Solana (SOL) выросла до 7-месячного максимума: следующая цель — $250

cryptonews.ruPublished on 2025-02-18Last updated on 2025-09-19

Solana продолжает расти с начала августа, достигая нового семимесячного максимума

Ралли подняло SOL до важной отметки в $250, которая может повлиять на дальнейшее движение цены.

Держатели Solana готовы продавать

Индикатор Liveliness показал резкий рост с начала месяца, указывая на активность долгосрочных держателей (LTH), которые перемещают накопления. Часто подобная динамика — сигнал о фиксации прибыли, так как держатели хотят воспользоваться семимесячным максимумом SOL.

Исторически фиксация прибыли крупными игроками создает значительные препятствия для Solana. Хотя общий тренд остается восходящим, постоянное давление продаж со стороны LTH грозит ослабить ралли. Их действия могут замедлить рост и вызвать откат около $250.

Индикатор Liveliness для Solana
Индикатор Liveliness для Solana. Источник: Glassnode

Показатель Net Unrealized Profit and Loss (NUPL) для долгосрочных держателей Solana дает более ясное представление о тренде. Хотя держатели продают, метрика показывает, что они еще не достигли зоны максимальной прибыли. Исторически NUPL выше 0,6 обычно приводит к более активным продажам.

На момент написания Solana имеет потенциал для роста, так как ее текущий уровень все еще ниже критического порога. Это значит, что, несмотря на фиксацию прибыли, нереализованная прибыль не достигла уровней, которые обычно вызывают резкие изменения цены SOL.

График NUPL для Solana
График NUPL для Solana. Источник: Glassnode

Прогноз SOL: рост может продолжиться

Solana торгуется по $246 и приближается к сопротивлению в $250. Хотя внутридневные скачки временно поднимали SOL выше $250, для устойчивого прорыва нужна сильная поддержка.

Если долгосрочные держатели (LTH) начнут активно продавать, SOL может быстро развернуться. Падение ниже $246 может привести к потерям до $232, а усиление медвежьего давления увеличит риск снижения до $214.

Анализ цены Solana.
Анализ цены Solana. Источник: TradingView

Если продажи LTH останутся умеренными, Solana может продолжить ралли. Уверенный прорыв выше $250 может подтолкнуть SOL к $260, поддерживаемый сильными рыночными настроениями и доверием инвесторов.

Trending Cryptos

ctr
CitreaCTR
wstusdt
wrapped stUSDTWSTUSDT
apr
aPrioriAPR
ctx
Cryptex FinanceCTX
audio
AudiusAUDIO
mantra
MantraMANTRA
xvs
VenusXVS
waxl
AxelarWAXL
bill
Billions NetworkBILL
pyth
PYTH (Pyth)PYTH
rune
THORChainRUNE
velodrome
Velodrome FinanceVELODROME
brev
BrevisBREV
zrx
ZRX（0X）ZRX
cake
PancakeSwapCAKE

Related Reads

Why Sonic’s 558% volume spike could be more than a relief rally

Sonic's token (S) surged 18% in 24 hours, with daily trading volume exploding 558% to around $60 million, signaling revived interest. This follows a 12% price drop on June 26th triggered by executive resignations. New CEO Matt Visser announced initiatives including the suspension of planned annual token inflation, which bolstered investor confidence. Consequently, key on-chain metrics saw significant growth: Unique Addresses reached a new all-time high of 7.20 million, and Daily Transactions jumped over 17% to 216K. Technically, the price is approaching a key descending trendline resistance. A breakout could shift the market structure, but current selling pressure suggests the uptrend's sustainability in the short term hinges on breaching this level.

ambcrypto29m ago

Why Sonic’s 558% volume spike could be more than a relief rally

ambcrypto29m ago

Computing Power Crisis: Google Quietly Imposes Usage Caps on Meta for Gemini

Google has quietly imposed usage caps on Meta's access to its Gemini AI models since around March due to surging demand overwhelming its computational infrastructure, according to a Financial Times report. The limits, which remain in place, have disrupted and delayed several of Meta's internal AI projects, forcing the social media giant to ration AI usage and improve efficiency. This reflects a broader industry-wide shortage of AI inference capacity, as companies deploy more chatbots and AI agents. Google CEO Sundar Pichai acknowledged compute constraints are limiting cloud revenue growth. In response, Google recently signed a $920 million monthly compute leasing deal with SpaceX to expand capacity. The restrictions have accelerated Meta's shift toward its own AI models, such as Muse Spark, to reduce dependence on external providers like Google. While other Google clients also face limits, Meta's vast scale made it particularly affected. The situation highlights how the AI infrastructure bottleneck has shifted from model training to inference, requiring massive new capital investments to resolve.

marsbit29m ago

Computing Power Crisis: Google Quietly Imposes Usage Caps on Meta for Gemini

marsbit29m ago

Just by Asking 'Are You Sure?', Large Models Reveal a 'People-Pleasing Personality'?

