ETH 的温和复苏激发投资者的乐观情绪:牛市反弹即将到来吗?

币界网Published on 2024-08-21Last updated on 2024-08-21

币界网报道:
  • ETH 已经显示出复苏的迹象,尽管由于市场情绪谨慎,以太坊尚未达到之前的高位。

  • ETH 交易所流出量的增加表明投资者信心增强,暗示着潜在的看涨趋势。

  • ETH 可能正处于修正阶段的最后阶段,并引用了令人鼓舞的链上指标。

了解 ETH 的近期复苏与市场指标如何预示其调整阶段的结束,并可能为新一轮反弹铺平道路。

ETH 的温和复苏:分析关键指标

最近几天,市值第二大的加密货币以太坊 (ETH) 的价格水平出现了温和回升。在撰写本文时,ETH 的交易价格为 2,661 美元,在过去 24 小时内上涨了 1.6%。此次价格上涨之前,以太坊曾大幅下跌,上周跌至 2,545 美元的低点。尽管有所回升,但以太坊的当前价格仍远远落后于 3 月份 4,070 美元的峰值,比三年前创下的 4,878 美元的历史高点下跌了约 45%。

探索市场情绪和链上指标

随着以太坊试图恢复势头,人们开始质疑它是否会持续这种复苏,或者这些走势是否只是短暂的调整。以太坊可能即将结束其调整阶段,有两个关键的链上指标。主要关注两个数据集:Taker 买卖比率和未平仓合约 (OI)。衡量交易所买卖比率的 Taker 买卖比率已变为正值,表明买家实力正在复苏。如果这种趋势继续下去,这种转变可能预示着即将到来的反弹。

未平仓合约对以太坊市场动态的影响

另一个重要指标是未平仓合约 (OI),代表市场中未平仓的多头仓位和空头仓位总数。从历史上看,未平仓合约已经显示出预测市场调整的能力。例如,2024 年 6 月,以太坊的价格达到 3,800 美元,同时未平仓合约达到 130 亿美元的峰值。在 2024 年 8 月 5 日的宏观经济事件引发的调整之后,未平仓合约暴跌至 70 亿美元。Kesmeci 指出,要想让以太坊的价格大幅上涨,杠杆交易者需要重新进入市场,从而引发新的购买活动。

动荡中出现复苏迹象

虽然链上指标提供了乐观的前景,但以太坊的市场动态仍然很复杂。在过去的 24 小时内,市场见证了超过 43,000 名交易员的清算,总清算额达到 1.1152 亿美元,其中 2663 万美元是基于以太坊的。这样的发展表明持续的波动性和杠杆头寸的固有风险。同时,一个重要的趋势是以太坊交易所流出量的增加。数据显示,以太坊持续增长,8 月 14 日和 8 月 19 日出现了大量流出事件,导致交易所的供应量减少。

结论

以太坊离开交易所的数量不断增加表明投资者正在将其持有的资产转移到长期存储中,从而减少了交易供应并可能发出看涨情绪的信号。这一趋势加上链上指标的改善,增强了人们对以太坊的乐观前景。尽管如此,这些因素和更广泛的市场条件的相互作用最终将决定以太坊能否维持复苏或面临进一步的挑战。

目前的数据显示,以太坊的买家正在逐渐恢复实力。然而,时间将证明这是暂时的反弹还是由多头引领的强劲反弹的开始。

Trending Cryptos

Related Reads

AI Sweeps the Globe, So Why Is Crypto + AI Facing Gloom?

