$AVGO #일봉

Channel
실버만삭스의 루틴
@silvermansachs_routine
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
7,884subscribers
-229 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
Register entry
| Telegram ID | -1001649043417 |
|---|---|
| Type | Channel |
| Usernames | @silver_mansachs @silvermansachs_routine |
| Description | 롱·숏 모두에게 균형잡힌 시각을 제공합니다. ※본 채널은 매수·매도를 권하지 않습니다. |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 7 November 2026 |
| Measurements held | 27 |
| On Telegram | t.me/silvermansachs_routine |
Topic
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 12 September 2026 and assigned it the closest of 31 fixed categories, at 95% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Nov 2026, 17:22 | 7,884 | no change |
| 7 Nov 2026, 16:47 | 7,884 | +7,882 |
| 2 Nov 2026, 00:38 | 2 | -8,177 |
| 30 Oct 2026, 03:36 | 8,179 | -33 |
| 27 Oct 2026, 00:44 | 8,212 | -8 |
| 24 Oct 2026, 05:20 | 8,220 | -9 |
| 21 Oct 2026, 06:27 | 8,229 | -5 |
| 18 Oct 2026, 13:46 | 8,234 | +4 |
| 15 Oct 2026, 17:41 | 8,230 | +41 |
| 12 Oct 2026, 12:11 | 8,189 | +23 |
| 5 Oct 2026, 19:40 | 8,166 | +122 |
| 28 Sept 2026, 12:40 | 8,044 | +18 |
| 18 Sept 2026, 12:37 | 8,026 | -14 |
| 14 Sept 2026, 06:15 | 8,040 | -12 |
| 10 Sept 2026, 15:17 | 8,052 | -5 |
| 5 Sept 2026, 16:01 | 8,057 | -4 |
| 1 Sept 2026, 15:35 | 8,061 | -1 |
| 29 Aug 2026, 20:42 | 8,062 | -11 |
| 26 Aug 2026, 18:05 | 8,073 | -3 |
| 23 Aug 2026, 17:05 | 8,076 | first reading |
Engagement
251 posts held, back to 5 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 20 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 14.4%
- avg views ÷ 7,884 subscribers
- Avg views / post
- 1,130
- 20 posts measured
- Reaction rate
- 0.24%
- reactions ÷ views · ER floor
- Posts in window
- 20
- of 251 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 17 of 20 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 7 November 2026 |
|---|---|
| Posts held | 251 (5 August 2026 – 7 November 2026) |
| Views total | 22,679 |
| Reactions total | 48 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Nov 2026, 17:22 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
- Photos
- 18
- Links
- 1
Lifetime counters from Telegram’s own channel header, read 7 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
- Video runtime
- 33s
- Average length
- 33s
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
266 reactions across 57 posts, in 9 distinct kinds. The most used accounts for 82.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| custom 5404870433939922908 | 218 | 82.0% | |
| ❤ | 22 | 8.27% | |
| 🔥 | 9 | 3.38% | |
| 🤷 | 4 | 1.50% | |
| 🤷♂ | 4 | 1.50% | |
| 😁 | 3 | 1.13% | |
| 🤣 | 3 | 1.13% | |
| 🥱 | 2 | 0.752% | |
| 🤮 | 1 | 0.376% |
Custom emoji. One row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The count beside it is Telegram’s.
