作者： Matt Connor 2026-07-22 · 更新于 2026-09-05 · data as of September 5, 2026 · refreshed weekly

52周新高与新低股票查询

查看收盘价达到或接近52周高点和低点的股票，并按交易所成交记录统计两类股票数量，作为市场广度指标。

52周高点是指一只股票在过去一年中收盘价达到的最高水平，52周低点则是最低水平。在最近一个已完成的交易时段，即Sep 4, 2026，该筛选结果中有31只股票收盘价达到52周高点或距离该高点不超过1%，另有10只股票收盘价达到52周低点或距离该低点不超过1%。该筛选覆盖美国大型、高流动性的运营公司，并根据交易所成交记录重新生成。

52周高点股票

下方列出高点名单中年初至今涨幅最大的股票，最多十二只。每只股票收盘价均达到或处于过去52周最高收盘价的1%以内。

查询处于52周高点的股票，按年初至今回报率排序

ticker	收盘价	年初至今收益率（%）
DELL	516.38	310.2
PBF	74.92	176.2
MPC	385.9	137.3
DINO	106.1	130.3
VLO	369.15	126.8
PSX	253.55	96.5
PR	23.69	68.9
SNOW	353.98	61.4
ZETA	32.52	59.8
HUM	406.38	58.7
STT	194.92	51.1
CNH	13.78	49.5

每个数字背后的完整 SQL

WITH universe AS (
    SELECT ticker
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 20 DAY
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
      AND ticker IN (SELECT arrayJoin(tickers) FROM global_markets.stocks_income_statements
                     WHERE period_end >= today() - 400)
      AND ticker NOT IN ('SPCX','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 ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 460 AND today())
    GROUP BY ticker
    HAVING sum(toFloat64(close) * toFloat64(volume)) >= 2000000000
),
last_session AS (
    SELECT max(date) AS d FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY' AND date >= today() - 12 AND date < today()
),
daily AS (
    SELECT ticker, date AS dt, toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE date < today()
      AND ticker IN (SELECT ticker FROM universe)
      AND date > (SELECT d FROM last_session) - 365
),
ranged AS (
    SELECT ticker,
           argMax(c, dt) AS last_close,
           max(c) AS hi,
           min(c) AS lo,
           argMaxIf(c, dt, dt <= toDate('2025-12-31')) AS base_close,
           count() AS n_sessions,
           min(dt) AS first_dt,
           max(dt) AS last_dt
    FROM daily
    GROUP BY ticker
    HAVING n_sessions >= 200
       AND countIf(dt <= toDate('2025-12-31')) > 0
       AND last_dt = (SELECT d FROM last_session)
       AND first_dt <= (SELECT d FROM last_session) - 350
)
SELECT ticker,
       round(last_close, 2) AS close,
       round((last_close / base_close - 1) * 100, 1) AS ytd_return_pct
FROM ranged
WHERE last_close >= hi * 0.99
ORDER BY ytd_return_pct DESC, ticker ASC
LIMIT 12

自己运行这个查询

DELL位居榜首，年初至今涨幅为310.2%，收盘价为$516.38。由于该名单中的股票按定义均处于52周高点的1%以内，因此价格一栏同时代表正在测试的价位。PBF紧随其后，涨幅为176.2%；MPC涨幅为137.3%。表格末尾的CNH年初至今涨幅为49.5%。

这正是差异所在。52周高点只说明价格处于自身一年区间中的位置，仅此而已。一只去年夏季大幅下跌、随后逐步收复失地的股票，与一只从未停止上涨的股票，都可能被贴上同样的标签。

52周收盘价低点股票

这是同一筛选条件的反向版本。这些股票的收盘价等于或处于过去52周最低收盘价上方1%以内，并按年初至今表现最弱的顺序排列，最多列出12只。

查询处于52周低点的股票，按年初至今回报率排序

ticker	收盘价	年初至今收益率（%）
LULU	96.88	-53.4
NKE	38.42	-39.7
BROS	46.61	-23.9
AS	28.62	-23.4
IDXX	531.36	-21.5
MLM	509.13	-18.2
MCD	259.24	-15.2
TJX	132.54	-13.7
LHX	261.07	-11.1
CMS	68.54	-2

