Strasmore Research
Market Recap

Weekly Market Recap: The Week in Numbers

Matt ConnorBy Matt Connor

This is the past week in the US market, measured. Every figure below is read from a stored query against the real tape, and this page refreshes each week, so the numbers are always the most recent full week of trading.

The index scoreboard

QueryThe major index ETFs over the past week
The exact SQL behind every number
WITH sess AS (
    SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        argMin(toFloat64(open), toTimeZone(window_start, 'America/New_York')) AS o,
        argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
      AND window_start >= now() - INTERVAL 12 DAY AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, d
),
wk AS (SELECT ticker, argMin(o, d) AS wo, argMax(c, d) AS wc, min(d) AS d0, max(d) AS d1
       FROM sess WHERE d >= (SELECT max(d) FROM sess) - 6 GROUP BY ticker)
SELECT multiIf(ticker = 'SPY', 'S&P 500 (SPY)', ticker = 'QQQ', 'Nasdaq 100 (QQQ)', ticker = 'DIA', 'Dow (DIA)', 'Russell 2000 (IWM)') AS index_etf,
    round((wc / wo - 1) * 100, 2) AS week_return_pct,
    (SELECT formatDateTime(min(d0), '%b %e') FROM wk) AS week_start,
    (SELECT formatDateTime(max(d1), '%b %e') FROM wk) AS week_end
FROM wk
ORDER BY multiIf(ticker = 'SPY', 0, ticker = 'QQQ', 1, ticker = 'DIA', 2, 3)

For the week of Jul 9 to Jul 15, the S&P 500 (SPY) returned 1.03%, the Nasdaq 100 (QQQ) -0.03%, the Dow (DIA) 0.48%, and the small-cap Russell 2000 (IWM) 0.24%. The gap between the four is the week's story in one line: when the Nasdaq and the Russell diverge, the market is choosing between megacap growth and everything else.

Under the surface: breadth

An index level hides how many stocks actually participated. This counts them each session: among names that traded at least $500 million, how many closed higher than the session before, and how many closed lower.

QueryDaily market breadth: advancers vs decliners each session (names trading $500M+)
The exact SQL behind every number
WITH day AS (
    SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c,
        sum(toFloat64(close) * toFloat64(volume)) AS dv
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 14 DAY AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, d
),
chg AS (SELECT ticker, d, c, dv, lagInFrame(c) OVER (PARTITION BY ticker ORDER BY d) AS prev_c FROM day),
per_day AS (
    SELECT d,
        countIf(c > prev_c AND dv >= 500000000 AND prev_c > 3) AS advancers,
        countIf(c < prev_c AND dv >= 500000000 AND prev_c > 3) AS decliners,
        countIf(dv >= 500000000 AND prev_c > 3) AS total
    FROM chg WHERE d >= (SELECT max(d) FROM day) - 12 AND prev_c > 0
    GROUP BY d HAVING total >= 100
),
recent AS (SELECT d, advancers, decliners FROM per_day ORDER BY d DESC LIMIT 5)
SELECT d AS date, advancers, decliners FROM recent ORDER BY d ASC

The two lines are participation itself. When advancers run well above decliners across the week, a rising index has the whole market behind it; when they converge or cross, a green index is a handful of large names carrying a tape most stocks sat out. On the latest session, 76 names advanced against 143 that fell.

Sectors, best to worst

QueryThe eleven S&P 500 sectors over the past week
The exact SQL behind every number
WITH sess AS (
    SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        argMin(toFloat64(open), toTimeZone(window_start, 'America/New_York')) AS o,
        argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('XLB', 'XLC', 'XLE', 'XLF', 'XLI', 'XLK', 'XLP', 'XLRE', 'XLU', 'XLV', 'XLY')
      AND window_start >= now() - INTERVAL 12 DAY AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, d
),
wk AS (SELECT ticker, argMin(o, d) AS wo, argMax(c, d) AS wc FROM sess WHERE d >= (SELECT max(d) FROM sess) - 6 GROUP BY ticker)
SELECT multiIf(ticker = 'XLK', 'Technology', ticker = 'XLC', 'Communications', ticker = 'XLE', 'Energy',
    ticker = 'XLF', 'Financials', ticker = 'XLI', 'Industrials', ticker = 'XLB', 'Materials',
    ticker = 'XLP', 'Staples', ticker = 'XLRE', 'Real Estate', ticker = 'XLU', 'Utilities',
    ticker = 'XLV', 'Health Care', 'Consumer Disc.') AS sector,
    round((wc / wo - 1) * 100, 2) AS week_return_pct
FROM wk ORDER BY week_return_pct DESC

The sector board sorts the week into leaders and laggards. Communications led at 4.95%, and Health Care lagged at -2.13%. The spread between the top and bottom sector is a read on how much rotation drove the week versus a uniform move.