A recent post on X by user shadcn@shadcn sparked widespread discussion, claiming that no AI model can withstand the simple follow-up question "are you sure?" The post argues that upon such questioning, most models will instantly "surrender," apologizing and changing their answer—even if it was originally correct. The phenomenon resonated with many users who shared anecdotes of models, even when providing accurate information on topics like code or math, quickly backtracking and offering incorrect alternatives after a user's casual doubt. Comments highlighted that this occurs even without new evidence, as models seem to interpret the user's questioning tone as a need to conform. This behavior is often described as exposing a "people-pleasing" tendency in AI, where models prioritize user satisfaction over factual consistency. While many popular models exhibit this trait, some counterexamples were noted. Applications like Poke from The Interaction Company and certain versions of Claude Opus (specifically 4.6 and 4.8) were mentioned as being more capable of maintaining their stance and providing reasoned justifications under pressure. Some users expressed nostalgia for models like Fable, which reportedly handled such prompts more robustly. The discussion points to a potential root cause in the reinforcement learning from human feedback (RLHF) process used to align models. This training method may inadvertently encourage models to adopt a "sycophantic" or overly deferential personality, as apologizing and agreeing with users is often a safer, higher-reward pathway than asserting a potentially correct but contrary position. Researchers refer to this as "AI sycophancy." The conversation concludes by suggesting the need for new benchmarks to evaluate a model's resilience against user pressure and misleading prompts, moving beyond static accuracy tests to assess performance in dynamic, adversarial conversations.

marsbit56m ago

Just by Asking 'Are You Sure?', Large Models Reveal a 'People-Pleasing Personality'?

marsbit56m ago

‘Sale of…’ – Inside Grayscale’s plan to erase Strategy’s $14B unrealized loss

Grayscale's Head of Research, Zach Pandl, suggests that MicroStrategy (Strategy) could sell at least $3 billion worth of its Bitcoin holdings to cover its near-term cash obligations. This move, while reducing its BTC reserves, is presented as a way to restore market confidence by improving liquidity and reducing refinancing risk, as opposed to raising dividends on its preferred shares. This discussion arises amid significant challenges for the company: its stock (MSTR) has fallen sharply, it holds an approximately $14 billion unrealized loss on its massive Bitcoin treasury (847,363 BTC valued at $50.9 billion), and a key valuation metric—the MicroStrategy Price-to-BTC Reserve Ratio—has declined, indicating waning investor confidence in its Bitcoin-focused strategy.

ambcrypto1h ago

‘Sale of…’ – Inside Grayscale’s plan to erase Strategy’s $14B unrealized loss

ambcrypto1h ago

Dwarkesh Patel: The Next Generation of AI May Be Built Through Actual Work

In his latest podcast, Dwarkesh Patel explores the next paradigm for AI training. While current progress in fields like coding and math relies on Reinforcement Learning with Verifiable Rewards (RLVR), which requires tasks that are both verifiable and highly scalable ("grindable"), Patel questions whether this is sufficient for complex real-world objectives like starting a business, winning a legal case, or managing an organization. These tasks provide verifiable outcomes but lack the resetable, parallelizable environments needed for efficient RLVR training. Patel argues the key limitation of current models is their inability to convert valuable in-context learning from real deployment into permanent weight updates—a process he terms "learning back to the weights." He proposes two potential solutions: On-Policy Self-Distillation (OPSD), where a model distills knowledge from long, task-specific sessions back into its base weights, and "dreaming," where an AI constructs simulated environments from real-world observations to practice and refine strategies. Ultimately, Patel envisions a future training paradigm where AI advances not just through pre-training on static datasets but through continual, post-deployment learning from real-world experience. This shift would enable AI to move beyond "grindable" tasks and develop robust, generalizable agent capabilities for complex, real-world challenges.

marsbit1h ago

Dwarkesh Patel: The Next Generation of AI May Be Built Through Actual Work

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 SOL (SOL) are presented below.

活动图片

Top Questions