每个数字背后的完整 SQL

WITH universe AS (
    SELECT ticker
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 20 DAY
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
      AND ticker IN (SELECT arrayJoin(tickers) FROM global_markets.stocks_income_statements
                     WHERE period_end >= today() - 400)
      AND ticker NOT IN ('SPCX','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 ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 460 AND today())
    GROUP BY ticker
    HAVING sum(toFloat64(close) * toFloat64(volume)) >= 2000000000
),
last_session AS (
    SELECT max(date) AS d FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY' AND date >= today() - 12 AND date < today()
),
daily AS (
    SELECT ticker, date AS dt, toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE date < today()
      AND ticker IN (SELECT ticker FROM universe)
      AND date > (SELECT d FROM last_session) - 365
),
ranged AS (
    SELECT ticker,
           argMax(c, dt) AS last_close,
           max(c) AS hi,
           min(c) AS lo,
           argMaxIf(c, dt, dt <= toDate('2025-12-31')) AS base_close,
           count() AS n_sessions,
           min(dt) AS first_dt,
           max(dt) AS last_dt
    FROM daily
    GROUP BY ticker
    HAVING n_sessions >= 200
       AND countIf(dt <= toDate('2025-12-31')) > 0
       AND last_dt = (SELECT d FROM last_session)
       AND first_dt <= (SELECT d FROM last_session) - 350
)
SELECT ticker,
       round(last_close, 2) AS close,
       round((last_close / base_close - 1) * 100, 1) AS ytd_return_pct
FROM ranged
WHERE last_close <= lo * 1.01
ORDER BY ytd_return_pct ASC, ticker ASC
LIMIT 12

自己运行这个查询

LULU在这份名单中的年初至今表现最弱，为-53.4%；其收盘价为$96.88，与自身52周收盘价低点相差不超过1%。其次是NKE，为-39.7%。

52周低点只是价格区间中的一个位置，并不能据此判断一家公司的经营状况。盈利公司可能因长期横盘而进入这份名单，经营陷入困境的公司也可能如此。仅凭收盘价无法区分两者。若要了解针对整个市场的同类问题，请参阅市场如何从崩盘中复苏。

高点和低点数量如何反映市场广度

市场广度衡量的是有多少只股票参与一轮行情，而不是指数走了多远。创新高数量与创新低数量的对比，是最古老的市场广度指标之一。在本页中，这一数据值得关注。

查询过去六周每日新52周高点与新低点对比

31 rows (showing 20)
日期	处于52周高点	处于52周低点	净创新高数
Jul 24, 2026	38	12	26
Jul 27, 2026	39	1	38
Jul 28, 2026	55	3	52
Jul 29, 2026	32	15	17
Jul 30, 2026	28	7	21
Jul 31, 2026	18	8	10
Aug 3, 2026	24	1	23
Aug 4, 2026	50	2	48
Aug 5, 2026	56	2	54
Aug 6, 2026	36	3	33
Aug 7, 2026	45	3	42
Aug 10, 2026	56	5	51
Aug 11, 2026	51	3	48
Aug 12, 2026	59	7	52
Aug 13, 2026	60	2	58
Aug 14, 2026	48	2	46
Aug 17, 2026	39	5	34
Aug 18, 2026	39	6	33
Aug 19, 2026	38	0	38
Aug 20, 2026	26	10	16

