Channel
@fenchacaij
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Telegram's recommendations · Cite this entry
250,470subscribers
-25,170 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1001515857362 |
|---|---|
| Type | Channel |
| Username | @fenchacaij |
| Description | 接快讯，商务合作联系 @Cindy88888 |
| Created | Between 1 August 2021 and 28 February 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 26 September 2026 |
| Measurements held | 33 |
| Confirmed unchanged | 1 time, most recently 26 September 2026 |
| On Telegram | t.me/fenchacaij |
Crypto & trading — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 8 August 2026 and assigned it the closest of 31 fixed categories, at 90% 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 why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 26 Sept 2026, 15:58 | 250,470 | -4,626 |
| 17 Sept 2026, 10:41 | 255,096 | -868 |
| 15 Sept 2026, 13:37 | 255,964 | -344 |
| 13 Sept 2026, 20:01 | 256,308 | -981 |
| 12 Sept 2026, 04:39 | 257,289 | -1,424 |
| 9 Sept 2026, 19:18 | 258,713 | -1,824 |
| 6 Sept 2026, 08:58 | 260,537 | -1,419 |
| 3 Sept 2026, 23:17 | 261,956 | -645 |
| 2 Sept 2026, 11:57 | 262,601 | -735 |
| 1 Sept 2026, 08:36 | 263,336 | -734 |
| 31 Aug 2026, 08:43 | 264,070 | -865 |
| 30 Aug 2026, 07:55 | 264,935 | -429 |
| 29 Aug 2026, 10:48 | 265,364 | -402 |
| 28 Aug 2026, 12:32 | 265,766 | -555 |
| 27 Aug 2026, 15:25 | 266,321 | -115 |
| 26 Aug 2026, 13:28 | 266,436 | -515 |
| 25 Aug 2026, 12:54 | 266,951 | -753 |
| 24 Aug 2026, 10:55 | 267,704 | -1,018 |
| 22 Aug 2026, 16:06 | 268,722 | -623 |
| 21 Aug 2026, 09:36 | 269,345 | first reading |
360 posts held, back to 4 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 106 pages of Telegram’s post history, 20 posts per page.
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 126 of 200 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 26 September 2026 |
|---|---|
| Posts held | 360 (4 August 2026 – 26 September 2026) |
| Views total | 149,659 |
| Reactions total | 190 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 26 Sept 2026, 19:59 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.
Lifetime counters from Telegram’s own channel header, read 26 September 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.
313 reactions across 202 posts, in 8 distinct kinds. The most used accounts for 70.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 222 | 70.9% | |
| ❤ | 84 | 26.8% | |
| 👻 | 2 | 0.639% | |
| 🐳 | 1 | 0.319% | |
| 👏 | 1 | 0.319% | |
| 😁 | 1 | 0.319% | |
| 😢 | 1 | 0.319% | |
| 🥰 | 1 | 0.319% |
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 215 of the 360 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 313 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 360 most recent posts we hold, published 4 August 2026 to 26 September 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.
