP BY ticker
)
ORDER BY june_return_pct DESC

这是一场存储板块的行情，而 MU 表现落后。SNDK 上涨了 31.3%，INTC 上涨了 27.5%，WDC 上涨了 19.1% —— 所有存储股的涨幅均超过 MU 5.1 至 17.3 个百分点 —— 而 SPY 和 NVDA 在 6 月份收跌。MU 的特点在于：其成交额位居榜首。

交易量最大的股票

查询全美市场排名（按2026年6月常规交易时段美元成交额）：排除一个待核实实体
每个数字背后的完整 SQL
SELECT ticker,
    round(sum(toFloat64(volume) * toFloat64(close)) / 1e9, 1) AS regular_hours_dollar_bn,
    round(100 * sum(toFloat64(volume) * toFloat64(close)) / max(sum(toFloat64(volume) * toFloat64(close))) OVER (), 1) AS pct_of_leader
FROM global_markets.delayed_stocks_minute_aggs
WHERE window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
  AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
  AND ticker NOT IN ('SPCX')
GROUP BY ticker
ORDER BY regular_hours_dollar_bn DESC
LIMIT 10
查询排名详情：MU排名、领先下一名的幅度及依据
每个数字背后的完整 SQL
WITH (
    SELECT sum(toFloat64(volume) * toFloat64(close))
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'MU'
      AND window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
) AS mu_d
SELECT
    countIf(d > mu_d AND ticker != 'MU') + 1 AS mu_rank,
    round(mu_d / 1e9, 1) AS mu_dollar_bn,
    round((mu_d - maxIf(d, d < mu_d AND ticker != 'MU')) / 1e9, 1) AS lead_over_next_bn
FROM (
    SELECT ticker, sum(toFloat64(volume) * toFloat64(close)) AS d
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= toDateTime('2026-06-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
      AND (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199
      AND ticker NOT IN ('SPCX')
    GROUP BY ticker
)

截至 6 月常规交易时段，MU 的美元成交额在所有美股上市公司中排名第 first —— 高达 $995.7 十亿，仅次于成交额为 $224.3 十亿的 SPY。单家公司的月度成交额超过旗舰指数基金，这在市场结构中非常罕见；上述财报周及同业板块的数据提供了背景参考。数据统计范围为 6 月 1 日至 30 日的常规交易时段，已排除一个待身份核实的重复上市代码（查看详情）。市场整体概况请见 6 月回顾。

交易数据构成

查询MU 全天交易概览：成交笔数、成交规模及报价统计
每个数字背后的完整 SQL
WITH
    (
        SELECT (round(count() / 1e6, 2),
                round(100.0 * countIf(bid_price > 0 AND ask_price > 0 AND ask_price > bid_price) / count(), 2),
                countIf(bid_price > 0 AND ask_price > 0 AND ask_price < bid_price),
                countIf(bid_price <= 0 OR ask_price <= 0))
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'MU'
          AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
    ) AS quote_census
SELECT
    round(count() / 1e6, 2) AS prints_m,
    quantileDeterministic(0.5)(toFloat64(size), toUInt64(abs(sequence_number))) AS median_print_shares,
    round(avg(toFloat64(size)), 1) AS avg_print_shares,
    round(100.0 * countIf(size < 100) / count(), 1) AS odd_lot_pct_of_prints,
    round(100.0 * countIf(toFloat64(size) != round(toFloat64(size))) / count(), 2) AS fractional_pct_of_prints,
    quote_census.1 AS nbbo_updates_m,
    quote_census.2 AS clean_two_sided_pct,
    quote_census.3 AS crossed_updates,
    quote_census.4 AS one_sided_or_empty_updates
FROM global_markets.stocks_trades
WHERE ticker = 'MU'
  AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)

35.17 百万笔成交，中位数成交量为 8 股 —— 以及 91.7% 的零股交易。这是我们观测到的零售参与度最高的交易记录之一（NVDA 6 月的数据显著较低；深度分析报告 包含对比图表）。在股价达到四位数的情况下，“标准手数”涉及金额巨大，因此小额成交在结构上占据主导；碎股交易（9.04%）也反映了这一趋势。报价端情况：19.64 百万次 NBBO 更新，99.86% 为双向报价，16963 次交叉报价，78 次单向或空报价 —— 以上均为已披露数据，而非遗漏。

