Strasmore Research
市场回顾 Matt Connor作者: Matt Connor · 更新于 2026-09-05 · data as of September 5, 2026 · refreshed weekly

上一交易日异常期权活动与看涨看跌期权分布

根据完整美国期权成交数据,查看哪些标的成交量远超自身20个交易日均值,以及看涨期权与看跌期权如何分布,快速识别值得关注的异动。

异常期权活动,是指某个标的在一个交易时段内的期权合约成交量,远高于其近期正常水平。本榜单针对最近一个已结束的期权交易时段进行排名,Sep 3:将期权合约总成交量与同一标的过去20个交易时段的平均成交量进行比较。期权成交数据的结算时间晚于股票成交数据,因此这里所称的交易时段,是我们数据中最新的完整交易时段,而不是今天。

在查看表格前,需要明确一点:这是一个成交量筛选工具。大多数已发布的异常期权活动提醒,会将当日成交量与未平仓量进行比较。未平仓量是指收盘后仍未平仓的合约数量。我们存储的期权成交数据记录的是交易,而不是持仓,且其中没有未平仓量字段。下方每个比率,都是将某个标的的成交量与其自身的历史成交量进行比较。期权成交量与未平仓量的区别介绍了这两项指标分别反映什么。

异常期权活动:上一交易日

查询异常期权活动:最近完整交易日与各标的自身20个交易日均值对比
股票代码成交量比率交易日成交量(千)基准成交量(千)前高成交量(千)交易日标签交易日 ID基准交易日数
DPRO27.9204.97.496.4Sep 32026090320
IOT9.145.9516.6Sep 32026090320
ZS7.5123.716.529.6Sep 32026090320
DOCU7598.421.2Sep 32026090320
SNOW7274.539184.7Sep 32026090320
VFC6.348.97.816.2Sep 32026090320
CPB5.947.2848.7Sep 32026090320
LULU5.4153.128.347Sep 32026090320
PL5.192.818.235.9Sep 32026090320
HPE4.6254.455.2213.3Sep 32026090320
每个数字背后的完整 SQL
WITH tape AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(volume)) AS vol
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 45, 'America/New_York')
    GROUP BY d
),
ranked AS (
    SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS raw_rn
    FROM tape
),
cal AS (
    SELECT d, vol, rn, sum(if(rn BETWEEN 2 AND 21, 1, 0)) OVER () AS baseline_sessions
    FROM (
        SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS rn
        FROM ranked
        WHERE vol >= 0.75 * (SELECT quantileExact(0.5)(vol) FROM ranked WHERE raw_rn > 1)
    )
),
day_root AS (
    SELECT substring(ticker, 3, length(ticker) - 17) AS root,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(volume)) AS vol
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 45, 'America/New_York')
    GROUP BY root, d
),
scored AS (
    SELECT r.root AS root,
           sumIf(r.vol, c.rn = 1) AS last_vol,
           avgIf(r.vol, c.rn BETWEEN 2 AND 21) AS base_vol,
           maxIf(r.vol, c.rn BETWEEN 2 AND 21) AS prior_high,
           countIf(c.rn BETWEEN 2 AND 21) AS root_sessions,
           max(c.baseline_sessions) AS baseline_session_count,
           maxIf(toYYYYMMDD(c.d), c.rn = 1) AS session_id,
           maxIf(formatDateTime(c.d, '%b %e'), c.rn = 1) AS session_label
    FROM day_root r INNER JOIN cal c ON r.d = c.d
    WHERE c.rn <= 21
      AND r.root NOT IN ('SPCX')
      AND r.root NOT IN ('KORU','SOXL','SOXS','TQQQ','SQQQ','NVDL','NVDS','NVD','TSLL','TSLQ','TSLZ','SPXL','SPXS','UPRO','SPXU','LABU','LABD','FAS','FAZ','TNA','TZA','YINN','YANG','UDOW','SDOW','BOIL','KOLD','UCO','SCO','USD','SSO','SDS','QLD','QID','ERX','ERY','DRN','DRV','CURE','SOXY','MUU','SNXX','UVXY','SVXY','UVIX','SVIX','BULZ','WEBL','WEBS','DPST','DRIP','GUSH','AGQ','ZSL','BITX','ETHU','MSTX','MSTU','CONL','DUST','JNUG','JDST','NUGT')
    GROUP BY r.root
    HAVING last_vol >= 25000 AND base_vol >= 5000 AND root_sessions >= 18
)
SELECT root AS ticker,
       round(last_vol / base_vol, 1) AS vol_ratio,
       round(last_vol / 1000, 1) AS session_volume_k,
       round(base_vol / 1000, 1) AS baseline_volume_k,
       round(prior_high / 1000, 1) AS prior_high_volume_k,
       session_label,
       session_id,
       baseline_session_count
FROM scored
ORDER BY vol_ratio DESC, ticker ASC
LIMIT 10
自己运行这个查询

