今日币价7月11日:比特币为CPI周做准备,美股繁荣,MINA领跑山寨币

Tap Chi BitcoinPubblicato 2023-07-11Pubblicato ultima volta 2023-07-11

Introduzione

比特币(BTC)价格在较短的时间范围内上涨,但在较高的时间范围内没有可行的价格走势。

比特币(BTC)价格在较短的时间范围内上涨,但在较高的时间范围内没有可行的价格走势。

来源:TradingView

道琼斯工业平均指数周一(7 月 10 日)上涨,华尔街试图从上周的跌势中恢复过来。这一逆转发生之际,投资者正准备迎接本周末一系列通胀数据以及第二季度财报季的开始。

周一收盘,道琼斯工业平均指数上涨 0.62%,至 33,944 点。标准普尔 500 指数上涨 0 至 24%,至 4,409 点。与此同时,纳斯达克综合指数小幅上涨 0.18%,至 13,685 点。主要股指均结束了链的下跌。

美国CPI消费者价格指数报告将于7月12日发布,随后PPI生产者价格指数报告(衡量批发价格压力的指标)将于7月13日发布。

上周,标普500指数下跌1.16%,纳斯达克综合指数和道琼斯指数分别下跌0.92%和1.96%。尽管6月份就业数据增幅低于预期,但工资涨幅略高于预期,引发了人们对美联储(Fed)进一步加息可能性的担忧。

Fundstrat 的 Tom Lee 认为通胀报告可能低于预期,这可能导致通胀再次大幅攀升。

周一金价基本持平,投资者等待可能影响美联储政策立场的美国通胀数据。尾盘,现货黄金合约基本持平于1,925美元/盎司。黄金期货下跌 0.1%,至每盎司 1,931 美元。

同一天,由于美国可能进一步加息,油价下跌,但主要石油出口国沙特阿拉伯和俄罗斯削减原油供应的举措限制了油价跌幅。收盘时,布伦特原油合约下跌1%,至每桶77.69美元,盘初触及2个多月来最高水平。

在 1 小时图表上,BTC/USDT 显示出可操作的价格变动,这与市场停滞的一日时间框架不同。在将 50 天、100 天和 200 天指数移动平均线 (EMA) 翻转至 30,522 美元、30,296 美元和 30,344 美元的支撑位后,这位加密货币之王仍然看涨。

然而,专家预测,在 CPI 引起一定波动之前,这种拖累将会持续存在。根据普遍观点,该报告可能为 3.1%,这一结果可能会引发顶级加密货币的反弹。

如果发生这种情况,比特币价格可能会突破该区间并突破 30,500 美元,创下新高。

斐波那契回撤指标显示,比特币价格可能会升至斐波那契 50% 水平,即 30,727 美元。这一前景受到相对强弱指数 (RSI) 和 AO 的支撑,上涨显示出看涨势头。

比特币图表。来源:TradingView

然而,随着时间的推移,消费者物价指数等宏观经济的影响力已经减弱,这意味着比特币要么继续其低迷的价格走势,要么屈服于投资者的贪婪并一路下跌。

至于ETH,由于各个生态系统缺乏适当的催化剂,最大的山寨币价格正在与BTC同步波动,随着大盘为CPI做准备,其地位逐渐上升。

以太坊图表。来源:TradingView

而山寨币的另一面,前 100 名中约有一半处于上涨状态,其中 MINA 处于领先地位,在 24 小时内涨幅超过 15%。紧随其后的是 MATIC 接近 9%、KAVA +7%、FTM +6.3%、BNB +5.3% 以及其他看涨山寨币的回报率为 0.5-4%。

资料来源:Coinecko

“今日币价”栏目将于每日9:00更新市场动态,欢迎读者关注。

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AI PC Battle: Bet on the Toll Booth, Not the Camp