每个数字背后的完整 SQL

WITH universe AS (
    SELECT ticker
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 20 DAY
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
      AND ticker IN (SELECT arrayJoin(tickers) FROM global_markets.stocks_income_statements
                     WHERE period_end >= today() - 400)
      AND ticker NOT IN ('SPCX','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 ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 460 AND today())
    GROUP BY ticker
    HAVING sum(toFloat64(close) * toFloat64(volume)) >= 2000000000
),
last_session AS (
    SELECT max(date) AS d FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY' AND date >= today() - 12 AND date < today()
),
daily AS (
    SELECT ticker, date AS dt, toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE date < today()
      AND ticker IN (SELECT ticker FROM universe)
      AND date <= (SELECT d FROM last_session)
      AND date >= (SELECT d FROM last_session) - 425
),
rolled AS (
    SELECT ticker, dt, c,
           max(c) OVER w AS hi,
           min(c) OVER w AS lo,
           count() OVER w AS n_sessions,
           min(dt) OVER (PARTITION BY ticker) AS first_dt
    FROM daily
    WINDOW w AS (PARTITION BY ticker ORDER BY dt RANGE BETWEEN 364 PRECEDING AND CURRENT ROW)
)
SELECT formatDateTime(dt, '%b %e, %Y') AS date,
       countIf(c >= hi * 0.99) AS at_52w_high,
       countIf(c <= lo * 1.01) AS at_52w_low,
       countIf(c >= hi * 0.99) - countIf(c <= lo * 1.01) AS net_highs
FROM rolled
WHERE n_sessions >= 200
  AND first_dt <= dt - 350
  AND dt > (SELECT d FROM last_session) - 43
GROUP BY dt
ORDER BY dt

自己运行这个查询

在Sep 4, 2026，该筛选结果显示，有31只股票处于或接近52周高点，另有10只处于或接近低点，净值为21。六周前的Jul 24, 2026，同一筛选结果显示有38只股票创出或接近高点，12只股票创出或接近低点。

在图表所列的31个交易日中，两项数据每天都大幅波动。因此，单个交易日的水平不如数周内的趋势重要。指数保持稳定，但创新高数量减少，说明市场参与面比指数本身显示的更窄。低点数量增加，则构成他人恐惧时买入的市场环境；我们已将这一现象与新闻情绪进行了对比测试。

市场其余部分距离各自高点有多远

高点和低点是分布的两端；大多数股票处于中间位置。本面板按股票低于52周高点的幅度对整个股票池进行分类，并报告各区间的年初至今收益率中位数。

查询整个筛选范围距52周高点有多远

区间	股票数	占比（%）	年初至今收益率中位数（%）
At the high (under 1%)	31	5.8	38.3
1% to 5% below	74	13.8	24.4
5% to 10% below	94	17.5	15.7
10% to 20% below	122	22.7	3.7
20% to 35% below	121	22.5	-6.3
More than 35% below	96	17.8	-17.8

每个数字背后的完整 SQL

WITH universe AS (
    SELECT ticker
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 20 DAY
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) >= 570
      AND toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York')) < 960
      AND ticker IN (SELECT arrayJoin(tickers) FROM global_markets.stocks_income_statements
                     WHERE period_end >= today() - 400)
      AND ticker NOT IN ('SPCX','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 ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 460 AND today())
    GROUP BY ticker
    HAVING sum(toFloat64(close) * toFloat64(volume)) >= 2000000000
),
last_session AS (
    SELECT max(date) AS d FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY' AND date >= today() - 12 AND date < today()
),
daily AS (
    SELECT ticker, date AS dt, toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE date < today()
      AND ticker IN (SELECT ticker FROM universe)
      AND date > (SELECT d FROM last_session) - 365
),
ranged AS (
    SELECT ticker,
           argMax(c, dt) AS last_close,
           max(c) AS hi,
           min(c) AS lo,
           argMaxIf(c, dt, dt <= toDate('2025-12-31')) AS base_close,
           count() AS n_sessions,
           min(dt) AS first_dt,
           max(dt) AS last_dt
    FROM daily
    GROUP BY ticker
    HAVING n_sessions >= 200
       AND countIf(dt <= toDate('2025-12-31')) > 0
       AND last_dt = (SELECT d FROM last_session)
       AND first_dt <= (SELECT d FROM last_session) - 350
)
SELECT multiIf(p < 1, 'At the high (under 1%)',
               p < 5, '1% to 5% below',
               p < 10, '5% to 10% below',
               p < 20, '10% to 20% below',
               p < 35, '20% to 35% below',
               'More than 35% below') AS bucket,
       count() AS stocks,
       round(100.0 * count() / sum(count()) OVER (), 1) AS share_pct,
       round(quantileExact(0.5)(ytd), 1) AS median_ytd_pct
FROM (
    SELECT (1 - last_close / hi) * 100 AS p,
           (last_close / base_close - 1) * 100 AS ytd,
           multiIf(p < 1, 0, p < 5, 1, p < 10, 2, p < 20, 3, p < 35, 4, 5) AS ord
    FROM ranged
)
GROUP BY bucket, ord
ORDER BY ord