重新定价期间的价差

单季度涨幅接近三倍的股票，其价差问题更为特殊：在股价寻找新水平的过程中，报价是否依然保持紧凑？

查询分交易时段价差：常规交易时段的中位数（美分及基点）
每个数字背后的完整 SQL
SELECT session,
    round(quantileDeterministicIf(0.5)(toFloat64(ask_price) - toFloat64(bid_price), toUInt64(toUnixTimestamp64Micro(sip_timestamp)), bid_price > 0 AND ask_price >= bid_price) * 100, 1) AS med_spread_cents,
    round(quantileDeterministicIf(0.5)((toFloat64(ask_price) - toFloat64(bid_price)) / ((toFloat64(ask_price) + toFloat64(bid_price)) / 2) * 10000, toUInt64(toUnixTimestamp64Micro(sip_timestamp)), bid_price > 0 AND ask_price >= bid_price), 2) AS med_spread_bps,
    count() AS quote_updates,
    countIf(NOT (bid_price > 0 AND ask_price > 0 AND ask_price >= bid_price)) AS invalid_dropped
FROM global_markets.cache_stocks_quotes
WHERE ticker = 'MU'
  AND sip_timestamp >= toDateTime64('2026-06-01 13:30:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
  AND (toHour(sip_timestamp) * 60 + toMinute(sip_timestamp)) BETWEEN 810 AND 1199
GROUP BY toDate(toTimeZone(sip_timestamp, 'America/New_York')) AS session
ORDER BY session

在整个上涨过程中，价差始终保持在机构级水平：2026-06-01 交易日的价差为 3.96 基点，最近一个交易日为 4.19 基点——分位数的变动大致与股价水平同步。关于运作机制，请参阅 价差说明。

期权：双向持仓

查询MU 期权市场概览：总量、到期日及看涨/看跌比例
每个数字背后的完整 SQL
SELECT
    round(count() / 1e6, 2) AS prints_m,
    uniqExact(ticker) AS distinct_contracts,
    round(sum(size) / 1e6, 2) AS contracts_traded_m,
    round(sum(toFloat64(price) * size) * 100 / 1e9, 2) AS premium_notional_busd,
    round(toFloat64(sumIf(size, substring(ticker, 11, 1) = 'P')) / toFloat64(sumIf(size, substring(ticker, 11, 1) = 'C')), 2) AS month_put_call_ratio,
    uniqExact(substring(ticker, 5, 6)) AS expiries_traded,
    round(intDiv(toUInt32OrZero(substring(argMax(ticker, sz), 12, 8)), 1000), 0) AS busiest_strike_usd
FROM (
    SELECT ticker, price, size, sum(size) OVER (PARTITION BY ticker) AS sz
    FROM global_markets.options_trades
    WHERE startsWith(ticker, 'O:MU') AND length(ticker) = 19
      AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
)

16.76 百万份合约，涵盖 11789 种不同合约，总权利金达 $77.13 十亿。持仓呈现双向特征：月度看涨/看跌期权比率为 0.99，基本处于平价状态（NVDA 的 6 月份看涨期权占主导；对比）。在剧烈的价格重定价期间出现平价状态，表明期权市场在波动两侧均有分歧——数据反映的是分歧，而非意图。

查询合约分布：各行权价区间的看涨与看跌成交量
每个数字背后的完整 SQL
SELECT
    concat('$', toString(toUInt32(bucket))) AS strike_bucket,
    call_contracts,
    put_contracts,
    round(100.0 * put_contracts / (call_contracts + put_contracts), 1) AS put_share_pct
FROM (
    SELECT
        least(floor(toFloat64(toUInt32OrZero(substring(ticker, 12, 8))) / 1000 / 100) * 100, 1500) AS bucket,
        toUInt64(sumIf(size, substring(ticker, 11, 1) = 'C')) AS call_contracts,
        toUInt64(sumIf(size, substring(ticker, 11, 1) = 'P')) AS put_contracts
    FROM global_markets.options_trades
    WHERE startsWith(ticker, 'O:MU') AND length(ticker) = 19
      AND sip_timestamp >= toDateTime64('2026-06-01 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
    GROUP BY bucket
)
ORDER BY toUInt32OrZero(substring(strike_bucket, 2))

行权价分布范围在 1 月份看来是不可思议的——成交量从 $0 区间一直延伸至 $1500+ 的上限，这体现了标的资产的波动幅度。持有该波动所需的成本是多少？本月最后一个交易日的收盘价给出了答案：

