Почему упала цена биткоина в сентябре

cryptonews.ruPublished on 2022-05-05Last updated on 2024-09-05

Аналитики увидели признаки страха в поведении краткосрочных инвесторов биткоина, считающихся самой уязвимой категорией держателей с точки зрения рыночных стрессов

Краткосрочные инвесторы несут «бумажные» убытки, что может привести к дополнительному давлению на котировки главной криптовалюты в случае продолжения ее падения, пишут аналитики Glassnode.

Средняя цена покупки биткоина находится на отметке $62,4 тыс. По такой цене криптовалюту покупали краткосрочные инвесторы, которые держат биткоины менее 155 дней.

Опасения аналитиков связаны с тем, что, как только цена биткоина достигнет средней точки безубыточности для такой категории держателей или превысит ее, они с большой вероятностью начнут массово продавать монеты, опасаясь вновь остаться в минусе.

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Как анализировать данные о краткосрочных и долгосрочных холдерах биткоина

Glassnode специализируется на анализе ончейн-данных блокчейна биткоина. Так называемая ончейн-аналитика — это вид исследования информации, полученной из публичных блокчейн-сетей, таких как биткоин и Ethereum. Технология блокчейн представляет собой открытый реестр информации со всей историей транзакций, позволяя любому компетентному лицу из любой точки мира и в любое время посмотреть на эти операции и сделать собственные выводы.

Исследователи начали использовать ончейн для наблюдения за транзакционным поведением пользователей, ценового прогнозирования, а также для сопоставления полученных данных из других источников информации для более глубокой аналитики.

Таким образом, опираясь на собственные метрики, Glassnode зафиксировала расхождение в настроениях долгосрочных и краткосрочных держателей биткоина. Поведение обеих групп обычно служит индикатором рынка и используется в прогнозах. Разнонаправленные действия владельцев монет нередко свидетельствуют о краткосрочной волатильности.

Это указывает на то, что условно среднестатистический инвестор в главную криптовалюту остается в прибыли даже в условиях продолжающегося снижения котировок.

В Glassnode подчеркнули, что хоть краткосрочные держатели и представляют меньшинство, их растущие нереализованные убытки считаются риском для дополнительного давления на цену биткоина.

Пока поведение инвесторов далеко от экстремальных уровней паники, наблюдавшихся во время распродажи в середине 2021 года или медвежьего рынка 2022 года. Однако появились первые признаки страха в движениях главной криптовалюты относительно ончейн-анализа.

20-Year-Old Founder Hires 18-Year-Old Employee, Funded by 19-Year-Old

The AI industry is experiencing a radical youth movement. Companies are aggressively recruiting extremely young talent—interns as young as 17 with daily salaries up to $800, and recent undergraduates earning annual packages of $200k-$1M. This shift prioritizes being "AI Native"—a mindset more common in the post-2000 generation—over traditional experience. Major tech firms like ByteDance, Tencent, and Alibaba restructure to empower young researchers directly, finding that past management expertise can hinder innovation in this fast-paced field. This creates a self-reinforcing cycle: young researchers gain authority, hire peers, attract young investors, and start companies, collectively building a new power network that sidelines older professionals. The intense competition for a limited pool of top young minds has led to astronomical salaries, defensive hiring, and sophisticated early talent scouting from high school. However, this concentration of opportunity exacerbates inequality, leaving many experienced workers facing obsolescence as AI disrupts traditional tech roles. While rewarding "extraordinary" talent unprecedentedly, the industry is simultaneously delivering a harsh penalty for those perceived as ordinary or tied to outdated methods.

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20-Year-Old Founder Hires 18-Year-Old Employee, Funded by 19-Year-Old

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TL;DR: Ahead of Micron's Q2 FY26 earnings report on June 24, Citi raised its price target from $840 to $1,200, citing stronger-than-expected 2026 memory prices and high gross margins. The stock price had already neared $1,211, shifting focus to whether the aggressive earnings assumptions will hold. The $1,200 target is based on significantly higher FY27 EPS estimates ($114.73), driven by projections of DRAM ASPs rising 200% and NAND ASPs rising 186% in 2026. The bullish outlook hinges on the ongoing transition of spot price increases (DRAM up 52% YTD) to contract prices, with server demand being a key driver. Supply remains tight, with a projected 5% DRAM shortage in 2026, exacerbated by HBM production consuming capacity. Long-Term Agreements (LTAs) could provide stability but their specific terms are crucial. Risks include HBM yield/ramp challenges, potential industry overcapacity, and AI/data center capex fluctuations. With the stock near the target, future moves depend on the continuation of strong pricing, tight supply, and high margins.