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 69 of the 251 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 280 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 251 most recent posts we hold, published 5 August 2026 to 7 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Recent posts
Telegram’s own ‘edited’ marker: 3 of 20 posts held from the last 30 days (15.0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
📊 모건스탠리|미 중간선거 결과가 코스피에 주는 의미 ━━━━━━━━━━━━ 📝 세줄요약 1️⃣ 민주당이 상·하원 모두 장악하고 텍사스·오하이오·조지아·아이오와 주지사도 가져감 → 미국 데이터센터 인허가 속도**가 AI 심리의 변수로 부상 2️⃣ 선거·중동 같은 외부 변수로 관망하던 자금이 있음. 변수가 걷히면 **11월 코스피 변곡 가능. 관건은 AI 심리 3️⃣ 업종 차별화 예상: 금융·AI 외 설비투자·퀄리티 소비재 유리. 반도체·발전·방산은 블루웨이브를 상당 부분 이미 반영했지만 단기 흐름은 미지근 ━━━━━━━━━━━━ 📉 시장 반응 (11/3) • 미국 S&P·나스닥 각 +1.7%. 다만 AI를 쓰는 기업(adopter)은 강세, AI 인프라 기업(enabler)은 약세 • 코스피 5,683.80 (+0.62%), 오전 약세 후…
❤1
#중국 # 모건스탠리 중국 주식전략: 미 중간선거 영향 ## 세줄요약 1. 민주당이 의회를 장악해도 미·중 갈등의 방향은 그대로이고, 형태만 바뀐다. 정책이 급변할 가능성은 줄지만 대중 강경 기조는 초당적이다. 2. 전달 경로는 4개다: AI 차별화, 무역 안정, 유가·금리 하락, 녹색에너지 복귀. 지수 전체가 재평가되기보다는 업종 안에서 선별적 로테이션**이 일어날 것으로 본다. 3. 밸류는 역사적 저점 대비 10% 위에 있고, 심리는 과매도이며, 국가대 매수와 자사주 매입 대출 같은 안정화 장치가 있다. 그래서 **시장 전체에 공격적으로 베팅하기보다 여러 업종에서 고르는 종목 선별**을 권한다. --- ## 핵심 논리: "방향이 아니라 형태" - 민주당 의회는 행정부를 재정·외교·내정 전반에서 견제한다. 2025년처럼 행정명령으로 …
❤2
해봤잖슴
❤3
공매도 금지시 일어나는일 1/ 주말에 공매도 금지 발표 2/ 채널장들 공매도 잔고 많은 순서대로 나래비 세워서 올림 3/ 개장과 함께 숏 많은 애들만 불꽃 슈팅 4/ 개인들 그거 먹으려고 홀딩하던 주식 던지고 저퀄리티 주식에 달려듬 5/ 외국인 롱숏 청산으로 퀄리티 주식 초과 하락 6/ 지수 횡보에 잡주 전성시대
🔥2
$APP 앱러빈 +31.3% 마감 | 3Q26: "고쳤다"던 말을 숫자로 증명했습니다 (회사소개) 앱러빈은 모바일 게임 광고 시장의 1위 회사입니다. 게임사가 "우리 게임을 깔아줄 유저"를 찾을 때, 앱러빈의 AI 광고 엔진 액손(Axon)이 수많은 게임 안 광고 자리 중 어디에 어떤 광고를 띄워야 설치하고 결제까지 할지 예측해 연결해주는데요. 이 엔진이 워낙 잘 맞혀서 매출의 80% 넘게 이익으로 남기는, 광고 회사 중에서도 손꼽히게 마진이 높은 회사입니다. 최근엔 같은 엔진으로 게임이 아닌 쇼핑몰(이커머스) 광고주까지 끌어들이며 두 번째 성장축을 만들고 있습니다. 배경부터 보면, 2분기에 앱러빈은 모델 개선이 없어 실적이 기대에 못 미쳤고 주가는 연초 대비 약 -40%까지 빠졌습니다. 당시 회사는 "경쟁 탓이 아니라 우리 실수(foot…
❤4
#게임 #모바일 $U