三列数据构成这份榜单。倍数表示标的成交量高于自身正常水平的幅度。基准值表示正常水平;在低流动性期权链上出现的大倍数,其事件规模可能小于活跃期权链上的较小倍数。20个交易日高点则表示本交易日是否超过上个月的任何一个交易日。最后三列是审计记录:榜单所依据的交易日,分别以标签和普通数字显示;以及基准值所基于的有效交易日数量。该数量必须达到20,页面才会发布。

DPRO 位居榜首,成交量达到自身平均水平的 27.9 倍:成交 204.9 千张合约,基准值为 7.4 千张,此前20个交易日高点为 96.4 千张。

  • IOT 达到 9.1 倍:成交 45.9 千张合约,基准值为 5 千张。
  • ZS 达到 7.5 倍,成交 123.7 千张合约,基准值为 16.5 千张。
  • DOCU 达到 7 倍,成交 59 千张合约,此前20个交易日高点为 21.2 千张。
  • SNOW 达到 7 倍,成交 274.5 千张合约。

榜单最后一行的 HPE 仍达到自身平均成交量的 4.6 倍。倍数和成交规模反映的是两个不同维度,因此榜单同时列出二者。股票市场中对应的指标见本周异常成交量股票;具体计算方法见相对成交量

看涨期权还是看跌期权:成交方向

看涨期权与看跌期权的成交量占比,基本就是预警真正指向的内容。同一份榜单、同一交易时段,按看涨期权占比排序:

查询看涨还是看跌:该交易日看涨和看跌期权合约成交量
股票代码看涨期权成交量(千)看跌期权成交量(千)看涨期权占比(%)交易日 ID
DPRO201.13.89820260903
IOT369.97820260903
HPE182.372.17220260903
PL57.335.56220260903
SNOW163.6110.96020260903
ZS73.749.96020260903
CPB25.821.45520260903
DOCU31285320260903
LULU72.780.44720260903
VFC5.543.41120260903
每个数字背后的完整 SQL
WITH tape AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(volume)) AS vol
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 45, 'America/New_York')
    GROUP BY d
),
ranked AS (
    SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS raw_rn
    FROM tape
),
cal AS (
    SELECT d, vol, rn, sum(if(rn BETWEEN 2 AND 21, 1, 0)) OVER () AS baseline_sessions
    FROM (
        SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS rn
        FROM ranked
        WHERE vol >= 0.75 * (SELECT quantileExact(0.5)(vol) FROM ranked WHERE raw_rn > 1)
    )
),
day_root AS (
    SELECT substring(ticker, 3, length(ticker) - 17) AS root,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(volume)) AS vol,
           sumIf(toFloat64(volume), substring(ticker, length(ticker) - 8, 1) = 'C') AS calls,
           sumIf(toFloat64(volume), substring(ticker, length(ticker) - 8, 1) = 'P') AS puts
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 45, 'America/New_York')
    GROUP BY root, d
),
scored AS (
    SELECT r.root AS root,
           round(sumIf(r.vol, c.rn = 1) / avgIf(r.vol, c.rn BETWEEN 2 AND 21), 1) AS vol_ratio,
           sumIf(r.calls, c.rn = 1) AS calls,
           sumIf(r.puts, c.rn = 1) AS puts,
           maxIf(toYYYYMMDD(c.d), c.rn = 1) AS session_id
    FROM day_root r INNER JOIN cal c ON r.d = c.d
    WHERE c.rn <= 21
      AND r.root NOT IN ('SPCX')
      AND r.root NOT IN ('KORU','SOXL','SOXS','TQQQ','SQQQ','NVDL','NVDS','NVD','TSLL','TSLQ','TSLZ','SPXL','SPXS','UPRO','SPXU','LABU','LABD','FAS','FAZ','TNA','TZA','YINN','YANG','UDOW','SDOW','BOIL','KOLD','UCO','SCO','USD','SSO','SDS','QLD','QID','ERX','ERY','DRN','DRV','CURE','SOXY','MUU','SNXX','UVXY','SVXY','UVIX','SVIX','BULZ','WEBL','WEBS','DPST','DRIP','GUSH','AGQ','ZSL','BITX','ETHU','MSTX','MSTU','CONL','DUST','JNUG','JDST','NUGT')
    GROUP BY r.root
    HAVING sumIf(r.vol, c.rn = 1) >= 25000
       AND avgIf(r.vol, c.rn BETWEEN 2 AND 21) >= 5000
       AND countIf(c.rn BETWEEN 2 AND 21) >= 18
),
board AS (
    SELECT root, vol_ratio, calls, puts, session_id
    FROM scored
    ORDER BY vol_ratio DESC, root ASC
    LIMIT 10
)
SELECT root AS ticker,
       round(calls / 1000, 1) AS call_volume_k,
       round(puts / 1000, 1) AS put_volume_k,
       round(100.0 * calls / (calls + puts), 0) AS call_share_pct,
       session_id
FROM board
WHERE calls + puts > 0
ORDER BY call_share_pct DESC, ticker ASC
自己运行这个查询

