Glassnode: трейдеры переходят в режим защиты после падения биткоина

cryptonews.ruОпубліковано о 2025-03-20Востаннє оновлено о 2025-10-21

  • В Glassnode указали на осторожность инвесторов после падения биткоина с примерно $115 000 до $104 000.
  • Участники рынка сокращают риски и фиксируют убытки.

Крипторынок перешел в фазу защиты, заявили в Glassnode, указав на сразу несколько технических и ончейн-метрик.

Эксперты назвали обвал биткоина с $115 000 до минимума $104 000 всего за четыре дня быстрой и решительной очисткой рынка. По их словам, это выбило с позиций более слабых участников и вызвало защитную ротацию по всему рынку.

С тех пор цена восстановилась, но «шрамы» после падения еще не зажили: настроения остаются осторожными, и это отражается в текущем позиционировании трейдеров, говорится в сообщении.

Также в Glassnode разобрали некоторые метрики, в частности:

  • индекс RSI пробил нижнюю границу и несколько восстановился, но остается в зоне слабости;
  • кумулятивная дельта объема остается отрицательной — давление продавцов не исчезает;
  • спотовые объемы во время падения снизились, что указывает на слабый спрос;
  • открытый интерес во фьючерсах сократился, а фандинг — упал.
  • на опционном рынке зафиксировали рост 25 Delta Skew — признак повышенного спроса на стратегии защиты от падения.

По словам экспертов, в совокупности эти сигналы указывают на рынок, который переходит в режим защиты, когда участники больше заботятся о сохранении капитала, чем о поиске прибыли от направления движения.

Кроме того, аналитики отметили, что ключевые показатели свидетельствуют о стрессе на рынке. По их данным, соотношение нереализованной прибыли и убытка (NUPL) стало отрицательным, а реализованная прибыль/убыток (Realized PnL) опустился ниже предыдущего минимума.

Впрочем, реализованная капитализация (Realized Cap) продолжает расти, что может свидетельствовать о накоплении биткоина долгосрочными инвесторами.

«Избыток рынка был устранен, защитные позиции сформированы, и структура позиционирования стала чище. Отскок от минимумов является обнадеживающим сигналом, однако рыночная структура остается хрупкой. Пока доверие не восстановится, рынок, вероятно, будет оставаться более осторожным, чем убежденным», — подытожили в Glassnode.

Напомним, ранее в CryptoQuant заявили, что биткоин перешел в позднюю стадию бычьего цикла.

Пов'язані матеріали

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

**How to Detect AI-Generated Videos: A Survey on Dynamic, Traceable, and Explainable Detection Systems** With rapid advances in AI video generation (e.g., Sora, Veo), creating highly realistic, multi-minute videos is now possible, widening the gap with detection research. Current AI video detection, often limited to unreliable binary classifications, is insufficient. This survey, accepted at ACL 2026, reframes the goal as **"factual fidelity verification"**—checking if a video's content (who, when, where, what) aligns with the real world perceptually and cognitively. It categorizes AI-generated videos into three paradigms: **Local Manipulation Videos (LMV**, e.g., face swaps), **Audio-Visual Editing (AVE**, e.g., lip-syncing), and **Generative Video Synthesis (GVS**, fully synthetic videos like Sora's). Detection challenges evolve from visual artifacts in LMV to multi-modal inconsistencies in AVE and higher-level world knowledge violations in GVS. The core proposal is a **Vision-Language Dual-View framework** with four hierarchical layers: 1. **Layer 1 (Intrinsic Visual Cues):** Analyzes low-level signal statistics, noise patterns, and physiological signals. 2. **Layer 2 (Spatiotemporal Consistency):** Checks for temporal coherence in object motion and scene dynamics. 3. **Layer 3 (Cross-Modal Consistency):** Verifies alignment between video, audio, and text within the video. 4. **Layer 4 (Language-Guided World-Level Reasoning):** Uses external knowledge, facts, and physical laws to judge semantic plausibility and factual correctness. The survey traces a shift in detection focus from lower layers (1 & 2) toward higher, language-involved layers (3 & 4). It also reviews evolving evaluation metrics and datasets tailored for each video paradigm. The conclusion advocates for a **dynamic, evidence-first detection system** that moves beyond simple classification. Future trustworthy detection requires combining visual evidence (from CV) with semantic reasoning and explanation (from NLP & multimodal AI), ultimately creating traceable and explainable judgments about a video's adherence to real-world constraints.