【CZ 否认拥有影响加密市场价格的能力，称发帖与利好消息时间接近纯属巧合】 Binance 创始人 CZ 在参加《When Shift Happens》播客节目中表示，其始终看涨比特币，但否认拥有影响整个加密市场价格的能力。此前，CZ 曾于 8 月 19 日在比特币处于约 6 万美元区间时发文表示，如果希望两年后的自己感谢今天的决定，就需要立即采取行动。该帖被市场解读为对买入比特币的暗示，此后数日加密市场上涨约 20%。CZ 回应称，自己没有推动整个市场价格的能力。他表示，自己一直看涨加密行业，几乎所有帖子都带有看涨倾向，每天可能发帖 5 至 10 次，因此其中一条恰好出现在利好消息之前并不意外。CZ 表示，该帖发布约 5 小时后，特朗普发表了对加密行业较为积极的讲话，其中提到推动 Hyperliquid 进入美国市场，但自己事前并不知道讲话内容。他强调，如果掌握任何可能影响市场的信息，反而不会围绕相关内容发帖。因此，该帖与随…
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【哈萨克斯坦拟允许加密矿企利用油田伴生气发电】 哈萨克斯坦正推动加密矿企利用油田剩余伴生气自建电站，以减少天然气燃烧、缓解国家电网压力并提高石油产量。数据显示，该国 2024 年燃烧约 3 亿至 3.4 亿立方米伴生气，若用于发电，预计可产生 1.2 至 1.3 太瓦时电力。目前，哈萨克斯坦能源部等部门正制定相关法律框架。 🎾🎾🎾🎾🎾🎾🎾🎾 快讯/广告 联系 @Cindy88888
【Vitalik：移动端离线本地知识应用仍难处理复杂问题】 Vitalik 在 X 平台发文表示，正在测试开发者构建的移动端离线本地知识应用。与其两个月前尝试构建的应用相比，这些应用已有改进，但在复杂问题上的速度和效果仍不及可在笔记本电脑上运行的模型；其在专业旅行相关查询上的表现较弱，例如查询所在城市的最佳纯素餐厅。他希望此类应用继续改进，使用户无需访问互联网即可查询所需的各类事实信息。
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【韩国央行行长遭冒充：社媒虚假账号诱导用户加入投资群】 9 月 26 日，社交媒体上出现冒充韩国银行行长李昌镛（이창용）的虚假账号，以“帮助了解经济走势”、“学习小组”等名义诱导用户加入投资群，该账号于 9月 22 日至 23 日期间发布多条包含李昌镛个人履历的帖子，虽然未直接公开进行投资推荐，但会引导添加好友或私信的用户加入所谓“学习群”，进而将其引流至群聊。 据悉，韩国央行已向社交媒体运营方提出删除请求，该账号数小时内被封禁，但已有大量成员加入的相关“投资带单群”且仍在 Telegram 上运营。此外，近期除李昌镛外，韩国金融业知名人士、大企业负责人及艺人等也频繁遭冒充，用于投放投资诈骗广告，包括冒充 SK 集团会长崔泰源、LG 集团会长具光谟以及三星电子会长李在镕等，部分诈骗广告还利用 AI 生成深度伪造视频，模仿声音、语气和表情。
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【Meta新墨西哥州隐私案败诉，或面临最高2190亿美元罚款】 9 月 26 日，美国新墨西哥州陪审团裁定，Meta 在言论及数据隐私政策上存在欺骗公众行为，违反州法律，Meta 由此成为剑桥分析数据丑闻中迄今面临最高法律代价的一次。州总检察长 Raúl Torrez 称该裁决是追究科技巨头责任的「里程碑式时刻」，将向法院申请对 Meta 处以最高 2190 亿美元民事罚款。陪审团认定，在裁决书列出的 34 项陈述中，Meta 对其中大多数负有责任，每项违规次数在 130 万至 210 万次之间，对应新墨西哥州 Facebook 用户数量或 2020 年该州总人口数；每次违规最高可处以 5000 美元罚款，最终金额将由圣塔菲第一司法巡回法院法官 Francis Mathew 裁定。 据 BIT(bit.com) 行情数据，消息公布后，Meta 周五收跌 3.33%。 该案源于 2018 年曝光的剑桥分析丑闻：一名数据科学家开发…
【美国 CFTC 指控 Cash FX Group 涉 9.5 亿美元庞氏骗局，参与者至少损失 4.06 亿美元】 美国商品期货交易委员会（CFTC）对 Cash FX Group S.A.及其 CEO Huascar Jose Lopez Castillo、The Conversion Pros Inc.及其 CEO Ronald Pope、Justin Halladay 提起诉讼，指控上述被告运营多层级营销庞氏骗局，向包括美国在内的公众募集超过 9.5 亿美元，声称用于零售外汇合约交易。CFTC 称，被告虚假宣称资金由专业交易员、专有算法和 AI 进行交易，并承诺最高每周 15%的回报；实际上 Cash FX 仅进行少量外汇交易，几乎所有参与者资金均被挪用，并通过新参与者投入的资金向其他参与者支付虚构的交易收益，同时向被告支付数百万美元。CFTC 称，Cash FX 还向参与者提供虚假账户报表以维持高额交易回报的假象，参与…