在看涨期权占比较高的一端,DPRO98%的合约成交量集中在看涨期权上:看涨期权成交量为201.1千张,看跌期权成交量为3.8千张。在同一表格的另一端,VFC的看涨期权成交量为11%,其中看涨期权为5.5千张,看跌期权为43.4千张。

这一占比值得关注,但也很容易被过度解读。每张已成交合约都有买方和卖方,而成交记录不会显示哪一方发起交易。看涨期权成交量较大,可能代表直接买入看涨期权的多头仓位,也可能代表卖方针对其已持有的股票卖出备兑看涨期权。买入和卖出看涨期权将介绍交易双方分别承担的风险与义务。成交活跃时,期权价格通常也会较高;相关内容请参阅隐含波动率最高的股票榜单。

本交易日合约的构成

查询该交易日合约构成:按到期天数划分的期权成交量
距到期日分组合约数(百万)成交量占比(%)交易日 ID
same day182520260903
1 to 7 days22.5931.420260903
8 to 30 days17.123.720260903
31 to 90 days7.5210.420260903
91 to 365 days5.517.720260903
over a year1.291.820260903
每个数字背后的完整 SQL
WITH tape AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(volume)) AS vol
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 45, 'America/New_York')
    GROUP BY d
),
ranked AS (
    SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS raw_rn
    FROM tape
),
cal AS (
    SELECT d, vol, rn, sum(if(rn BETWEEN 2 AND 21, 1, 0)) OVER () AS baseline_sessions
    FROM (
        SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS rn
        FROM ranked
        WHERE vol >= 0.75 * (SELECT quantileExact(0.5)(vol) FROM ranked WHERE raw_rn > 1)
    )
),
bars AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           toFloat64(volume) AS v,
           toDate(concat('20', substring(ticker, length(ticker) - 14, 2), '-',
                         substring(ticker, length(ticker) - 12, 2), '-',
                         substring(ticker, length(ticker) - 10, 2))) AS expiry
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 12, 'America/New_York')
      AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM cal WHERE rn = 1)
),
scored AS (
    SELECT multiIf(expiry - d <= 0, 1, expiry - d <= 7, 2, expiry - d <= 30, 3,
                   expiry - d <= 90, 4, expiry - d <= 365, 5, 6) AS bk,
           v, d
    FROM bars
)
SELECT arrayElement(['same day', '1 to 7 days', '8 to 30 days', '31 to 90 days', '91 to 365 days', 'over a year'], bk) AS dte_bucket,
       round(sum(v) / 1e6, 2) AS contracts_m,
       round(100.0 * sum(v) / sum(sum(v)) OVER (), 1) AS pct_of_volume,
       toYYYYMMDD(max(d)) AS session_id
FROM scored
GROUP BY bk
ORDER BY bk ASC
自己运行这个查询

当日到期合约占整个交易日的 25%,共计 18 百万张。另有 31.4% 的合约在一周内到期。期限最远的一端,剩余期限超过一年的合约占 1.8%。

这一构成会影响异常活动标题的解读。此类榜单背后的大部分成交量都来自短期限合约,而当日到期合约会在收盘铃响前结算,不会被持有至下一交易日。0DTE期权涵盖的就是期权链中这一端。

这是否为到期日交易时段?

