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

ambcryptoPublicado a 2025-12-15Actualizado a 2025-12-15

Resumen

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

Criptos en tendencia

Lecturas 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.

marsbitHace 6 min(s)

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

marsbitHace 6 min(s)

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.

marsbitHace 7 min(s)

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

marsbitHace 7 min(s)

Trading

Spot

Artículos destacados

Cómo comprar XMR

¡Bienvenido a HTX.com! Hemos hecho que comprar Monero (XMR) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Monero (XMR) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Monero (XMR)Después de comprar tu Monero (XMR), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Monero (XMR)Tradear fácilmente con Monero (XMR) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

383 Vistas totalesPublicado en 2024.12.10Actualizado en 2026.06.02

Cómo comprar XMR

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de XMR (XMR).

活动图片