**Title:** The AI PC Battle: Don't Bet on Sides, Bet on the Tollbooth **Summary:** The AI PC competition is moving beyond simple "x86 vs. Arm" narratives. The core investment thesis should focus on identifying which players can sustain margins, cash flow, and pricing power throughout the upgrade cycle, rather than backing a particular architecture. The opportunity is analyzed in three layers: 1. **The Advanced Foundry Tollbooth:** TSMC is positioned to collect "tolls" regardless of which chip designer wins, due to its dominant ~70% share in advanced semiconductor manufacturing, which is essential for high-end AI PC chips. 2. **Compute & Platform Spillover:** AMD represents an offensive in the x86 CPU+GPU space, while NVIDIA leverages its GPU and CUDA software stack dominance. Both benefit from the demand for increased local AI compute. 3. **Architecture Diffusion & Turnaround Plays:** ARM and Intel offer potential for significant upside (elasticity), but investments here require stricter discipline due to higher execution risks and competitive challenges. The industry is transitioning from concept to shipment validation. While short-term forecasts for AI PC adoption have been revised down slightly due to tariffs and procurement delays, the long-term trend towards AI becoming a standard PC feature remains intact. The key driver for upgrade cycles will be whether compelling enterprise applications (e.g., privacy-sensitive computing, low-latency inference) emerge beyond consumer-focused features like meeting summarization. Investment strategy should prioritize companies with platform-level advantages and recurring revenue streams. TSMC offers high certainty as the foundational tollbooth. AMD presents a strong offensive play within the established ecosystem. ARM and Intel are higher-risk, higher-potential-reward turnaround bets. The report cautions against chasing short-term hype and emphasizes a disciplined, long-term approach focused on buying ecosystem strength and cash-flow certainty after market enthusiasm subsides. **Key Risks:** Underwhelming AI PC applications slowing upgrade cycles; slow improvement in Windows on Arm compatibility; macro/tariff impacts on PC demand; potential advanced node supply-demand mismatches affecting TSMC; high overall AI sector valuations making stocks vulnerable to a risk-off shift in markets.

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Marvell Technology's stock price surged from under $10 in 2016 to a record $290 in June 2026, fueled not by making GPUs, but by dominating AI infrastructure connectivity. This analysis argues the market misvalues MRVL as merely a smaller Broadcom in custom AI chips, overlooking its true, unique position. Marvell's core strength lies in enabling high-speed data flow for AI clusters through three interconnected businesses. First, it holds a commanding ~70% market share in high-speed optical DSPs (essential for data center light modules), a deep-moat business with accelerating growth. Second, its custom AI chip design business serves hyperscalers like AWS, Microsoft, and Google, with a significant revenue pipeline despite lower margins. Third, stable cash flows come from Ethernet switch chips and enterprise storage controllers. Together, they form a full-stack "AI data movement" platform. CEO Matt Murphy's transformative leadership since 2016, involving strategic divestments, key acquisitions (like Inphi for optical DSPs), and securing long-term agreements with major cloud providers, repositioned the company. A pivotal $2 billion strategic investment from NVIDIA in 2026 underscored Marvell's critical role in the AI ecosystem, particularly through collaborations like NVLink Fusion. While Marvell faces risks—including client concentration (losing the Amazon Trainium3 design), lower-margin business mix, competitive threats, insider selling, and complex supply chains—its fundamentals remain strong. The optical interconnect moat is widening with the acquisition of Celestial AI (photonics fabric), and financial metrics show accelerating revenue growth and operating leverage. With a PEG ratio suggesting undervaluation relative to its growth, the thesis is that the market undervalues Marvell's monopolistic position in AI "plumbing" while overemphasizing its competitive custom chip segment. The story transcends investing, symbolizing how in any complex system—from the internet to AI—the value of "connection" ultimately surpasses that of individual "nodes."

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AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

A discussion on Zhihu about "AI relay stations" shifted the niche developer topic of "cheap tokens" into broader user awareness. Users moved beyond simply questioning the legitimacy of these services to focus on practical concerns: Where do cheap tokens truly come from? Is the model being accessed the real one? Can relay stations see prompts, code, and API keys? For occasional users, are the risks worth it? The core debate centered less on price and more on trust. A primary worry is model authenticity—the risk of "model swapping," where users paying for a premium model might be routed to a cheaper one, creating an information asymmetry. Others argued that cost comparisons matter; while cheaper than official pay-as-you-go APIs, relay stations may not be the lowest-cost option versus subscriptions, domestic models, or free tiers, making user needs assessment crucial. Speculation about token sources ranged from legitimate bulk discounts to gray-area methods like account sharing or exploiting regional pricing. This opacity makes risk assessment difficult for users. Data security emerged as a critical concern, especially for enterprise use. When processing sensitive information like code, contracts, or client data, the inability to verify a relay station's data handling, retention, or access policies poses significant compliance and confidentiality risks. The evolving consensus suggests relay stations can be used cautiously for low-sensitivity, disposable tasks (e.g., summarizing public info, simple translation). However, they should not be the default for sensitive, professional, or production workflows involving proprietary data, Agents, or automated systems. Recommendations include avoiding large prepayments, not relying on a single service, using test prompts to monitor quality, anonymizing data where possible, and keeping official channels as backups. Ultimately, the discussion framed tokens not just as a billing unit but as a measure of real cost encompassing price, model integrity, data security, and service stability. The popularity of relay stations highlights user demand for affordable access, but the debate underscores a key trade-off: the savings from cheap tokens may come at the price of trust, transparency, and control over one's data and AI experience.

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In-Depth Research Report on TradFi: The Convergence Wave of Crypto and Traditional Finance

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