月度到期日当天,整个市场的期权成交量都会上升,因为规模最大的上市合约批次会同时到期。一份在到期日运行的比率筛选结果会标记市场中的一半交易时段,因此下方面板会标出该窗口内的每个交易时段。

查询按交易日统计的全市场期权成交量,并标注月度到期日
25 rows (showing 20)
交易日合约数(百万)交易日类型月度到期日(m)交易日 ID
Jul 3175.5ordinary75.120260731
Aug 372.7ordinary75.120260803
Aug 478.9ordinary75.120260804
Aug 569.5ordinary75.120260805
Aug 663.2ordinary75.120260806
Aug 773.2ordinary75.120260807
Aug 1061.4ordinary75.120260810
Aug 1154.6ordinary75.120260811
Aug 1255.4ordinary75.120260812
Aug 1366.6ordinary75.120260813
Aug 1466ordinary75.120260814
Aug 1760.9ordinary75.120260817
Aug 1856.9ordinary75.120260818
Aug 1967.2ordinary75.120260819
Aug 2064.9ordinary75.120260820
Aug 2175.1monthly expiration75.120260821
Aug 2461ordinary75.120260824
Aug 2552.8ordinary75.120260825
Aug 2649.4ordinary75.120260826
Aug 2766.1ordinary75.120260827
每个数字背后的完整 SQL
WITH tape AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           sum(toFloat64(volume)) AS vol
    FROM global_markets.options_minute_aggs
    WHERE window_start >= toDateTime(today() - 45, 'America/New_York')
    GROUP BY d
),
ranked AS (
    SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS raw_rn
    FROM tape
),
cal AS (
    SELECT d, vol, rn, sum(if(rn BETWEEN 2 AND 21, 1, 0)) OVER () AS baseline_sessions
    FROM (
        SELECT d, vol, row_number() OVER (ORDER BY d DESC) AS rn
        FROM ranked
        WHERE vol >= 0.75 * (SELECT quantileExact(0.5)(vol) FROM ranked WHERE raw_rn > 1)
    )
),
w AS (
    SELECT d, vol,
           toStartOfMonth(d) + toIntervalDay(((5 - toDayOfWeek(toStartOfMonth(d)) + 7) % 7) + 14) AS third_friday
    FROM cal
    WHERE rn <= 25
),
marked AS (
    SELECT d, vol,
           (d = max(if(d <= third_friday, d, toDate('1970-01-01'))) OVER (PARTITION BY toStartOfMonth(d)))
             AND (third_friday <= max(d) OVER ()) AS is_expiry
    FROM w
),
latest AS (
    SELECT d, vol, is_expiry,
           max(if(is_expiry, d, toDate('1970-01-01'))) OVER () AS last_expiry_d
    FROM marked
)
SELECT formatDateTime(d, '%b %e') AS session,
       round(vol / 1e6, 1) AS contracts_m,
       multiIf(is_expiry, 'monthly expiration', 'ordinary') AS session_type,
       round(max(if(d = last_expiry_d, vol, 0)) OVER () / 1e6, 1) AS monthly_expiry_m,
       toYYYYMMDD(d) AS session_id
FROM latest
ORDER BY d ASC
自己运行这个查询

该面板中最新的交易时段就是交易所报告的交易时段,两张表中的日期相同,其标签为 ordinary。当天全美市场成交了 72百万张合约。同一窗口内最近一次月度到期日成交了 75.1百万张合约。面板中每个比率所依据的20个交易时段基准通常恰好包含一个月度到期日,因此分母在各交易时段中都包含同一笔集中影响。到期日还引出一个成交量筛选无法回答的行权价问题:最大痛点计算的是合约结算时让期权持有人获得金额最少的行权价;其计算依据是未平仓合约量,而不是本文衡量的成交量。

异常期权活动会引领股价吗?

值得回答的问题是:期权交易活跃的交易日之后,标的资产是否会出现较大波动。统计区间为过去200个交易日:选取每个股票-交易日中达到相同成交量门槛的样本,按其成交量相对于自身基准的倍数分组,再与标的股票下一交易日收盘价的绝对变动进行匹配。表中的“典型值”是每只股票在该区间内每日变动的自身中位数,因此每个分组都与同一只股票自身进行比较。