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Micron Q2 Earnings Preview: Citi Raises Price Target

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After Missing the 20x, I've Found a 'Dumb' Method for AI Investing

**Missing the 20x Opportunity: A Simple 'Dumb' Approach to AI Investing** The AI boom, driving NVIDIA's revenue from $60B to $216B in two years, creates immense investment pressure. However, like the internet bubble of 2000, the largest AI opportunities likely lie ahead, perhaps after a correction. Instead of rushing in now or waiting paralyzed for a crash, the author proposes a third way: building a "knowledge warehouse" by systematically mapping the AI industry to be ready when opportunities arise. The core of the strategy is understanding AI's four-layer value chain: 1. **Compute Infrastructure (The "Engine"):** This foundational layer, where all money eventually flows, includes: a) **Chip Design:** NVIDIA's dominance via its CUDA ecosystem, b) **Chip Manufacturing/Packaging/Memory:** TSMC's near-monopoly in advanced manufacturing and SK Hynix's lead in High Bandwidth Memory (HBM), c) **Optical Interconnects:** Essential for large-scale AI clusters (e.g., Lumentum, Coherent), d) **Cooling & Power:** Critical for high-density AI data centers (e.g., Vertiv), e) **Servers/Data Centers & Cloud Platforms:** The physical and virtual wholesale providers. 2. **Models & Tools (The "OS"):** The competitive layer of foundation models (OpenAI, Anthropic, Google, Meta, xAI), now generating real revenue. A key shift is the center of gravity moving from **Training** models to **Inference** (running models), which demands different chip characteristics and could challenge NVIDIA's monopoly. 3. **Middleware & Platform ("The Glue"):** Connects models and applications (e.g., Scale AI, Hugging Face). This layer could explode if applications take off. 4. **Vertical Applications ("The Cash Register"):** Where AI meets end-users (e.g., enterprise AI, coding tools, medical AI, robotics). A critical cross-cutting constraint is **Energy**, as AI's massive power consumption drives investment in nuclear and other energy infrastructure. The author identifies four key questions for further research: 1) How will the shift from Training to Inference reshape the competitive landscape? 2) With tech giants spending over $600B on capex, where is the ROI from AI applications? 3) What are the under-the-radar opportunities in the "second" and "third" circles of the value chain (e.g., cooling, specialty foundries)? 4) How will geopolitics (e.g., U.S.-China chip restrictions) bifurcate the supply chain? The conclusion is that missed opportunities stem from insufficient research, not slow timing. By methodically studying each layer—its business models, competition, and valuations—investors can build the "killer intuition" needed to act decisively when the market presents its chance.

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After Missing the 20x, I've Found a 'Dumb' Method for AI Investing

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Having Raised $37 Million with a16z's Backing, Why Did Goldfinch Ultimately Fail?

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"Rented Conviction: How Much of Bitcoin ETF Flows Is Real Money" The weekly inflows into Bitcoin ETFs are often interpreted as a gauge of institutional belief. However, a significant portion of this activity is driven by a hidden arbitrage trade, not directional conviction. The core mechanism is a cash-and-carry arbitrage: traders buy spot Bitcoin (often via ETFs) while simultaneously shorting CME futures to lock in the price difference, or "basis." This delta-neutral trade is essentially an interest rate play. In weekly data, about half the fluctuation in ETF flows can be explained by new short positions added by leveraged funds (hedge funds), with a correlation of 0.70. Bitcoin's price movement in a given week shows no statistical power in predicting these flows. While this arbitrage trade drives weekly *volatility*, it is not the main component of the cumulative *stock*. Of the total ~$55 billion in net ETF inflows, the current net arbitrage position is only about $1 billion. The remainder is steady, directional buying averaging ~$400 million per week, which constitutes the vast majority of the accumulated "mountain" over two years. Thus, ETF flow data overstates the *volatility* of conviction, not its *level*. This arbitrage trade has been unwinding for nearly two years. Leveraged fund short positions peaked at ~$14 billion in late 2024 and have since declined to ~$4.5 billion. When the basis compresses to unprofitable levels, ETF inflows and short positions retreat together. Recent outflows should not be mistaken for a loss of faith but rather the routine unwinding of this rate trade. For Ethereum ETFs, the pattern is weaker. Accounting for staking yield makes the basis often negative, so neither strong conviction buying nor robust arbitrage supports its flows. To interpret ETF flows correctly, monitor the CME basis versus T-bill rates and leveraged fund net shorts. They reveal how much of the next "demand" headline is real. The real, patient buy-and-hold demand is what constitutes the enduring bulk of ETF assets.

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