Monero price prediction – Why $400 is the critical support for XMR now

ambcryptoPublicado em 2025-12-15Última atualização em 2025-12-15

Resumo

Monero (XMR) experienced a 4.09% decline over the weekend after facing resistance at the $418–$420 level, a key barrier over the past six weeks. While the weekly chart shows a bullish structure and RSI momentum, the On-Balance Volume (OBV) indicates a bearish divergence, suggesting weakening demand. On the 4-hour chart, both RSI and OBV signal increased selling pressure, making a test of the $395 support likely. Although a bounce from $400 is possible if Bitcoin rallies above $90k, traders are advised to take profits on long positions due to lack of bullish strength. Key support levels to watch are $395, $380, and $360. A sustained XMR uptrend would require stronger buying pressure and a Bitcoin surge above $94k.

Monero [XMR] registered a slight dip of 4.09% over the weekend. Measured from Friday’s high of $419.4, this dip originated around the key resistance of $418 – A level that has been in play over the past six weeks now.

Last week, AMBCrypto pointed out that the $420 and $450 price levels would be the likely bullish targets for XMR. This move was expected after the retest of the $360-area as support. This prediction has since come to pass.

With Monero facing pushback from the first magnetic zone at $420, should traders expect the rally to continue, or a price dip back towards $360?

Is Monero’s bearish divergence a concern?

The weekly chart revealed a bullish swing structure. The dip below $367, the 50% retracement level, was defended, resulting in previous week’s bounce to $419. The RSI also hinted at bullish momentum with a reading of 59.

While the structure and momentum seemed to favor the bulls, the OBV disagreed. The volume indicator showed a bearish divergence with the price action of the past 7 months. The OBV’s lower highs suggested that Monero may be rallying on the back of weakening demand – An unsustainable trend.

On the 4-hour chart, the structure was still bullish, but the technical indicators had begun to turn. The RSI dropped below neutral 50 to signal a bearish momentum shift. The OBV also fell below the previous week’s low, highlighting a hike in selling pressure.

Hence, it appeared likely that the $395-level would be tested next as support. Can bulls hold on though?

The bullish case

Since the structure on the 4-hour chart was bullish, a recovery might be possible. A dip to the psychological $400-level, followed by a Bitcoin [BTC] rally back above $90k, could bolster short-term confidence.

Traders’ call to action- Take profits on long positions

Traders already in long positions can look to take profits. The rejection at $420 confirmed the lack of bullish strength to rally to $450. Such a rally would have set the conditions for a sustained uptrend, but it was not to be.

The evidence at hand showed that a drop below $400 is likely next. However, it is unclear how deep it would go. For now, the clear support levels would be $395, $380, and the $360 demand zone.

Traders can wait for Bitcoin to climb back above $94k before looking to go long.

Final Thoughts

XMR’s OBV made new lows across the selected timeframes, highlighting a distinct lack of buying pressure.
Even if $400 is defended on Monday, traders should be wary of going long too soon. A Bitcoin short-term rally is necessary to boost altcoin confidence.

Disclaimer: The information presented does not constitute financial, investment, trading, or other types of advice and is solely the writer’s opinion

Criptomoedas em alta

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

Leituras Relacionadas

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbitHá 5m

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbitHá 5m

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbitHá 6m

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbitHá 6m

Bitcoin, XRP left out – The S&P Digital Asset Index shows crypto’s biggest shift yet

Pantera Capital and S&P Dow Jones Indices have launched the S&P Digital Asset Index, a basket of 18 cryptocurrencies focused on fundamental strength. Notably, Bitcoin and XRP are excluded from the initial confirmed assets, which include Ethereum, BNB, Solana, Hyperliquid, and Tron. This selection highlights a potential market shift, prioritizing Layer-1 networks with proven scalability, utility, and sustainable on-chain revenue—reportedly $3 billion annualized in the past six months—over mere market capitalization. Analysts suggest this "fundamental index for crypto" could guide institutional capital toward assets with real economic activity, possibly leading to a significant divergence in the next market cycle where fundamentals, not just price momentum, drive investment.