marsbit27 хв тому

How to Detect AI-Generated Videos? A Review of Dynamic, Traceable, and Explainable Detection Systems

marsbit27 хв тому

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

The article explores the surprising trend where AI's first major impact on crypto has been in security auditing, not in areas like trading or analytics. It details how AI-powered tools are dramatically lowering the barrier to finding smart contract vulnerabilities, enabling attackers to scan thousands of contracts and execute exploits within minutes. This has rendered traditional, manually-produced audit reports with their month-long validity periods increasingly obsolete, creating a critical "structural crack" in the old security model. Cases like Drift Protocol and KelpDAO show that even extensively audited protocols can be hacked through social engineering, operational flaws, or infrastructure misconfigurations beyond pure code review. Attackers are also using AI to find and exploit vulnerabilities in years-old, deployed contracts. Notably, OpenZeppelin's co-founder has expressed a grim view that "all DeFi is insecure" due to AI's asymmetric advantage. In response, the audit industry is undergoing a fundamental shift. While there's a short-term spike in defensive re-audits, the long-term business model is changing. Firms are developing AI-assisted systems and moving from one-time report deliveries towards embedded, continuous services like real-time monitoring and formal verification. Examples include AI tools uncovering critical, previously missed vulnerabilities in heavily audited protocols like Curve Finance and Zcash. The conclusion is that security must become a continuous investment, not a one-time checkbox, and audit firms must rapidly evolve their tools and service models to survive.

marsbit34 хв тому

It Turns Out the First Real-World Application of AI x Crypto is in Security Auditing

marsbit34 хв тому

Never expected that the first tangible application of AI x Crypto is in security auditing

Unexpectedly, the initial major application of AI in the Crypto sphere has turned out to be security auditing. In 2026, DeFi has faced significant security challenges, with 121 hacking incidents resulting in approximately $942 million in losses. While AI was expected to first impact areas like quantitative trading, its initial breakthrough has instead transformed security auditing by drastically lowering the cost and skill barrier for finding smart contract vulnerabilities. The traditional audit model is facing obsolescence. Advanced AI models, such as Claude Mythos, enable attackers to scan thousands of contracts and identify vulnerability patterns at scale, compressing the time from discovery to execution to mere minutes. This renders the month-long validity of traditional audit reports ineffective. Notably, attacks now frequently target well-audited, established protocols by exploiting business logic flaws, operational security weaknesses, and even years-old historical contracts, demonstrating that old audit reports offer zero protection. This pressure is forcing a fundamental shift in the industry. In the short term, a wave of defensive re-auditing is occurring, driven by projects seeking to meet new AI-era security standards and regulatory requirements. In the long run, audit firms' business models are diverging. The one-time report delivery model is declining in value, as evidenced by platforms like Code4rena shutting down. Leading firms are now pivoting towards AI-powered defense, integrating continuous monitoring, real-time on-chain risk detection, and embedding security directly into the development phase, as seen with tools like OpenZeppelin's Skills system. Ultimately, the era of "audit once, secure forever" is over. Security must become a continuous, embedded infrastructure investment for projects. For audit companies, survival depends on proactively transforming from traditional service providers into platforms offering AI-native, ongoing security solutions.

链捕手41 хв тому

Never expected that the first tangible application of AI x Crypto is in security auditing

链捕手41 хв тому

Торгівля

Спот
活动图片