Strasmore Research
Muhtasari wa soko Matt ConnorNa Matt Connor · Imeboreshwa 2026-07-24 · data as of July 24, 2026 · refreshed weekly

Hisa zenye mabadiliko makubwa mwezi huu

Orodha ya hisa zinazopanda na kushuka zaidi mwezi huu kulingana na rekodi za soko, ikionyesha kiasi cha dola zilizofanyiwa biashara kwa kila mabadiliko.

Hapa chini ni orodha ya hisa zenye mabadiliko makubwa zaidi mwezi huu, ikitolewa moja kwa moja kutoka kwenye rekodi za soko, ikijumuisha 21 zinazokamilika za biashara kuanzia Jun 23 hadi Jul 22. Orodha hii inajumuisha kampuni zinazofanya kazi ambazo zilifanya biashara ya angalau $1 billion katika kipindi hicho, hivyo kila mabadiliko kwenye orodha hii una mzunguko wa fedha halisi. Mifuko ya uwekezaji ya leveraged na inverse haijajumuishwa, na kipindi hiki huendelea mbele kila baada ya kukamilika kwa vikao vipya vya biashara.

Hisani kubwa zaidi za hisa mwezi huu

Jedwali hili linapanga hisa zenye ongezeko kubwa zaidi katika kipindi husika. Kila safu inaonyesha kiasi cha dola kilichotumika pamoja na asilimia, jambo ambalo hutofautisha upyaji wa bei wenye mzunguko mkubwa na ule mdogo.

UlizaHisa zinazopata faida kubwa zaidi mwezi huu (kampuni zinazofanya biashara ya $1B+ katika kipindi hiki)
SQL halisi nyuma ya kila namba
WITH window_days AS (
    SELECT d
    FROM (
        SELECT d, max(d) OVER () AS last_full
        FROM (
            SELECT d
            FROM (
                SELECT d, bars, medianExact(bars) OVER () AS typical_bars
                FROM (
                    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d, count() AS bars
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE window_start >= now() - INTERVAL 34 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
                    GROUP BY d
                    HAVING d < toDate(toTimeZone(now(), 'America/New_York'))
                )
            )
            WHERE bars >= 0.85 * typical_bars
        )
    )
    WHERE d > last_full - 30
)
SELECT ticker,
       round((argMax(c, d) / argMin(o, d) - 1) * 100, 1) AS month_return_pct,
       round(sum(dollars) / 1e9, 1) AS month_dollar_bn
FROM (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMin(toFloat64(open), window_start) AS o,
           argMax(toFloat64(close), window_start) AS c,
           sum(toFloat64(close) * toFloat64(volume)) AS dollars
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 34 DAY
      AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM window_days)
      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 NOT IN ('SPCX')
      AND ticker 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 ticker IN (SELECT ticker FROM global_markets.stocks_ratios
                     WHERE market_cap > 0)
      AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 45 AND today())
    GROUP BY ticker, d
)
GROUP BY ticker
HAVING sum(dollars) >= 1000000000 AND argMin(o, d) >= 10 AND count() >= 15
ORDER BY argMax(c, d) / argMin(o, d) DESC
LIMIT 10

CRNX iliongoza orodha kwa asilimia 136.1% huku bilioni $9.7 zikifanyiwa biashara, ikimtanguliza PBF kwa 65.9% na MAN kwa 62.1%. Ongezeko la ukubwa huo kwa mwezi moja kwa kawaida huonekana kwanza kama relative volume iliyopanda dhidi ya average daily volume ya hisa hiyo, na mara nyingi huanza na overnight gap katika kikao kimoja badala ya kupanda taratibu katika kipindi chote.

Hisia kubwa za kushuka kwa hisa mwezi huu

Mchakato wa kinyume: kushuka kwa kasi zaidi katika kipindi kilichochangwa, miongoni mwa kampuni zinazovuka kizingiti cha $1 billion.