查询月终波动预期：6月最后一份平值跨式期权价格
每个数字背后的完整 SQL
WITH (
    SELECT argMaxIf(toFloat64(close), window_start, (toHour(window_start) * 60 + toMinute(window_start)) BETWEEN 810 AND 1199)
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'MU' AND window_start >= toDateTime('2026-06-30 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
) AS spot
SELECT
    toString(any(expiry)) AS nearest_expiry,
    dateDiff('day', toDate('2026-06-30'), any(expiry)) AS calendar_days_to_expiry,
    round(spot, 2) AS spot_close,
    argMin(strike_usd, (dist, strike_usd)) AS atm_strike,
    round(argMin(call_last + put_last, (dist, strike_usd)), 2) AS straddle_usd,
    round(argMin(call_last + put_last, (dist, strike_usd)) / spot * 100, 2) AS straddle_pct_of_spot,
    count() AS two_sided_strikes
FROM (
    SELECT expiry, strike_usd, abs(strike_usd - spot) AS dist,
        anyIf(last_px, opt_type = 'C') AS call_last,
        anyIf(last_px, opt_type = 'P') AS put_last
    FROM (
        SELECT
            toDateOrNull(concat('20', substring(ticker, 5, 2), '-', substring(ticker, 7, 2), '-', substring(ticker, 9, 2))) AS expiry,
            substring(ticker, 11, 1) AS opt_type,
            toFloat64(toUInt32OrZero(substring(ticker, 12, 8))) / 1000 AS strike_usd,
            argMax(toFloat64(price), (sip_timestamp, toFloat64(price))) AS last_px
        FROM global_markets.options_trades
        WHERE startsWith(ticker, 'O:MU') AND length(ticker) = 19
          AND sip_timestamp >= toDateTime64('2026-06-30 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
        GROUP BY expiry, opt_type, strike_usd
    )
    WHERE expiry > toDate('2026-06-30')
    GROUP BY expiry, strike_usd
    HAVING countIf(opt_type = 'C') > 0 AND countIf(opt_type = 'P') > 0
)
WHERE expiry = (
    SELECT min(toDateOrNull(concat('20', substring(ticker, 5, 2), '-', substring(ticker, 7, 2), '-', substring(ticker, 9, 2))))
    FROM global_markets.options_trades
    WHERE startsWith(ticker, 'O:MU') AND length(ticker) = 19
      AND sip_timestamp >= toDateTime64('2026-06-30 00:00:00', 9) AND sip_timestamp < toDateTime64('2026-07-01 00:00:00', 9)
      AND toDateOrNull(concat('20', substring(ticker, 5, 2), '-', substring(ticker, 7, 2), '-', substring(ticker, 9, 2))) > toDate('2026-06-30')
)

截至 6 月 30 日收盘，最近的到期日为 2 个日历日；平值期权组合（$1150 行权价的看涨与看跌期权）最后成交总价为 $69.95：占 $1151.01 股价的 6.08%。买方需要在 2 天内实现同等幅度的单向波动才能实现盈亏平衡——6 月份结束时，市场定价仍预期会有巨大的波动。

新闻动态

查询6月MU相关新闻：文章量、主要媒体占比及共同标签
每个数字背后的完整 SQL
WITH
    (
        SELECT (JSONExtractString(any(publisher), 'name'), count())
        FROM global_markets.stocks_news
        WHERE has(tickers, 'MU')
          AND published_utc >= toDateTime('2026-06-01 00:00:00') AND published_utc < toDateTime('2026-07-01 04:00:00')
        GROUP BY JSONExtractString(publisher, 'name') ORDER BY count() DESC LIMIT 1
    ) AS top_pub
SELECT
    count() AS june_articles,
    uniqExact(JSONExtractString(publisher, 'name')) AS publishers,
    top_pub.1 AS top_publisher,
    round(100.0 * top_pub.2 / count(), 0) AS top_publisher_pct,
    countIf(has(tickers, 'NVDA')) AS nvda_co_articles,
    countIf(has(tickers, 'SNDK')) AS sndk_co_articles,
    countIf(has(tickers, 'INTC')) AS intc_co_articles
FROM global_markets.stocks_news
WHERE has(tickers, 'MU')
  AND published_utc >= toDateTime('2026-06-01 00:00:00') AND published_utc < toDateTime('2026-07-01 04:00:00')

195 来自 4 出版社的 MU 标签文章——请保持审慎阅读，其中 The Motley Fool 仅撰写了 59% 的文章。共同标签勾勒出了半导体行业的故事：86 篇涉及 NVDA，32 篇涉及 SNDK，21 篇涉及 INTC。内容核心是什么？厂商自身的文章标签提供了相关词汇，但并未采纳这些观点：