$U 유니티 +15.0% 마감 (11/5) | 3Q26: AI에 잡아먹힐 줄 알았던 게임엔진이 광고로 살아났습니다 (회사소개) 유니티는 게임을 만드는 도구인 "게임엔진" 회사입니다. 전 세계 모바일 게임 상당수가 유니티로 만들어지는데요. 사업은 둘로 나뉩니다. 개발자에게 엔진을 구독으로 파는 Create, 그리고 그렇게 만들어진 게임 안에 광고를 붙여 돈을 버는 Grow입니다. Grow의 핵심은 2025년 출시한 AI 광고 엔진 벡터(Vector)로, 어떤 유저에게 어떤 게임 광고를 보여줘야 설치하고 돈을 쓸지 예측합니다. 몇 년 전 광고 사업에서 크게 헛발질하며 앱러빈에 밀렸던 회사라, 벡터가 반격의 무기입니다. 숫자는 기대를 크게 넘었습니다. 벡터가 전 분기 대비 +40% 성장해 회사 가이던스(+20%)의 2배를 냈고, 전체 매출은 +…
❤2
ㅋㅋ
$IRM 아이언마운틴 -11.4% 마감 (11/5) | 3Q26: 문서 창고가 데이터센터 회사가 되는 중입니다 (회사소개) 아이언마운틴은 원래 기업의 종이 문서를 맡아 보관해주는 "문서 창고" 회사입니다. 은행·병원·정부가 법적으로 오래 보관해야 하는 서류를 맡기는데, 한번 들어오면 잘 안 나가서 38년 연속 보관 매출이 늘어난 안정적인 현금 창출원인데요. 이 현금을 바탕으로 지금은 두 가지 성장 사업을 키우고 있습니다. 하이퍼스케일러에게 임대하는 데이터센터(DC), 그리고 기업·데이터센터에서 나온 서버와 메모리를 회수해 데이터를 지우고 재판매하는 IT 자산 처분(ALM)입니다. 리츠(REIT)라서 배당도 꾸준히 올립니다. 숫자는 좋았습니다. 매출 +13%, 주당 AFFO(리츠의 실질 이익) +16%로 둘 다 자체 전망을 넘었고, 연간 가…
❤2
패닉할만해
😁3
$NET 클라우드플레어 +14.7% (11/5 시간외) | 3Q26: AI 에이전트가 늘수록 문지기가 바빠집니다 (회사소개) 클라우드플레어는 인터넷 트래픽이 웹사이트에 도착하기 전에 한 번 거쳐 가는 "관문"을 운영하는 회사입니다. 전 세계 데이터센터에 서버를 깔아두고 웹사이트를 빠르게 전달하고(CDN), 디도스 같은 공격과 악성 봇을 걸러주는데요. 전 세계 웹사이트 상당수가 이 관문 뒤에 있다 보니, 누가 어떤 트래픽을 보내는지 가장 넓게 보는 회사 중 하나입니다. 최근엔 개발자가 이 관문 위에서 직접 앱과 AI 기능을 돌릴 수 있는 Workers 플랫폼까지 키우며 "엣지 클라우드" 회사로 넓혀가고 있습니다. 숫자는 매튜 프린스 CEO 말대로 "역대 최고의 분기"였습니다. 매출 $782.5M으로 +39%, 약 4년 만에 가장 빠른 성장인…
❤4
Showing the 12 most recent of 251 posts we hold for @silvermansachs_routine. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
@FastStockNews · 128,8674 posts뺀지뤼의 SKLab.(스크랩)
@bbanjil · 10,6773 posts캬오의 공부방
@Kyaooo · 17,4682 posts프리라이프
@free_life59 · 12,7102 posts피카츄 아저씨⚡️
@pikachu_aje · 24,9402 posts급등일보 미국주식🇺🇸 속보·매크로·리서치
@FastStockNewsUSA · 40,9861 postYeouido Lab_여의도 톺아보기
@Yeouido_Lab · 43,4611 post가치투자클럽
@corevalue · 67,1281 post뉴스/재료 발굴하기
@crabintheshell · 8711 postWaymaker
@dots999 · 2021 post가투방(DCTG) 저장소
@gatubang · 14,5421 post습관이 부자를 만든다. 🧘
@habit4117 · 28,2901 post인상주의 투자 인생 업그레이드노트
@insangnism · 11,2291 post잠실개미&10X’s N.E.R.D.S
@jake8lee · 17,2331 post묻따방 🐕
@mootda · 19,7701 post잼민123🦅
@mujammin123 · 25,7901 post레드버드 기업분석
@redbirdstock · 10,7441 postSally's law
@sallyslaw79 · 1421 post
Republishes
Channels on the register whose posts this channel has forwarded.