查询重度期权交易日之后的走势:次日绝对涨跌幅与普通日同批标的对比
相对成交量区间事件数后续变动中位数(%)典型变动中位数(%)差距(百分点)后续带符号变动中位数(%)
5x or more6242.511.960.55-0.16
3x to 5x12122.352.150.2-0.21
2x to 3x18522.282.210.07-0.22
1x to 2x83801.982.23-0.25-0.05
below 1x115591.892.06-0.17-0.01
每个数字背后的完整 SQL
WITH daily AS (
    SELECT underlying_symbol AS sym,
           date AS d,
           sum(volume) AS vol,
           max(underlying_close) AS px
    FROM global_markets.options_greeks
    WHERE date >= today() - 200
      AND underlying_close > 0
      AND underlying_symbol NOT IN ('SPCX')
      AND underlying_symbol NOT IN ('KORU','SOXL','SOXS','TQQQ','SQQQ','NVDL','NVDS','NVD','TSLL','TSLQ','TSLZ','SPXL','SPXS','UPRO','SPXU','LABU','LABD','FAS','FAZ','TNA','TZA','YINN','YANG','UDOW','SDOW','BOIL','KOLD','UCO','SCO','USD','SSO','SDS','QLD','QID','ERX','ERY','DRN','DRV','CURE','SOXY','MUU','SNXX','UVXY','SVXY','UVIX','SVIX','BULZ','WEBL','WEBS','DPST','DRIP','GUSH','AGQ','ZSL','BITX','ETHU','MSTX','MSTU','CONL','DUST','JNUG','JDST','NUGT')
      AND underlying_symbol NOT IN (SELECT ticker FROM global_markets.stocks_splits
                                    WHERE execution_date BETWEEN today() - 230 AND today())
    GROUP BY sym, d
),
seq AS (
    SELECT sym, d, vol, px,
           avg(vol) OVER (PARTITION BY sym ORDER BY d ROWS BETWEEN 20 PRECEDING AND 1 PRECEDING) AS base,
           count() OVER (PARTITION BY sym ORDER BY d ROWS BETWEEN 20 PRECEDING AND 1 PRECEDING) AS base_n,
           any(px) OVER (PARTITION BY sym ORDER BY d ROWS BETWEEN 1 FOLLOWING AND 1 FOLLOWING) AS next_px,
           any(px) OVER (PARTITION BY sym ORDER BY d ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS prev_px
    FROM daily
),
moves AS (
    SELECT sym, vol, base, base_n,
           if(next_px > 0, 100 * abs(next_px / px - 1), -1) AS next_abs,
           if(next_px > 0, 100 * (next_px / px - 1), -999) AS next_signed,
           if(prev_px > 0, 100 * abs(px / prev_px - 1), -1) AS own_abs
    FROM seq
),
typical AS (
    SELECT sym, quantileExact(0.5)(own_abs) AS typ
    FROM moves
    WHERE own_abs >= 0
    GROUP BY sym
)
SELECT arrayElement(['5x or more', '3x to 5x', '2x to 3x', '1x to 2x', 'below 1x'], bk) AS rvol_bucket,
       count() AS event_count,
       round(quantileExact(0.5)(next_abs), 2) AS median_next_move_pct,
       round(quantileExact(0.5)(typ), 2) AS median_typical_move_pct,
       round(quantileExact(0.5)(next_abs) - quantileExact(0.5)(typ), 2) AS gap_pp,
       round(quantileExact(0.5)(next_signed), 2) AS median_next_signed_pct
FROM (
    SELECT m.sym AS sym,
           multiIf(m.vol / m.base >= 5, 1, m.vol / m.base >= 3, 2, m.vol / m.base >= 2, 3,
                   m.vol / m.base >= 1, 4, 5) AS bk,
           m.next_abs AS next_abs,
           m.next_signed AS next_signed,
           t.typ AS typ
    FROM moves m INNER JOIN typical t ON m.sym = t.sym
    WHERE m.base_n = 20 AND m.base >= 5000 AND m.vol >= 25000 AND m.next_abs >= 0
)
GROUP BY bk
ORDER BY bk ASC
自己运行这个查询

在期权成交量达到自身基准五倍或以上的交易日之后,下一交易日绝对变动的中位数为2.51%;同一批股票在普通交易日的对应数值为1.96%,两者相差0.55个百分点。在表格较为平静的一端,两列数值分别为1.89%和2.06%,相差-0.17个百分点。

坦率地说,影响幅度很小,而且体现的是波动幅度,而非方向。期权交易更活跃的交易日之后,股价绝对变动略大,但中位数差异只有零点几个百分点。五倍成交量交易日后的中位数涨跌幅为-0.16%,低于自身基准的交易日之后为-0.01%。这两者都不像醒目标题所暗示的那样构成显著行情。还应考虑这类情况的稀少程度:在研究区间内,有624个股票-交易日的期权成交量达到自身基准的五倍,而低于自身基准的有11559个。