自己运行这个查询

股票池中有5.8%、即31只股票，收盘价距离52周高点不超过1%。另一端有17.8%只股票，收盘价低于52周高点超过35%。最高区间的年初至今收益率中位数为38.3%；最低区间为-17.8%，图表展示了两者之间的变化形态。

这种梯度更接近算术结果，而非发现了什么规律：一只股票接近52周高点，按定义说明它今年大部分时间都在上涨。本面板的价值在于其覆盖范围：极端情况有多罕见，以及市场中有多大比例的股票距离自身最佳价格很远。大仓位持有其中一只股票，承担的正是单只股票的实际风险。

市场自身处于52周区间的位置

一只股票处于高位时，若市场自身也接近高位，其含义与市场远离高位时不同。以下五只大盘ETF采用与上文股票完全相同的衡量方法。

查询五只大盘ETF处于各自52周区间的位置

标签	低于高点（%）	高于低点（%）	年初至今收益率（%）
S&P 500 (SPY)	0.51	22.5	13.5
Dow 30 (DIA)	1.11	18.9	11.7
S&P 500 equal weight (RSP)	1.36	20.6	14.7
Russell 2000 (IWM)	3.23	28.9	19.9
Nasdaq 100 (QQQ)	3.3	29.2	17.5

每个数字背后的完整 SQL

WITH last_session AS (
    SELECT max(date) AS d FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY' AND date >= today() - 12 AND date < today()
),
daily AS (
    SELECT ticker, date AS dt, toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE date < today()
      AND ticker IN ('SPY', 'QQQ', 'DIA', 'IWM', 'RSP')
      AND date > (SELECT d FROM last_session) - 365
),
ranged AS (
    SELECT ticker,
           argMax(c, dt) AS last_close,
           max(c) AS hi,
           min(c) AS lo,
           argMaxIf(c, dt, dt <= toDate('2025-12-31')) AS base_close,
           count() AS n_sessions,
           min(dt) AS first_dt,
           max(dt) AS last_dt
    FROM daily
    GROUP BY ticker
    HAVING n_sessions >= 200
       AND countIf(dt <= toDate('2025-12-31')) > 0
       AND last_dt = (SELECT d FROM last_session)
       AND first_dt <= (SELECT d FROM last_session) - 350
)
SELECT multiIf(ticker = 'SPY', 'S&P 500 (SPY)',
               ticker = 'QQQ', 'Nasdaq 100 (QQQ)',
               ticker = 'DIA', 'Dow 30 (DIA)',
               ticker = 'RSP', 'S&P 500 equal weight (RSP)',
               'Russell 2000 (IWM)') AS label,
       round((1 - last_close / hi) * 100, 2) AS pct_below_high,
       round((last_close / lo - 1) * 100, 1) AS pct_above_low,
       round((last_close / base_close - 1) * 100, 1) AS ytd_return_pct
FROM ranged
ORDER BY pct_below_high ASC

自己运行这个查询

S&P 500 (SPY)收盘时低于52周高点0.51%，是五只ETF中距离高点最近的一只；同时高于52周低点22.5%。Nasdaq 100 (QQQ)距离自身高点最远，为3.3%。年初至今，两者的表现分别为13.5%和17.5%。

同一指数的等权重版本与市值加权版本之间的差距，本身就是一项市场广度指标。当等权重指数更接近高点时，平均成分股的表现超过了市值最大的成分股。当市值加权指数更接近高点时，则说明少数超大型公司正在支撑指数。这两种格局都不能预测未来走势；而在不合适的阶段离开市场，可能付出错过市场最佳交易日所述的代价。