UlizaHisa zinazopata hasara kubwa zaidi mwezi huu (kampuni zinazofanya biashara ya $1B+ katika kipindi hiki)
SQL halisi nyuma ya kila namba
WITH window_days AS (
    SELECT d
    FROM (
        SELECT d, max(d) OVER () AS last_full
        FROM (
            SELECT d
            FROM (
                SELECT d, bars, medianExact(bars) OVER () AS typical_bars
                FROM (
                    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d, count() AS bars
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE window_start >= now() - INTERVAL 34 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
                    GROUP BY d
                    HAVING d < toDate(toTimeZone(now(), 'America/New_York'))
                )
            )
            WHERE bars >= 0.85 * typical_bars
        )
    )
    WHERE d > last_full - 30
)
SELECT ticker,
       round((argMax(c, d) / argMin(o, d) - 1) * 100, 1) AS month_return_pct,
       round(sum(dollars) / 1e9, 1) AS month_dollar_bn
FROM (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMin(toFloat64(open), window_start) AS o,
           argMax(toFloat64(close), window_start) AS c,
           sum(toFloat64(close) * toFloat64(volume)) AS dollars
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 34 DAY
      AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM window_days)
      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 NOT IN ('SPCX')
      AND ticker 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 ticker IN (SELECT ticker FROM global_markets.stocks_ratios
                     WHERE market_cap > 0)
      AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 45 AND today())
    GROUP BY ticker, d
)
GROUP BY ticker
HAVING sum(dollars) >= 1000000000 AND argMin(o, d) >= 10 AND count() >= 15
ORDER BY argMax(c, d) / argMin(o, d) ASC
LIMIT 10

CLBK ndiye aliyeshuka kwa kasi zaidi kwa -44.6% kwenye $1.1 billion zilizofanyiwa biashara, huku NVTS akiwa wa pili kwa -41.6%. Angalia safu ya dollar kwenye chati hii. Kushuka kwa thamani katika soko lenye mzunguko mkubwa ni tukio tofauti na lile la soko tulivu, na mambo haya mawili yanatofautishwa hapa badala ya kudhaniwa.

Swali lile lile katika muda mrefu wa kulinganisha

Swali moja, vipindi vitatu. Jina linaweza kuwa kilele cha chati moja na lisionekane kabisa kwenye nyingine mbili.

Mambo matatu haya yameundwa kando na skrini zao si sawa. Ukurasa wa kila wiki unafuata vigezo vyake vya ukwasi na kiwango cha chini cha kila kikao katika kipindi cha siku tano, na ukurasa wa mwaka hadi sasa unatumia wastani wa thamani ya dola ya kila siku badala ya jumla ya thamani iliyofanyiwa biashara katika kipindi chote. Kila ukurasa unaelezea sheria zake katika sehemu yake ya mbinu, hivyo soma sehemu hizo kabla ya kuhamisha jina kutoka chati moja kwenda nyingine.

Pengo moja kubwa linatawala chati ya kila wiki lakini linapotea kabisa katika kipindi cha mwaka. Mabadiliko ya taratibu ya kila mwezi hayafiki hata chati ya kila wiki. Kusoma chati hizi tatu kwa pamoja ndiyo njia rahisi zaidi ya kutofautisha tukio la siku moja na mwelekeo wa muda mrefu.

Hali ya soko katika kipindi hicho hicho

Utendaji wa hisa mwezi mmoja unaweza kutofautiana na utendaji wa soko kwa ujumla. Viashiria vikuu vinne, kila kimezingatia thamani ya kwanza wakati wa kufungua ndani ya kipindi hicho:

UlizaViashiria vikuu vinne katika kipindi hicho, vilivyorekebishwa kulingana na bei ya kufungua ya kwanza
SQL halisi nyuma ya kila namba
WITH window_days AS (
    SELECT d
    FROM (
        SELECT d, max(d) OVER () AS last_full
        FROM (
            SELECT d
            FROM (
                SELECT d, bars, medianExact(bars) OVER () AS typical_bars
                FROM (
                    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d, count() AS bars
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE window_start >= now() - INTERVAL 34 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
                    GROUP BY d
                    HAVING d < toDate(toTimeZone(now(), 'America/New_York'))
                )
            )
            WHERE bars >= 0.85 * typical_bars
        )
    )
    WHERE d > last_full - 30
)
SELECT session,
       round(anyIf(cum_pct, ticker = 'SPY'), 2) AS sp500_pct,
       round(anyIf(cum_pct, ticker = 'QQQ'), 2) AS nasdaq100_pct,
       round(anyIf(cum_pct, ticker = 'DIA'), 2) AS dow_pct,
       round(anyIf(cum_pct, ticker = 'IWM'), 2) AS russell2000_pct
FROM (
    SELECT ticker,
           formatDateTime(d, '%Y-%m-%d') AS session,
           100 * (c / first_value(o) OVER (PARTITION BY ticker ORDER BY d) - 1) AS cum_pct
    FROM (
        SELECT ticker,
               toDate(toTimeZone(window_start, 'America/New_York')) AS d,
               argMin(toFloat64(open), window_start) AS o,
               argMax(toFloat64(close), window_start) AS c
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE window_start >= now() - INTERVAL 34 DAY
          AND ticker IN ('SPY', 'QQQ', 'DIA', 'IWM')
          AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM window_days)
          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
        GROUP BY ticker, d
    )
)
GROUP BY session
ORDER BY session

Katika kikao cha mwisho, kiashiria cha S&P 500 kilikuwa 1.85%, kiashiria cha Nasdaq 100 kilikuwa -1.47%, kiashiria cha Dow kilikuwa 1.33% na kiashiria cha Russell 2000 kilikuwa 0.17%. Mifuko minne, kipindi kimoja, majibu manne tofauti. Sasa linganisha orodha ya washindi na kiwango hicho: kilele cha washindi kilivuka 136.1% katika vikao hivyo hivyo. Kampuni binafsi zinatofautiana zaidi kuliko mifuko inayozishikilia, jambo ambalo ndilo sababu kuu kwa nini orodha ya mabadiliko ni orodha ya majina ya kampuni moja moja.

Jinsi makampuni maarufu yalivyofanya kazi

Wasomaji wanaotafuta hisa zenye utendaji bora zaidi mwezi huu kwa kawaida hutaka pia kujua hali ya kampuni ambazo tayari wanamiliki. Kikundi kilichofungwa cha mega-caps nane maarufu, katika kipindi na muundo ule ule:

UlizaMajina mazito nane ya mega-cap katika kipindi hicho hicho
SQL halisi nyuma ya kila namba
WITH window_days AS (
    SELECT d
    FROM (
        SELECT d, max(d) OVER () AS last_full
        FROM (
            SELECT d
            FROM (
                SELECT d, bars, medianExact(bars) OVER () AS typical_bars
                FROM (
                    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d, count() AS bars
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE window_start >= now() - INTERVAL 34 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
                    GROUP BY d
                    HAVING d < toDate(toTimeZone(now(), 'America/New_York'))
                )
            )
            WHERE bars >= 0.85 * typical_bars
        )
    )
    WHERE d > last_full - 30
)
SELECT ticker,
       round((argMax(c, d) / argMin(o, d) - 1) * 100, 1) AS month_return_pct
FROM (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMin(toFloat64(open), window_start) AS o,
           argMax(toFloat64(close), window_start) AS c
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= now() - INTERVAL 34 DAY
      AND ticker IN ('AAPL', 'MSFT', 'NVDA', 'AMZN', 'GOOGL', 'META', 'TSLA', 'AVGO')
      AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM window_days)
      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 NOT IN (SELECT ticker FROM global_markets.stocks_splits
                         WHERE execution_date BETWEEN today() - 45 AND today())
    GROUP BY ticker, d
)
GROUP BY ticker
HAVING count() >= 15
ORDER BY argMax(c, d) / argMin(o, d) DESC