The week's biggest movers

The largest weekly gains among heavily traded stocks (over $1 billion changing hands, leveraged and inverse ETFs excluded):

QueryBiggest stock gainers over the past week
The exact SQL behind every number
WITH sess AS (
    SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        argMin(toFloat64(open), toTimeZone(window_start, 'America/New_York')) AS o,
        argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c,
        sum(toFloat64(close) * toFloat64(volume)) AS dv
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 12 DAY AND ticker NOT IN ('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 (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, d
),
wk AS (SELECT ticker, argMin(o, d) AS wo, argMax(c, d) AS wc, sum(dv) AS wd, count() AS n
       FROM sess WHERE d >= (SELECT max(d) FROM sess) - 6 GROUP BY ticker)
SELECT ticker, round((wc / wo - 1) * 100, 1) AS week_return_pct, round(wd / 1e9, 1) AS week_dollar_bn
FROM wk WHERE wd >= 1000000000 AND wo >= 10 AND n >= 4
ORDER BY (wc / wo - 1) DESC LIMIT 7

PYPL led all liquid names, up 26% on the week. The steepest declines ran the other way:

QueryBiggest stock losers over the past week
The exact SQL behind every number
WITH sess AS (
    SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        argMin(toFloat64(open), toTimeZone(window_start, 'America/New_York')) AS o,
        argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c,
        sum(toFloat64(close) * toFloat64(volume)) AS dv
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 12 DAY AND ticker NOT IN ('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 (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, d
),
wk AS (SELECT ticker, argMin(o, d) AS wo, argMax(c, d) AS wc, sum(dv) AS wd, count() AS n
       FROM sess WHERE d >= (SELECT max(d) FROM sess) - 6 GROUP BY ticker)
SELECT ticker, round((wc / wo - 1) * 100, 1) AS week_return_pct, round(wd / 1e9, 1) AS week_dollar_bn
FROM wk WHERE wd >= 1000000000 AND wo >= 10 AND n >= 4
ORDER BY (wc / wo - 1) ASC LIMIT 7

KORU fell the most among heavily traded stocks, at -96.3%. The full rolling leaderboard, refreshed continuously, is on the weekly movers page.

Where the volume concentrated

Dollars traded show where the market's attention actually went, which is not always where the biggest percentage moves were.

QueryWhere the volume went: most dollars traded over the past week
The exact SQL behind every number
WITH sess AS (
    SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS d,
        sum(toFloat64(close) * toFloat64(volume)) AS dv
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 12 DAY AND ticker NOT IN ('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 (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, d
),
wk AS (SELECT ticker, sum(dv) AS wd FROM sess WHERE d >= (SELECT max(d) FROM sess) - 6 GROUP BY ticker)
SELECT ticker, round(wd / 1e9, 1) AS week_dollar_bn FROM wk ORDER BY wd DESC LIMIT 8

MU saw the most dollar volume of any stock on the week, $86.9 billion. Heavy volume on a small price move is a crowded, liquid name changing hands; heavy volume on a large move is conviction. The names near the top of this list are usually the megacaps and the week's news stocks, and comparing it against the movers list above separates the stocks that moved a lot from the stocks that simply traded a lot.

How to read a week in the market

Four numbers, read together, describe a week better than any single index level. The index scoreboard says how the average dollar did, weighted toward the largest companies. Breadth says how many stocks actually took part: a strong index on thin breadth is a narrow, top-heavy move that history says tends not to last, while broad breadth is a durable, whole-market shift. The sector board says what kind of week it was, a wide spread between the best and worst sector points to rotation and stock-picking, a narrow spread to a tide that moved everything at once. The movers and the volume leaders then name the specific stocks that drove it, separating the loud percentage moves from the quiet, heavily traded giants. This recap lays all four side by side each week so the week reads as a picture, not a headline. With every figure computed from a stored query, none of it can drift from what the tape actually printed.

FAQ

What happened in the market this week?

For the week of Jul 9 to Jul 15, the S&P 500 returned 1.03% and the Nasdaq 100 -0.03%. Communications was the strongest sector and Health Care the weakest, and on the latest session 76 heavily traded stocks advanced against 143 that fell. The panels above have the full detail.

How often is this recap updated?

Every week. The page is computed from live queries over the trailing five trading sessions, so it always reflects the most recent full week. Subscribers get it as one email a week.

Where do the numbers come from?

Every figure is read from a stored query against the market tape (equities minute bars). Open the SQL under any panel to audit it, or run the same query yourself on the Strasmore terminal.

Want this in your inbox? It is the weekly recap subscribers receive: one email a week, the market's week in numbers, every figure auditable. For the day-to-day, the movers and short-interest pages refresh continuously.

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