The article "AI Sweeps the Globe, But Why Is Crypto + AI So Bleak?" analyzes the disconnect between the booming AI industry and the struggling crypto+AI sector. It argues the issue is not flawed logic but severe demand-supply mismatch across four key sub-sectors. Decentralized compute and storage projects offer theoretical benefits like cost savings and data sovereignty but lack a decisive technical edge over entrenched cloud providers (AWS, GCP). Enterprises are unwilling to risk migration for unproven infrastructure that can't guarantee the performance and reliability needed for critical AI workloads. ZKML and privacy solutions address important issues like model verification but solve non-urgent, long-term concerns for most businesses currently focused on core performance and ROI. Demand here is likely to be regulation-driven (e.g., EU AI Act) rather than organic. AI agent infrastructure is developing foundational tech for a future multi-agent economy. However, the current market phase is dominated by internal process automation within single companies, making this technology premature. AI agent payments is highlighted as the only sub-sector where blockchain competes on a level playing field with traditional finance, as neither has adequately solved the challenges of machine-to-machine micropayments and real-time settlement. Overall, crypto+AI projects are building for future needs (data ownership, decentralization, transparency) that don't align with the industry's immediate priorities (performance, cost, stability). The absence of a flagship, large-scale use case further hinders mainstream adoption and capital inflow. The path forward requires either adapting to current market demands or patiently building the foundational infrastructure for the next phase of AI.

marsbit11m ago

AI Sweeps the Globe, So Why Is Crypto + AI Facing Gloom?

marsbit11m ago

"King of Pump Calls" Arthur Hayes Strikes Again, This Time Targeting Deribit

On June 29, BitMEX co-founder Arthur Hayes purchased approximately 6.16 million SYN tokens via OTC platform Flowdesk for around $2.2 million. Hayes subsequently declared on X that SYN represents one of the most asymmetric investments he has seen since HYPE, stating it's time for an options DEX to challenge the dominant platform Deribit, and identifying Hypercall as that challenger. SYN's price surged over 40% following his comments, with a tenfold increase in June 2026 alone, bringing its FDV to roughly $110 million. The article details Synapse Protocol's evolution from a cross-chain messaging and liquidity network into the chain-based options trading protocol Hypercall. Hypercall, built on the Hyperliquid ecosystem's HyperEVM, aims to be a universal options exchange supporting any asset size with capped loss (limited to premium paid) and no forced liquidations. Deribit, established in 2016, remains the centralized leader in crypto options with an estimated 85% market share in BTC and ETH options and $3.588 billion in assets. Its strengths include deep liquidity and professional tools, but it faces criticisms over custody risk, KYC requirements, and regulatory uncertainty. The analysis positions Hypercall not as an immediate replacement for Deribit's entrenched network effects, but as a potential complementary and differentiated competitor, particularly for DeFi-native assets and new asset classes like RWA. The article concludes by noting Hayes's recent mixed "call" record, including fully exiting and later re-buying HYPE, and the controversial price target for CARDS from his family office Maelstrom, which was followed by a significant price drop.

marsbit31m ago

"King of Pump Calls" Arthur Hayes Strikes Again, This Time Targeting Deribit

marsbit31m ago

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

AI Booms, But Crypto + AI Remains Sluggish: A Demand-Side Analysis Despite the AI industry's explosive growth and massive investment, the convergence of blockchain and AI (Crypto + AI) has seen limited traction. The core issue is a severe supply-demand mismatch, not a flawed premise. Analyzing four key sub-sectors reveals specific gaps: 1. **Decentralized Compute/Storage:** Offer logical benefits like data sovereignty and cost savings but lack a decisive technical advantage over entrenched cloud giants (AWS, GCP). Enterprises prioritize performance and stability and are unwilling to bear the switching risk and uncertainty of decentralized networks. 2. **Model Verification/Privacy (e.g., ZKML):** Address important long-term issues like auditability and data privacy, but these are not urgent operational pain points for most businesses today. Widespread demand will likely follow regulatory mandates (like the EU AI Act), not precede them. 3. **AI Agent Infrastructure:** Projects are building infrastructure for a future of autonomous, interacting agents. However, the current market focus is on internal process automation within corporate firewalls. The technology is ahead of market readiness. 4. **AI Agent Payments:** This is the only sub-sector where blockchain is on a level playing field with traditional finance. Both are trying to solve the unsolved problem of real-time, micro-transactions for machines, making it the most immediately competitive area. The overarching problem is that the AI industry invests heavily in solutions that solve immediate bottlenecks (e.g., faster memory, more power). Most Crypto + AI solutions target secondary, longer-term concerns (decentralization, transparency) and often come with performance trade-offs. The lack of a flagship, large-scale commercial success case further hinders mainstream capital inflow. The path forward requires either aligning more closely with the current industry's performance demands or patiently building the foundational infrastructure for the next phase of AI.