Kikundi hicho kilianza na 11.5% kwa META kileleni hadi -4.7% kwa TSLA chini kabisa. Hazikufanya kazi kama kundi moja, jambo ambalo ni hali ya kawaida badala ya kuwa ubaguzi. Uanachama wa index huunganisha kampuni hizi; mishororo yao ya mapato haina uhusiano mkubwa. Kikundi hiki ni orodha iliyofungwa ya majina nane badala ya upangaji wa nafasi, hivyo kikitumie kama mwongozo na si kama dai kuhusu kampuni zipi ni kubwa zaidi.

Jinsi mwezi wa kawaida unavyoonekana

Majaribio ya kwanza yanaonyesha upande wa mwisho wa takwimu. Hii inaonyesha mwili wa takwimu. Kampuni zote 1167 zilizochujwa, zimepangwa katika makundi kulingana na faida zao katika kipindi hicho:

UlizaMahali kila kampuni ilipofika: faida za mwezi kwa kundi
SQL halisi nyuma ya kila namba
WITH window_days AS (
    SELECT d
    FROM (
        SELECT d, max(d) OVER () AS last_full
        FROM (
            SELECT d
            FROM (
                SELECT d, bars, medianExact(bars) OVER () AS typical_bars
                FROM (
                    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d, count() AS bars
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE window_start >= now() - INTERVAL 34 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
                    GROUP BY d
                    HAVING d < toDate(toTimeZone(now(), 'America/New_York'))
                )
            )
            WHERE bars >= 0.85 * typical_bars
        )
    )
    WHERE d > last_full - 30
)
SELECT tup.1 AS return_bucket,
       tup.3 AS stocks
FROM (
    SELECT arrayJoin(arrayZip(
               ['down 20% or more', 'down 10% to 20%', 'down 0% to 10%',
                'up 0% to 10%', 'up 10% to 20%', 'up 20% or more'],
               [1, 2, 3, 4, 5, 6],
               [countIf(r <= -20), countIf(r > -20 AND r <= -10), countIf(r > -10 AND r < 0),
                countIf(r >= 0 AND r < 10), countIf(r >= 10 AND r < 20), countIf(r >= 20)])) AS tup
    FROM (
        SELECT ticker,
               (argMax(c, d) / argMin(o, d) - 1) * 100 AS r
        FROM (
            SELECT ticker,
                   toDate(toTimeZone(window_start, 'America/New_York')) AS d,
                   argMin(toFloat64(open), window_start) AS o,
                   argMax(toFloat64(close), window_start) AS c,
                   sum(toFloat64(close) * toFloat64(volume)) AS dollars
            FROM global_markets.delayed_stocks_minute_aggs
            WHERE window_start >= now() - INTERVAL 34 DAY
              AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM window_days)
              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 NOT IN ('SPCX')
              AND ticker 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 ticker IN (SELECT ticker FROM global_markets.stocks_ratios
                             WHERE market_cap > 0)
              AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                                 WHERE execution_date BETWEEN today() - 45 AND today())
            GROUP BY ticker, d
        )
        GROUP BY ticker
        HAVING sum(dollars) >= 1000000000 AND argMin(o, d) >= 10 AND count() >= 15
    )
)
ORDER BY tup.2

52 ya kampuni zilizochujwa zilipata faida ya 20% au zaidi katika kipindi hicho, na 50 zilipoteza 20% au zaidi. Makundi mawili yanayokaribia hali ya kutobadilika yalikuwa na majina 300 na 515. Chati hiyo ndiyo muundo unaotumika kuandaa orodha ya hisa zinazocheza sana, na zile kumi bora ni upande wa kulia kabisa. Soma majedwali hapo juu kwa kuzingatia hilo: mabadiliko ya mwezi ya kumi bora ni takwimu inayojitenga, si mwezi wa kawaida kwa hisa ya kawaida.

