由于 3 万美元的比特币价格成为关键战线,美联储所剩弹药所剩无几

Cointelegraph中文Publicado a 2023-05-05Actualizado a 2023-05-05

Resumen

比特币价格在 5 月 2 日成功捍卫了 28,000 美元的支撑位,但尚未证明从 4 月 30 日起收回 29,200 美元水平所需的实力。

比特币价格在 5 月 2 日成功捍卫了 28,000 美元的支撑位,但尚未证明从 4 月 30 日起收回 29,200 美元水平所需的实力。

一些分析师将近期的下跌趋势归因于美联储将于 5 月 3 日加息的预期,但实际上,市场定价 92% 的可能性温和加息 25 个基点至自以来的最高水平2007 年 9 月。

正如市场情报平台Decentrader指出的那样,美联储主席杰罗姆鲍威尔的言论更有可能带来意外因素,要么指向进一步措施放缓经济,要么暗示最终利率接近5%的可能性更高。鲍威尔定于东部时间下午 2 点 30 分举行新闻发布会。

从就业角度看,央行有理由认为市场持续过热。美国政府报告称,3 月份每个失业工人有 1.6 个职位空缺。此外,根据 5 月 3 日发布的《ADP 全国就业报告》,4 月份民间就业岗位增加 29.6 万个,远高于市场预期的 14.8 万个。

然而,提高利率对家庭和小企业尤其有负面影响。融资和抵押贷款变得更加昂贵,而固定收益投资变得更具吸引力。这种抑制通货膨胀的不良影响可能会进一步动摇金融体系的核心,正如最近第一共和国银行倒闭所表明的那样。

价格突破 30,000 美元可能是投资者的看法从将比特币视为风险资产转变为直接受益于较弱的传统银行系统的稀缺数字资产的明确迹象。

但要衡量比特币在 28,000 美元上方的弹性是否可持续,投资者必须分析买家是否使用了过度杠杆,以及专业交易员是否使用BTC衍生品定价市场低迷的可能性更高。

比特币季度期货在鲸鱼和套利平台中很受欢迎。然而,这些固定月份合约的交易价格通常比现货市场略有溢价,表明卖家要求更多资金以延迟结算。

因此,健康市场中的期货合约应该以 5% 到 10% 的年化溢价交易——这种情况被称为期货溢价,这并不是加密市场独有的。

数据表明比特币交易员在过去几周格外谨慎。即使BTC价格在 4 月 26 日徘徊在 30,000 美元附近,也没有迹象表明对杠杆多头有需求。

此外,比特币期货溢价自 4 月 23 日以来一直停滞在 2% 附近,表明买家不愿使用杠杆,这对市场来说是健康的。通过避免期货合约敞口,它大大降低了比特币价格下跌期间大量清算的风险。

比特币期权市场也有助于解释最近的调整是否让投资者变得更加乐观。当套利柜台和做市商为上行或下行保护收取过高费用时,25% 的 delta 偏斜是一个明显的迹象。

简而言之,如果交易员预期比特币价格下跌,偏斜指标将升至 7% 以上,而兴奋阶段往往会有 7% 的负偏斜。

期权 delta 的 25% 偏斜表明过去四个星期看涨期权和看跌期权之间的需求平衡。鉴于比特币价格在 4 月 25 日至 4 月 30 日期间上涨了 10%,上次测试 30,000 美元阻力位时,这应该令人感到意外。

因此,比特币期权和期货市场表明,专业交易员不会在短期内将BTC价格突破 30,000 美元。另一方面,这些鲸鱼正在定价类似的意外正面和负面走势的可能性。

最终,鉴于美联储在不引发经济衰退的情况下加息显然有一个限度,无论 5 月 3 日的决定如何,比特币的价格都应该受到积极影响。

美联储主席鲍威尔最终将迫使美国财政部向经济注入更多资金以遏制银行业危机,这将有利于比特币等稀缺资产。

Lecturas Relacionadas

A 'Difficult Midsummer' for Stock Market Bulls: $100 Oil, AI Backlash, and Tariff Resumption