查询6月MU新闻主题：供应商文章标签统计
每个数字背后的完整 SQL
SELECT keyword, count() AS articles,
    round(100.0 * count() / (SELECT count() FROM global_markets.stocks_news WHERE has(tickers, 'MU') AND published_utc >= toDateTime('2026-06-01 00:00:00') AND published_utc < toDateTime('2026-07-01 04:00:00')), 1) AS pct_of_articles
FROM global_markets.stocks_news
ARRAY JOIN keywords AS keyword
WHERE has(tickers, 'MU')
  AND published_utc >= toDateTime('2026-06-01 00:00:00') AND published_utc < toDateTime('2026-07-01 04:00:00')
GROUP BY keyword
ORDER BY articles DESC, keyword ASC
LIMIT 10

“AI infrastructure”在 195 篇文章中占 49，其次是“memory chips”、“DRAM”和“high-bandwidth memory”——这是关于 AI 内存的词汇，仅以标签数量的形式呈现，而非逻辑解释。

空头头寸缩减——且仍存在回补缺口

查询FINRA场外卖空成交量：MU 6月覆盖量及卖空占比
每个数字背后的完整 SQL
SELECT toString(date) AS d,
    round(toFloat64(any(short_volume)) / 1e6, 2) AS short_shares_m,
    round(100 * toFloat64(any(short_volume)) / toFloat64(any(total_volume)), 1) AS short_pct_of_offexchange
FROM global_markets.stocks_short_volume
WHERE ticker = 'MU' AND date >= toDate('2026-06-01') AND date <= toDate('2026-06-30')
GROUP BY date
ORDER BY date

首先看回补情况：MU 6 月的卖空成交量文件仅包含 16 的 21 个交易日 —— 除了 6 月 29 日全市场截断外，部分 MU 数据行直接缺失 —— 因此无法计算月度比例；每日数据真实存在，缺口已披露。

查询6月结算数据：月中与月末卖空头寸
每个数字背后的完整 SQL
SELECT
    toString(maxIf(settlement_date, settlement_date <= toDate('2026-06-20'))) AS mid_june_settlement,
    round(toFloat64(maxIf(short_interest, settlement_date <= toDate('2026-06-20'))) / 1e6, 2) AS mid_june_shares_short_m,
    toString(maxIf(settlement_date, settlement_date > toDate('2026-06-20'))) AS eom_settlement,
    round(toFloat64(maxIf(short_interest, settlement_date > toDate('2026-06-20'))) / 1e6, 2) AS eom_shares_short_m,
    round((toFloat64(maxIf(short_interest, settlement_date <= toDate('2026-06-20'))) - toFloat64(maxIf(short_interest, settlement_date > toDate('2026-06-20')))) / 1e6, 2) AS decline_m_shares,
    round(toFloat64(maxIf(short_interest, settlement_date > toDate('2026-06-20'))) / toFloat64(maxIf(avg_daily_volume, settlement_date > toDate('2026-06-20'))), 2) AS eom_days_to_cover
FROM global_markets.stocks_short_interest
WHERE ticker = 'MU' AND settlement_date >= toDate('2026-06-01') AND settlement_date <= toDate('2026-06-30')

结算数据完整：2026-06-15 结算日的 41.59 百万股空头头寸在 2026-06-30 降至 31.67 百万股 —— 在包含财报周的下半月，头寸减少了 9.92 百万股。月末持仓等于 0.53 个交易日的平均成交量 —— 在该数据序列中规模较小。本页面最初发布时该数据尚未更新（延迟属正常现象）；上方数字为最新数据。

基本面分析

公司的官方声明显示了什么？需要注意：数据流滞后于申报日程——最新的一行早于 6 月的 10-Q 报告，因此该趋势反映的是整体走势，而非最新动态。

查询MU 最近六个季度财报：营收、净利润及稀释后EPS
每个数字背后的完整 SQL
SELECT quarter_end, revenue_bn,
    round(100 * revenue_bn / max(revenue_bn) OVER (), 1) AS pct_of_peak_revenue,
    net_income_bn, diluted_eps
FROM (
    SELECT toString(period_end) AS quarter_end,
        round(toFloat64(revenue) / 1e9, 2) AS revenue_bn,
        round(toFloat64(net_income_loss_attributable_common_shareholders) / 1e9, 2) AS net_income_bn,
        round(toFloat64(diluted_earnings_per_share), 2) AS diluted_eps
    FROM global_markets.stocks_income_statements
    WHERE has(tickers, 'MU') AND timeframe = 'quarterly'
    ORDER BY period_end DESC
    LIMIT 6
)
ORDER BY quarter_end ASC