Sababu zinazofanya hisa kuingia kwenye orodha ya mabadiliko ya kila mwezi

Mwezi ni muda mrefu wa kutosha kuunda matukio kadhaa, lakini ni mfupi kiasi kwamba tukio moja kwa kawaida hutawala. Majina kwenye orodha hizi mara nyingi huendana na matukio yaliyoainishwa kwenye kalenda ya kampuni. Matokeo ya kila robo mwaka ndiyo yanayotokea mara nyingi zaidi: matokeo bora au duni yanabadilisha bei katika kikao kimoja, na mwezi mzima hufanya biashara karibu na kiwango kipya hicho. Mabadiliko ya mwongozo wa matarajio (guidance) hufanya kazi hiyo hiyo bila kuhitaji matokeo ya kampuni.

Habari za muungano na ununuzi (M&A) huacha alama ya kipekee zaidi kwenye orodha ya kila mwezi. Hisa hupanda hadi kiwango karibu na bei ya dili lililotangazwa na kisha hupata utulivu, huku thamani ya dola inayofanyiwa biashara ikiwa kubwa wakati mabadiliko ya asilimia yanatulia. Ikiwa jina lililo karibu juu ya orodha ya wenye faida kubwa linaonyesha kiasi kikubwa cha dola na bei ambayo imetulia katikati ya mwezi, mwelekeo huo unapaswa kuchunguzwa kabla ya mambo mengine.

Mabadiliko ya sekta yana athari kubwa kwa mwezi, tofauti na athari zao za wiki moja. Mabadiliko katika viwango vya riba, mafuta, au bei ya kumbukumbu (memory pricing) huathiri kundi zima kwa pamoja, ndiyo sababu orodha za kila mwezi mara nyingi huonyesha makundi ya biashara zinazofanana badala ya majina kumi yasiyohusiana. Matendo ya wachambuzi, kuongezwa au kuondolewa kwa hisa kwenye index, matokeo ya majaribio na udhibiti, na short squeezes huchukua sehemu nyingine ya mabadiliko. Hakuna kati ya hayo linaloonekana kwa asilimia pekee, ndiyo sababu safu ya dola zinazofanyiwa biashara na muda wa soko huwekwa pembeni ya kila mabadiliko hapa.

Jinsi hii inavyopimwa

Kila kiwango cha chini kimeelezwa hapa badala ya kuachwa kwa njia ya kudokeza. Skrini yenye sheria zisizo wazi huonekana kama data lakini kwa kweli ni maoni.