Global equities face a triple threat this summer: surging oil prices, a burgeoning AI capex crisis, and resurgent tariffs, pressuring the core pillars of the current bull market. Brent crude topped $100/barrel amid Middle East and Red Sea supply disruptions, while proposed U.S. tariffs fueled inflation fears, pushing the 10-year Treasury yield to 4.66%. Simultaneously, the tech rally faltered as Alphabet's steep AI capex hike sparked concerns over returns, dragging down mega-caps and contributing to a weekly S&P 500 decline. The "Goldilocks" narrative of resilient profits, controlled inflation, and endless AI expansion is cracking. Rising oil prices threaten to reignite persistent inflation, forcing a repricing of interest rate risks. In tech, massive debt-fueled AI investments are facing investor scrutiny as higher rates raise the required return threshold. Fears are growing that the AI spending boom may be unsustainable, especially with moves toward cheaper, open-source models. Next week brings a crucial test with key central bank meetings and earnings from major tech firms like Microsoft and Apple. Technical indicators are deteriorating, with selling pressure mounting and assets like gold and the dollar gaining on避险 sentiment. Analysts warn that elevated valuations and compressed risk premiums leave markets vulnerable, with significant potential downside if the current headwinds persist.

链捕手Hace 3 min(s)

A 'Difficult Midsummer' for Stock Market Bulls: $100 Oil, AI Backlash, and Tariff Resumption

链捕手Hace 3 min(s)

Three 'Reflexivity' Shadows Hang Over the Market

Global markets are currently enveloped by three mutually reinforcing "reflexive" loops: oil price politics, outsized capital expenditure by hyperscale cloud providers, and AI debt risks. According to Goldman Sachs, the combined negative feedback from these factors places the market in a fragile and precarious state. The first loop involves the two-way feedback between surging oil prices and rising interest rates. Brent crude's brief breach of $100 per barrel tests expectations of a U.S. policy response to curb prices and inflation. However, the delay in such intervention forces markets to increasingly price in the risks themselves. Higher energy costs are already impacting corporate earnings, as seen with an airline's profit warning, and threaten to fuel broader inflation. The second loop concerns the massive, escalating capital expenditure (capex) by major tech firms like Google, which recently raised its 2026 capex forecast significantly. The market's tolerance for viewing such spending as a cost-free growth signal is waning, shifting focus to investment returns. This competitive capex spiral pressures the entire cloud and semiconductor sector. Furthermore, the competitive gap in AI between leading closed-source and Chinese open-source models is narrowing rapidly, threatening the economic rationale behind massive investments. The third reflexive danger lies in the financing structures supporting this expansion. Bond prices for entities funding AI infrastructure, such as a Meta financing vehicle, have fallen sharply from issue price. While hyperscale balance sheets remain strong, soaring capex is eroding free cash flow conversion. There is a growing risk that today's capacity build-out leads to future oversupply and significant depreciation charges. Key near-term tests for these dynamics include Microsoft's upcoming earnings, which will scrutinize its balance of growth, spending, and cash flow, and the IPO of Chinese memory chipmaker CXMT, a major new competitor. The current environment is characterized by reflexivity, where each variable is both a cause and an effect, creating a self-reinforcing cycle of uncertainty.

链捕手Hace 9 min(s)

Three 'Reflexivity' Shadows Hang Over the Market

链捕手Hace 9 min(s)

Claude Doesn't Submit Code Directly After Writing It: Runs 4 Skills for Self-Check, Fixes Issues, Then Comes Back to You

Claude No Longer Submits Code Directly: 4 Self-Check Skills to Run Before Coming Back to You AI already writes code, but the burden of reviewing it still falls on you. To address this, Anthropic has built a "verification loop" into Claude Code. After writing code, Claude now runs four self-check skills before delivering the work: * `/code-review`: Finds potential bugs and provides review feedback. * `/simplify`: Cleans up the diff, removing redundant or over-complex implementations to reduce future maintenance costs. * `/verify`: Performs end-to-end validation, actually running the application to confirm the feature works, not just appears to. * `/design`: Used only for UI changes; cross-references the implementation against the project's DESIGN.md file. This loop extends the AI agent's workflow from "gather context → execute" to "gather context → execute → auto-verify → fix → re-verify." It tackles the new bottleneck in AI-assisted development: the speed of verifying code now outpaces human review. These skills are built on Claude Code's existing verification foundation (like running apps and using linters). Teams can create their own custom verification skills by documenting their repetitive manual checks in plain language as Markdown files. Verification can be triggered at four levels: manually (Standalone), embedded in a task, chained with other skills, or automatically on every PR (On every PR). The shift signifies that competition in AI programming is moving from code generation to robust verification and self-correction. Well-built verification loops allow AI agents to run longer and more autonomously with less human supervision. Skills, which encapsulate team knowledge and workflows, are becoming a cross-vendor standard, meaning a team's efficiency gap will depend less on the AI model and more on their investment in these automated workflows and verification mechanisms.