Foresight News41m ago

AI is Sweeping the Globe, So Why is Crypto + AI in a Slump?

Foresight News41m ago

Continuous Net Outflows from ETFs, Are Institutions Exiting?

US spot Bitcoin ETFs have experienced approximately $6 billion in net outflows over the past six weeks, marking the longest consecutive weekly withdrawal streak since their launch in 2024. The iShares Bitcoin Trust (IBIT) from BlackRock has been particularly affected, accounting for over 70% of recent outflows. On-chain analysis indicates that long-term Bitcoin holders (holding for over 155 days), who control about 83% of the circulating supply, remain steadfast. The selling pressure is primarily coming from allocators who entered through ETF brokerage accounts. This represents the first major collective capitulation since Bitcoin gained mainstream Wall Street recognition, driven more by risk-off portfolio adjustments than a fundamental rejection of the asset. Factors such as rising inflation, a hawkish shift in Federal Reserve policy, massive capital inflows into AI infrastructure, and attractive IPO opportunities have redirected speculative funds. Bitcoin, treated as a high-beta risk asset, was among the first to be sold. While the pace of outflows has slowed significantly—from $1.72 billion in early June to $226.8 million mid-month—the structural issue remains. IBIT's large size means its outflows alone exert substantial market pressure. With spot market volume thin, new capital inflows absent, and ETF buying muted, the market lacks sufficient buying support to absorb this selling. The coming sessions are critical. If IBIT outflows decelerate and Bitcoin reclaims $60,000, this phase could be seen as a healthy reset. However, if heavy IBIT redemptions resume and the price falls below $58,000, it would signal a more sustained institutional exit, requiring non-ETF buyers to shoulder the entire selling pressure alone. The ETF, while lowering entry barriers, has not removed Bitcoin's inherent volatility.

marsbit1h ago

Continuous Net Outflows from ETFs, Are Institutions Exiting?

marsbit1h ago

Introduction to the Concept of World Models: A Story from Psychology to the Main Battlefield of AI

**World Models: From Psychology to AI's Core Concept** "World model" is a trending but often confusing term in AI, describing a system that allows machines to internally simulate, predict, and rehearse potential outcomes before taking real-world action—like a mental "sandbox." While definitions vary—Yann LeCun emphasizes physical understanding, OpenAI's Sora is a video-based "world simulator," Google DeepMind's Genie 3 creates interactive 3D environments, and companies like Alibaba and Tesla focus on practical applications—the core goal is consistent: reduce reliance on vast real-world data by creating an internal, predictive model for safer and more efficient AI. The concept has deep roots, tracing back to psychologist Kenneth Craik (1943). In AI, it was revitalized by researchers like David Ha and Jürgen Schmidhuber (2018). Major technical approaches include: 1) generative video models (e.g., Sora) for visual realism; 2) abstract predictive models (e.g., LeCun's JEPA) for efficiency and physical reasoning; and 3) explicit 3D simulators (e.g., NVIDIA Omniverse) for precision. Fei-Fei Li proposes a classification based on the AI action loop: renderers (output observations), simulators (output world states), and planners (output actions). The emerging "World Action Model" (WAM) paradigm aims to unify future prediction and action generation. An industry framework is forming: upstream (data, compute, sensors), midstream (general and vertical platforms), and downstream applications (autonomous driving, robotics, gaming, etc.). Autonomous driving is currently the most mature use case. The current lack of a unified definition reflects the field's early, dynamic stage, similar to past tech revolutions. Different approaches—focusing on pixels, physics, or behavior—represent parallel explorations of how best to compress and understand the world. This diversity, while seemingly chaotic, signals that world models have moved from an academic idea to a critical industrial battleground, ultimately aiming to give machines the ability to understand, imagine, and reason about the world.

marsbit1h ago

Introduction to the Concept of World Models: A Story from Psychology to the Main Battlefield of AI

marsbit1h ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of ETH (ETH) are presented below.

活动图片