  • Kipindi. Siku 30 za kalenda zilizopita kuanzia kwenye kikao cha hivi karibuni ambacho data yake imekamilika, ambacho kilikuja kuwa 21 sessions kutoka Jun 23 hadi Jul 22. Sheria mbili huamua ikiwa kikao kinakidhi vigezo. Lazima kiwe kimefunga kabla ya tarehe ya sasa huko New York, hivyo kikao kinachoendelea hakipo kwenye wigo huu. Idadi ya mishale (bars) ya saa za kawaida lazima ifikie 85% ya wastani wa kikao katika ukaguzi wa siku 34, jambo ambalo huondoa siku ya mapema ambayo data yake bado haijakamilika. Kikao kifupi cha sikukuu hutoa takriban nusu ya siku ya kawaida ya mishale, hivyo sheria hiyo hiyo hukiondoa badala ya kulipima ikiwa nusu imekamilika.
  • Faida (Return). Bei ya kwanza ya kufungua katika saa za kawaida ndani ya kipindi hadi bei ya kufunga ya kikao cha mwisho cha saa za kawaida, iliyoundwa kutokana na minute bars. Saa za kawaida pekee, hivyo bei za kabla ya soko (premarket) na baada ya soko (after-hours) hazijumuishwi. Ni faida ya bei (price return): gawio halijajumlishwa tena.
  • Viwango vya chini (Floors). Angalau $1 billion iliyofanyiwa biashara katika kipindi chote, bei ya kufungua ya $10 au zaidi, na rekodi katika angalau siku 15 za kipindi hicho. Kiwango cha dola ni jumla ya mwezi, jambo linalofanya iwe rahisi kulipita kuliko takwimu hiyo hiyo inayopimwa kwa wiki moja.
  • Vitu vinavyotolewa. Mifuko ya leverage na inverse inaondolewa kwa jina. Kila ticker lazima pia iwe na misingi ya kampuni (fundamentals), mtaji wa soko (market capitalization) kwenye rekodi zetu, katika data yetu ya marejeleo. Sheria hiyo ya pili huondoa ETFs, mifuko iliyofungwa (closed-end funds), imani za bidhaa (commodity trusts) na kundi linalokua kwa kasi la bidhaa za leverage za hisa moja, ambavyo vingeweza kujaza mbao zote mbili. Jedwali hilo la marejeleo lina mstari mmoja kwa kila ticker badala ya picha ya kila siku, hivyo skrini husoma jedwali zima na si tarehe moja inayofanya uamuzi wa nani anafaa. Ticker yoyote ambayo imefanya mgawanyo wa hisa (stock split) katika siku 45 zilizopita huondolewa badala ya kurekebishwa nyuma (back-adjusted), kwa sababu mgawanyo hubadilisha bei ya kila hisa bila kubadilisha thamani ya umiliki. Alama moja ambayo vyanzo vya watoa huduma wameibadilisha kati ya kampuni mbili tofauti inaondolewa kwa jina.
  • Jopo la mega-cap. Kikundi maalum cha kampuni nane kubwa za Marekani zinazomilikiwa na watu wengi, zilizoorodheshwa kwa jina kwenye SQL ya jopo hilo. Inatumia kipindi kilekile na uundaji wa faida uleule kama mbao hizo, na haijajumuishwa kwenye ukaguzi wa thamani ya dola.
  • Nini hakijatumika. Hakuna chujio cha sekta, hakuna kiwango cha chini cha mtaji wa soko na hakuna hitaji la kujiunga na index, hivyo kampuni ndogo yenye mzunguko mkubwa wa biashara inaweza kuwa kwenye mbao pamoja na kampuni kubwa. Ulinganishaji ni kwa mabadiliko ya asilimia pekee, ndiyo maana safu ya dola-zilizofanyiwa biashara imechapishwa kando yake.

Jopo moja linabeba risiti ya mambo yote: kikao ambacho kipindi kilikihusisha, na ulimwengu ambao mbao kila moja ilikaguliwa kutoka.

UlizaKipindi na ulimwengu uliochujwa nyuma ya kila chati kwenye ukurasa huu
SQL halisi nyuma ya kila namba
WITH window_days AS (
    SELECT d
    FROM (
        SELECT d, max(d) OVER () AS last_full
        FROM (
            SELECT d
            FROM (
                SELECT d, bars, medianExact(bars) OVER () AS typical_bars
                FROM (
                    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d, count() AS bars
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE window_start >= now() - INTERVAL 34 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
                    GROUP BY d
                    HAVING d < toDate(toTimeZone(now(), 'America/New_York'))
                )
            )
            WHERE bars >= 0.85 * typical_bars
        )
    )
    WHERE d > last_full - 30
),
screened AS (
    SELECT ticker
    FROM (
        SELECT ticker,
               toDate(toTimeZone(window_start, 'America/New_York')) AS d,
               argMin(toFloat64(open), window_start) AS o,
               sum(toFloat64(close) * toFloat64(volume)) AS dollars
        FROM global_markets.delayed_stocks_minute_aggs
        WHERE window_start >= now() - INTERVAL 34 DAY
          AND toDate(toTimeZone(window_start, 'America/New_York')) IN (SELECT d FROM window_days)
          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 NOT IN ('SPCX')
          AND ticker 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 ticker IN (SELECT ticker FROM global_markets.stocks_ratios
                         WHERE market_cap > 0)
          AND ticker NOT IN (SELECT ticker FROM global_markets.stocks_splits
                             WHERE execution_date BETWEEN today() - 45 AND today())
        GROUP BY ticker, d
    )
    GROUP BY ticker
    HAVING sum(dollars) >= 1000000000 AND argMin(o, d) >= 10 AND count() >= 15
)
SELECT formatDateTime(w.first_d, '%b %e') AS first_session_date,
       formatDateTime(w.last_d, '%b %e') AS last_session_date,
       w.n AS sessions,
       (SELECT count() FROM global_markets.stocks_ratios WHERE market_cap > 0) AS companies_on_file,
       (SELECT count() FROM screened) AS companies_screened
FROM (SELECT min(d) AS first_d, max(d) AS last_d, count() AS n FROM window_days) AS w