计量方法

本页的所有数据均来自美国交易所行情中的分钟线，并汇总为每日收盘价。完整规则如下：

股票范围。 仅纳入在美国上市的运营公司。公司必须提交过一份截至过去400天内某一期间的SEC损益表。因此，ETF和基金不会出现在任一榜单中。杠杆ETF和反向ETF也会按名称排除，以进一步避免误纳入。
流动性门槛。 股票在过去20个日历日内的成交额必须至少达到20亿美元。扣除周末和节假日后，该窗口通常包含约12个交易日，因此平均每天的成交额约为1.5亿美元。这样既能确保榜单中的股票具备实际交易流动性，也能避免低成交量行情制造极端结果。
排除拆股，不进行调整。 过去460天内执行过股票拆分的任何股票代码，都会从筛选结果中剔除。该窗口有意超过完整一年，足以覆盖下方广度图表所回溯的最早价格。这是此类页面发布错误数据的最常见原因：一次20合1的反向拆股会在没有实际交易支撑的情况下，将报价放大20倍；未经调整的筛选结果就可能显示出异常强劲的52周新高。正向拆股则会产生相反效果，制造虚假的52周新低。
采用收盘价，而非盘中极值。 最高点和最低点均根据收盘价计算。某只股票可能在盘中突破52周高点，但收盘低于该水平，因此不会出现在本页。基于盘中成交记录的筛选结果，统计数量会更高。
交易时段必须完整。 一日收盘价取纽约时间15:30至16:00之间最后一根分钟线。仍在加载中的交易时段如果该半小时内没有分钟线，就无法进入计算；当前进行中的交易时段也不会被纳入。提前收盘的节假日交易时段同样因不满足条件而被排除。页面上方标注的日期，是最后一个通过完整性检查的交易时段，因此可能比日历日期晚一两天。
完整一年的历史数据。 在52周窗口内，股票至少需要有200个交易时段的数据，且首个交易时段必须距今至少350天。上市仅三个月的公司不会在本页显示52周高点。
1%的区间。 “处于52周高点”是指收盘价等于或处于过去52周最高收盘价下方1%以内；低点榜单采用相同方法。年初至今回报率以2025年最后一个交易日的收盘价为起点计算。

常见问题

在52周高点买入是个好主意吗？

没有统一答案，本文内容也不构成投资建议。本页面只能展示回溯性数据：距离52周高点不超过1%的股票，年初至今收益率中位数为38.3%；而较52周高点低出35%以上的股票，年初至今收益率中位数为-17.8%。这些数据描述的是这些股票过去一年的表现，而不是未来一年的表现。该标签表示股价在区间中的位置，并不代表估值水平。

股票触及52周低点意味着什么？

这意味着该股收盘价低于此前52周内的所有收盘价。在Sep 4, 2026，本筛选结果中有10只股票符合这一条件。股价触及低点，可能反映企业基本面恶化，也可能只是盈利企业长期缓慢下跌。仅凭价格无法判断是哪一种情况。

目前有多少只股票处于52周高点？

截至Sep 4, 2026收盘，本筛选结果中有31只股票收于52周高点或距离该高点不超过1%；收于52周低点或距离该低点不超过1%的股票有10只。本筛选覆盖规模较大、流动性较高的美国运营企业，并要求过去20个日历日的成交额超过20亿美元。因此，统计数量会少于覆盖所有上市股票的筛选结果。

52周高点如何计算？

取过去52周内的所有收盘价，并找出其中最高值。如果最新收盘价等于该最高值，则该股创下52周收盘高点。本页面还统计距离该水平不超过1%的股票，并剔除过去460天内发生过拆股的股票，因为拆股会改变报价，但不会改变持仓的价值。

上方每个面板都保存了其背后的SQL查询。打开任意表格下方的查询，即可审查筛选条件，或在Strasmore终端上运行查询。如需查看本周涨跌幅最大的股票，而不是过去一年的极值，请参阅本周涨幅最大和跌幅最大的股票。

#52-week high#52-week low#market breadth#stock screener#market data