marsbitHace 15 min(s)

Claude Doesn't Submit Code Directly After Writing It: Runs 4 Skills for Self-Check, Fixes Issues, Then Comes Back to You

marsbitHace 15 min(s)

How Has Beijing Become a 'Global High Ground'?

How did Beijing become a "global highland"? In the first half of the year, Beijing's GDP grew by 5.4%, with growth in Q1 reaching 5.9%. Key drivers were not consumption or real estate, but hardcore sectors like chips, robotics, computing power, and large AI models. Investment concentrated sharply in tech, with high-tech manufacturing investment up 36% and technology services investment up 87.2%. Nearly 90% of venture capital flowed into hard tech. Output figures were strong: integrated circuit production rose 17.8%, industrial robots 75.5%, and service robots surged 230%. Companies like Qianxun AI and Galaxy General Robotics are deploying robots in real-world logistics and manufacturing, moving beyond demonstrations. The services sector is also being reshaped by tech, with software, IT, and finance growing faster than the sector average. Beijing leads in AI and embodied intelligence. Galaxy General Robotics secured significant funding, including from state-backed funds. The city released foundational AI models and its independent AI ecosystem, FlagOS, is expanding. Companies like Jiuzhang Yunjie provide neutral computing power and base models, supporting the broader ecosystem. Infrastructure is expanding, with Beijing aiming to add over 70,000 Petaflops of intelligent computing power annually. A "Beijing R&D, surrounding training" model is emerging, with computing resources being allocated to regions like Hebei and Tianjin. Efforts to deepen integration within the Beijing-Tianjin-Hebei region include industrial relocation and improved transport links. To bridge the gap between lab and market, Beijing is establishing pilot-scale production platforms. The underlying trend is the convergence of computing power, capital, talent, and real-world applications. While challenges remain in commercialization and regional integration, Beijing's focus on strengthening these interconnected elements is building the foundation for its claim as a global innovation hub.

marsbitHace 16 min(s)

How Has Beijing Become a 'Global High Ground'?

marsbitHace 16 min(s)

How Did the Target Price of 116 Yuan and a Market Cap of 7.9 Trillion for CXMT Come About?

Nomura's inaugural coverage report on ChangXin Memory Technologies (CXMT) gives a 'Buy' rating with a highly aggressive target price of 116 RMB, implying a 1239.5% upside from the IPO price of 8.66 RMB. The valuation is derived by applying a 20x P/E multiple to projected 2028 EPS of 5.79 RMB. The 20x multiple combines Micron's historical valuation with a premium for Chinese A-shares. The report's financial forecasts are exceptionally bullish. It projects revenue to soar from 62B RMB in 2025 to 773B RMB in 2028, with net profit surging to 393B RMB. A key, and arguably unsustainable, assumption is gross margin expanding to over 90% by 2028, driven almost entirely by price increases with minimal cost growth. This optimism is rooted in a forecast that global DRAM demand will grow over 7x from 2026 to 2030 (CAGR >60%), fueled by agentic AI. Nomura argues that supply growth (30-40% CAGR) will lag far behind demand, creating a persistent shortage. Bottlenecks in cleanroom space, equipment, materials, and skilled engineers will constrain rapid industry expansion. For CXMT, Nomura expects capacity to reach 550k wafers/month by 2028. The company's bit output is forecast to grow 40-45% annually, allowing its global market share to rise from ~10% to ~18%. While CXMT's technology lags leaders by about five years, limiting its wafer ASP, it benefits from supportive domestic procurement policies. Key risks include the cyclicality of memory pricing, with the 90% margin assumption representing a peak-cycle scenario rather than a sustainable norm.

链捕手Hace 24 min(s)

How Did the Target Price of 116 Yuan and a Market Cap of 7.9 Trillion for CXMT Come About?

链捕手Hace 24 min(s)

Trading

Spot
活动图片