Republishes 57 registered channels. The 48 listed are the ones this channel has forwarded most; the rest are counted here but not each listed.
@bornlupin · 49,2069 postsAWAKE - 실시간 주식 공시 정리채널
@darthacking · 65,5899 posts프리라이프
@free_life59 · 12,7108 posts하나 중국/신흥국 전략 김경환
@HANAchina · 35,2724 posts꿈을 담는 단지
@dykim0210 · 3,8594 postsFOMO 4가투
@fourgachi · 9,3744 postsKOREA DAILY INSIGHT - KIS GREG KIM
@KISGregKim · 6,4843 postsWalter Bloomberg
@WalterBloomberg · 33,7553 posts딘스티커 Dean's Ticker
@d_ticker · 16,1303 posts미미의 신고가
@mimi_ATH · 6,7603 postsCTRINE
@teamctrine · 8,2193 posts64비트사령부⚡️ 🇰🇷🇺🇸
@BTCpentagon · 6,5602 posts한투증권 미국/중국전략 정정영
@G2strategy · 16,6802 posts퀄리티기업연구소
@QualityInvestingLaboratory · 6,7962 posts[삼성 이영진] 글로벌 AI/SW
@Samsung_Global_AI_SW · 12,7352 posts카이에 de market
@cahier_de_market · 23,0722 posts서화백의 그림놀이 🚀
@easobi · 23,4052 postsFund Easy 잡담모드
@fundeasy_choi · 10,8202 posts가투방(DCTG) 저장소
@gatubang · 14,5422 postshallasanorganicfire
@hallasanorganicfire · 182 posts한국경제
@hankyung_fin · 12,6852 posts한화투자증권 리서치센터 투자전략팀
@hanwhastrategy · 11,6152 posts미국 주식 인사이더 🇺🇸 (US Stocks Insider)
@insidertracking · 73,8842 posts[메리츠 Tech 김선우, 양승수, 김동관]
@merITz_tech · 24,6992 posts몰?루
@molru · 3,8182 posts머니보틀
@moneybottle · 15,7432 posts묻따방 🐕
@mootda · 19,7702 posts용산의 현인
@petrojimmy · 7,0122 postsBRILLER
@BRILLER_Research · 17,9811 post유안타 손현정 [유틸리티(전력기기)/음식료]
@HJS_YSK · 4,6161 post호구구구
@HoguGugu · 1,5311 post캬오의 공부방
@Kyaooo · 17,4681 post🚨별이네💎멍멍놀이터🔥
@byulEnae · 2,5661 post[미래에셋 배터리/디스플레이 김철중] 디배저
@cjdbj · 16,4251 post우주방산AI로봇 아카이브
@defence_24 · 10,1331 post다영의 뉴스공장
@dynewscollecters · 3,1041 postGromit 공부방
@grmtstudy · 3,5051 post습관이 부자를 만든다. 🧘
@habit4117 · 28,2901 post해기사투자자의 무한도전
@haetwo · 4,8261 postPluto Research
@hedgehara · 19,5861 post기술적 분석으로 보는 주식시장 (by 소라게아빠)
@hermitcrab41_1 · 21,4721 post알음다트
@infodart · 6,7651 post플렉서블 리서치
@ivy77788 · 5,9091 post점프
@jump0000 · 3,3701 postKK Kontemporaries
@kkkontemp · 21,1361 post한국투자증권 패시브영업부
@koreainvestment_passive · 4,3001 postMarket News Feed
@marketfeed · 39,6861 postDS 양형모의 투자전략
@milperc · 7,5001 post
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 3 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 7 November 2026 — this entry's latest reading, not the date you are reading this.
“실버만삭스의 루틴” (@silvermansachs_routine), 7,884 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/silvermansachs_routine.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.