该筛选器的衡量方法

如果筛选器隐藏规则,表面上是数据,实质上就是观点。因此,本文列出全部规则。

  1. 数据来源。完整的美国期权分钟级成交数据。系统从每份合约的 OCC 代码中解析标的,因此指数期权代码和当日到期合约,都会与普通股票及基金期权链一并纳入。
  1. 比率。上一交易日已完成交易时段的合约总成交量,除以同一标的在此前 20 个符合条件交易时段的平均成交量。其中至少 18 个时段必须有成交量。榜单会在单独一列显示窗口长度。如果有效时段不足 20 个,页面会暂缓发布,而不会在标注为 20 个交易时段的情况下使用更短窗口计算平均值。
  1. 最低门槛。当日成交量至少为 25,000 张合约,基准平均成交量至少为 5,000 张合约。没有这些门槛,榜单会被某条订单就达到 10 倍读数的期权链占满。
  1. 交易时段资格。本页每个面板都从同一份成交数据的一个 45 天窗口中读取交易时段,并采用同一条规则:当某交易日的全市场成交量达到该窗口内较早交易日中位数的 75% 时,该交易日才计入。计算中位数时会排除最新交易日,因此部分数据尚未加载完整的交易日,不能通过压低自身的基准来满足门槛。成交数据会分批抵达窗口前端;成交量不足正常交易时段四分之三的交易日,会在数据补齐前暂不列入榜单。每个面板都会标出其采用的交易日,因此四个面板可以相互对照。
  1. 排除项。按名称剔除杠杆型和反向型基金:三倍产品的期权链会随其杠杆倍数波动,每周都会挤占榜单。另有一个重复使用的 ticker symbol 也被排除,因为供应商的历史数据将两家公司拼接在了同一代码下。
  1. 不使用未平仓合约。数据仓库没有未平仓合约字段,因此本页没有任何内容会将成交量与未平仓头寸进行比较。竞争对手的筛选器可能显示成交量超过未平仓量;本筛选器显示的是成交量超过自身 20 个交易时段的平均值。
  1. 到期日。月度到期会推高全市场期权成交量。交易时段面板会标注这一情况;如果榜单基于到期日交易时段,应与其他到期日交易时段进行比较。

后续表现研究使用第二张表,即每日期权 Greeks 数据文件。该文件包含规范的标的代码和标的收盘价。它排除当日到期合约和指数期权,并且结算时间比分钟级成交数据晚几个交易日,因此其覆盖范围小于榜单。研究窗口内发生股票拆分的标的也会被排除,因为拆分会制造虚假的巨大涨跌。

常见问题

什么是异常期权活动?

指某个标的在一个交易日内交易的期权合约数量,远高于其近期自身平均水平。本页面采用算术阈值,而非编辑判断:用该交易日的合约成交量除以同一标的前20个交易日的平均成交量,同时设定交易日成交量至少为25,000张,基准均值至少为5,000张。

异常期权活动能预测股票走势吗?

预测能力较弱,也无法判断方向。在我们过去200天的数据中,期权成交量达到某标的自身基准的五倍后,下一交易日的绝对涨跌幅中位数为2.51%;相比之下,同一批股票在普通交易日后的中位数为1.96%。下一交易日的涨跌幅中位数为-0.16%,这意味着成交量对股价波动幅度有一定信息,但几乎无法说明涨跌方向。

期权成交量数据何时更新?

交易时段内,合约成交量会通过统一期权行情系统实时显示。支撑本页面的已存储行情数据存在延迟,因此看板显示的是最新完整交易日的数据,而不是进行中的交易日数据,并会标注日期。本页面不提供实时行情。

为什么这个异常期权活动扫描器不使用未平仓合约数?

结算机构会在收盘后公布未平仓合约数,而我们持有的数据中没有未平仓合约数字段。将某交易日的成交量与同一标的自身的历史成交量进行比较,可以直接从行情数据中测算,也能回答类似问题:该期权链的交易是否比平时更活跃。


以上每个数字均来自已存储且带版本记录的查询。您可以打开任意面板下方的 SQL,审核相关测算;也可以在 Strasmore terminal 上按任意时间窗口运行相同筛选。

#期权#unusual options activity#options volume#market structure