Skrini ilianzia na 4121 tickers zenye mtaji wa soko kwenye jedwali la marejeleo na ilimalizia na 1167 kampuni zilizopita viwango vya thamani ya dola, bei na kikao. Takwimu zote mbili zinahifadhiwa kwa kila upangaji upya wa ukurasa huu, hivyo mzunguko wa ulimwengu uliopungua unaonekana kwenye risiti badala ya kupita bila kugundulika.

Maswali ya Mara kwa Mara

Ni zipi hisa zilizoimarika zaidi mwezi huu?

Kwa kipindi kinachofikia Jul 22, ongezeko kubwa zaidi miongoni mwa kampuni zinazofanya biashara zaidi ya $1 billion zilikuwa CRNX kwa 136.1% na PBF kwa 65.9%. Orodha kamili ya juu kumi inapatikana kwenye jedwali la kwanza ukurasa huu, na inajisasisha kila baada ya mzunguko mpya wa soko kukamilika.

Ni zipi hisa zilizoshuka zaidi mwezi huu?

CLBK ilikuwa na kushuka kwa kasi zaidi kwa -44.6%, ikifuatiwa na NVTS kwa -41.6%. Zote zinapimwa kwa kutumia idadi sawa ya vikao vya 21, huku kukiwa na kizingiti cha thamani ya biashara ya $1 billion kama ilivyo kwenye orodha ya hisa zinazoimarika.

Mapato ya hisa ya mwezi yanapimwaje?

Mapato ya mwezi hapa ni mabadiliko kutoka bei ya kufungua kikao cha kawaida cha kwanza ndani ya kipindi hadi bei ya kufunga kikao cha kawaida cha mwisho, inayoundwa kutokana na minute bars. Ni mapato ya bei, hivyo gawadi hazijajumlishwa, na kampuni yoyote yenye mgawanyo wa hisa (stock split) karibu na kipindi hicho huondolewa badala ya kurekebishwa (back-adjusted).

Kwa nini ETFs za leverage huondolewa kwenye orodha ya hisa zinazocheza?

Mfuko wa leverage au inverse unazidisha mabadiliko ya kila siku ya index au hisa moja, hivyo unaweza kuwa juu ya orodha ya kawaida ya mabadiliko kwenye siku ya kawaida ya soko bila kuwa na habari yoyote ya kampuni. Kuwaondoa kunahakikisha orodha hii inabaki kuwa ya kampuni zinazofanya kazi. Jinsi ETFs za leverage zinavyofanya kazi inaelezea marekebisho ya kila siku na mkengeko (drift) unaotokana na kumiliki mfuko huo kwa mwezi mmoja.

Kila sehemu hapo juu ni swali lililohifadhiwa likiwa na SQL yake. Fungua moja, badilisha kizingiti cha dola au urefu wa kipindi, na uchunge soko hilo lenyewe kwenye terminal ya Strasmore.

#market movers#gainers and losers#monthly returns#market data