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【SEC 委员：应重新审视 KYC 与 AML 监管】 美国证券交易委员会（SEC）委员 Hester Peirce 呼吁重新审视了解你的客户（KYC）与反洗钱（AML）监管方式。她认为，“数据越多越好”的做法会形成过大的信息堆，反而更难发现真正需要关注的问题。 Peirce 表示，零知识证明可用于核验合规情况，同时不必暴露个人数据。相关观点由 CoinDesk 在其 X 账号发布的洞察内容中引述。
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【阿联酋与新加坡正成为稳定币跨境支付试验场】 9月25日，阿联酋与新加坡正成为稳定币跨境支付与企业财资应用的重要试验场。Visa 亚太区数字货币负责人表示，稳定币更可能与银行体系并行发展，而非取代银行；Binance 亚太区负责人 SB Seker 称，亚太数字资产市场同比增长约 69%–70%，商户对稳定币支付的接受度正在提高。目前新加坡已有 XSGD、代币化存款等应用，阿联酋也在推进 DDSC 和数字迪拉姆跨境支付。不过业内人士指出，合规、外汇兑换、流动性和本地出金仍是中东—亚洲稳定币支付走廊进一步发展的主要障碍。 🎾🎾🎾🎾🎾🎾🎾🎾 快讯/广告 联系 @Cindy88888
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【IMF：2026 年全球 AI 投资规模或突破 2 万亿美元，债务融资依赖同步扩大】 国际货币基金组织（IMF）发布最新年度报告，引述外部估计指出，今年由私人部门主导的全球人工智能（AI）投资规模可能突破 2 万亿美元，成为近年来推动经济增长的最强劲动力之一。报告指出，与 AI 相关的技术投资估计带动美国 2025 年 GDP 增速提高 0.5 个百分点，而美国近年生产率增速加快，一定程度上也反映出 AI 应用的早期正面影响。不过，报告同时警告称，AI 投资热潮背后潜藏隐忧。随着相关基建及项目规模持续扩大，部分高成本投资越来越依赖债务融资。一旦未来回报未能达到预期，恐会引发资产估值显著调整、财富缩水及企业裁员等连锁效应。
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👍2
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👍2
【美联储拟要求银行支付型稳定币每 1 美元代币配备至少 1 美元储备】 美国联邦储备委员会（美联储）拟为银行发行的支付型稳定币制定规则，要求每 1 美元代币至少由 1 美元获准储备资产支持，并通常在两个工作日内完成客户赎回。发行方若持续低于最低资本要求，可能被要求清算储备资产并赎回全部代币。储备资产可包括美元、美联储银行余额、部分银行存款、剩余期限不超过 93 天的美国国债、合格回购协议及合资格投资基金，部分资产的代币化形式也可能纳入。若储备不足，发行方须通知美联储并恢复足额支持，否则需清算储备并赎回挂钩美元的代币。美联储拟要求发行方针对运营及部分信用风险计提标准化资本，已发行稳定币规模前 200 亿美元部分的资本收费为 2%，超过 500 亿美元部分为 1%。另一项提案允许受保存款州会员银行申请设立发行支付型稳定币的子公司。《GENIUS 法案》规定，申请实质完整后，美联储须在 120 天内作出决定。美联储理事 Michae…
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Showing the 12 most recent of 360 posts we hold for @fenchacaij. 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.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 17 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 23 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 26 September 2026 — this entry's latest reading, not the date you are reading this.
“分叉财经(crypto news)” (@fenchacaij), 250,470 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/fenchacaij.
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.