ambcryptoHá 14m

Bitcoin, XRP left out – The S&P Digital Asset Index shows crypto’s biggest shift yet

ambcryptoHá 14m

The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

"White House Speech Prompt Operator Earns Over $100,000 Using Insider Information on Prediction Markets" U.S. White House staffer Gabriel Perez, a long-time teleprompter operator for former President Donald Trump, has been suspended without pay for using non-public information to profit on prediction markets. As one of the few individuals with advance access to Trump's prepared speech texts, Perez placed bets on specific words or phrases Trump would mention in speeches over a three-month period, earning over $100,000. His activities were flagged by the prediction platform Kalshi, which froze over $90,000 in his account and reported him to the Commodity Futures Trading Commission (CFTC). While Perez avoided criminal charges, he is required to return his profits and cease such trading. This case marks the third major instance of insider trading on prediction markets involving government or corporate insiders, following earlier cases involving a special forces soldier and a Google engineer. The incident highlights the vulnerability of "mention" markets on prediction platforms, where individuals with advance knowledge or even the speakers themselves can easily manipulate outcomes. In response, platforms like Kalshi are tightening rules, now requiring users to disclose their employers to help prevent similar abuses.

marsbitHá 1h

The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

marsbitHá 1h

White House 'Exclusive' Teleprompter Operator Cashes in Over $100,000 Using Insider Information for Predictions

The White House is embroiled in another insider trading scandal, this time involving a teleprompter operator for former President Trump. Gabriel Perez, a longtime aide who controlled Trump's speech teleprompters, used his advance knowledge of speech content to profit over $100,000 on prediction markets like Kalshi by betting on which specific words Trump would mention. After Kalshi's monitoring systems flagged suspicious trading activity and froze his funds, the case was referred to the CFTC. Perez was suspended without pay by Trump, who called his actions "disgusting." Unlike previous insiders prosecuted in similar cases, Perez faced no criminal charges, only a CFTC settlement requiring him to return profits and cease such trading. The article highlights how "mention" markets are uniquely vulnerable to manipulation by insiders or even the speakers themselves, citing examples like a Grammy host jokingly saying a random word and a Coinbase CEO reading all prediction market options aloud. As prediction markets like Kalshi tighten compliance—such as requiring users to disclose employers—the piece argues this crackdown on insider trading, while promoting regulatory compliance, may move these markets further from "collective wisdom" and closer to being pure gambling venues.

Odaily星球日报Há 1h

White House 'Exclusive' Teleprompter Operator Cashes in Over $100,000 Using Insider Information for Predictions

Odaily星球日报Há 1h

Trading

Spot

Artigos em Destaque

Como comprar XMR

Bem-vindo à HTX.com!Tornámos a compra de Monero (XMR) simples e conveniente.Segue o nosso guia passo a passo para iniciar a tua jornada no mundo das criptos.Passo 1: cria a tua conta HTXUtiliza o teu e-mail ou número de telefone para te inscreveres numa conta gratuita na HTX.Desfruta de um processo de inscrição sem complicações e desbloqueia todas as funcionalidades.Obter a minha contaPasso 2: vai para Comprar Cripto e escolhe o teu método de pagamentoCartão de crédito/débito: usa o teu visa ou mastercard para comprar Monero (XMR) instantaneamente.Saldo: usa os fundos da tua conta HTX para transacionar sem problemas.Terceiros: adicionamos métodos de pagamento populares, como Google Pay e Apple Pay, para aumentar a conveniência.P2P: transaciona diretamente com outros utilizadores na HTX.Mercado de balcão (OTC): oferecemos serviços personalizados e taxas de câmbio competitivas para os traders.Passo 3: armazena teu Monero (XMR)Depois de comprar o teu Monero (XMR), armazena-o na tua conta HTX.Alternativamente, podes enviá-lo para outro lugar através de transferência blockchain ou usá-lo para transacionar outras criptomoedas.Passo 4: transaciona Monero (XMR)Transaciona facilmente Monero (XMR) no mercado à vista da HTX.Acede simplesmente à tua conta, seleciona o teu par de trading, executa as tuas transações e monitoriza em tempo real.Oferecemos uma experiência de fácil utilização tanto para principiantes como para traders experientes.

381 Visualizações TotaisPublicado em {updateTime}Atualizado em 2026.06.02

Discussões

Bem-vindo à Comunidade HTX. Aqui, pode manter-se informado sobre os mais recentes desenvolvimentos da plataforma e obter acesso a análises profissionais de mercado. As opiniões dos utilizadores sobre o preço de XMR (XMR) são apresentadas abaixo.