在已披露的六个季度中，营收从 $7.75 十亿 (2024-08-29) 增长至 $13.64 十亿 (2025-11-27) ——最新的一行是该期间的峰值——该季度的净利润为 $5.24 十亿，稀释后每股收益 (EPS) 为 $4.6。增长并非线性，但该期间的终点处于高位。

后市关注重点

仅列出时间节点：下一次卖空头寸结算将于 7 月中旬完成，并按照 FINRA 的常规延迟时间发布；待供应商数据更新后，6 月提交的季度数据将成为基本面分析依据；本系列下一期将对 7 月的数据进行相同处理。

常见问题

为什么 MU 股价在 2026 年 6 月波动剧烈？

本页面侧重于数据衡量而非原因解释：MU 的交易区间在 $850.1 至 $1255 之间；季度 8-K 文件日期为 2026-06-24；在当月成交量最大的交易日，股价上涨了 15.8%；在此期间，整个存储板块的走势与 MU 一致。

MU 在 2026 年 6 月的波动率如何？

这是该半年中波动最剧烈的月份：年化实现波动率为 127.6%，日均波动幅度为 7.6%，而期初至期末的收益率仅为 14%。

MU 6 月的走势是公司特有因素还是行业整体走势？

属于行业整体走势，且 MU 是涨幅落后者：SNDK 上涨了 31.3%，所有存储板块股票的涨幅均超过了 MU 的 14%，而 SPY 和 NVDA 的收盘价均为下跌。MU 的特征在于成交额：达到 $995.7 十亿，为该板块最高。

MU 的空头头寸高吗？

相对于其自身的成交量而言并不高：在 2026-06-30 结算时，空头头寸为 31.67 百万股——按平均成交量计算，回补约需 0.53 天，较 6 月中旬减少了 9.92 百万股。空头头寸的发布总是存在结算滞后。

MU 的看跌/看涨期权比率意味着什么？

6 月的比率为 0.99，意味着看跌和看涨期权的成交量几乎相等——这代表市场处于双向博弈，而非单向押注。比率远低于 1 表示看涨情绪占优（NVDA 的 6 月情况），远高于 1 则表示看跌情绪占优。

数据说明

完整数据说明
  • 实体。 MU 指代 Micron Technology, Inc.，CIK 编号为 0000723125 —— 这是一个连续的 EDGAR 身份；不存在代码重复问题。
  • 卖空成交量覆盖范围。 6 月份文件涵盖了 16 的 21 交易日（包含 6 月 29 日的截断及 MU 特有的缺失数据）；仅显示每日比例。
  • 同行篮子。 包括 SNDK、WDC、INTC（6 月份 MU 新闻流中最频繁出现的存储公司标签），以及作为基准的 NVDA 和 SPY；所有成分均在前期确定，而非事后汇总。
  • 期权解析。 到期日、类型和行权价根据 OCC 代码（第 5/11/12 位）重新解析；权利金基于 100 股乘数计算。
  • 跨式期权计算方法。 使用最接近月末后最近到期日的行权价的最后成交价（而非报价）；这是实际成交价，而非模型隐含波动率。
  • 基本面滞后。 损益表数据滞后于申报日历；最新的 MU 数据早于 6 月 10 日的 10-Q 报告 —— 仅供趋势参考。
  • 6 月 19 日收盘情况 已通过 SPY 零交易条目核实。
  • 价差面板 不包含交叉报价或单边报价，缺失计数已在行内披露。
  • 市场整体背景 —— 包括该代码在内的全市场排行榜 —— 已包含在 2026 年 6 月回顾 中。

方法论

  • 统计周期为 2026 年 6 月 1 日至 30 日（经观测 K 线验证，共 21 个交易日）。收益率计算范围为常规交易时段的开盘至收盘；由于 6 月完全采用 EDT 时间，常规交易时段的原始时间范围为 13:30–20:00 UTC。滚动面板涵盖 EST 时段月份，并采用东部时间过滤器。
  • 已实现波动率 = 日收盘价对数收益率（常规交易时段收盘价）的样本标准差，并按 252 开方进行年化处理；若当月首个收益率跨越月份边界，则计入次月。
  • 滚动对比数据实时重新计算，不使用存储值。
  • 数据生成仅通过受限的只读路径进行批量处理；公开页面永不查询实时数据。数据仓库截至 2026 年 7 月 12 日。

每个面板均为单一存储对象——包括图表、表格和 SQL。如需进一步查询，请使用 Strasmore 终端。

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