Monero Gains Momentum After Recent Sell-Off, Faces Resistance at $363

TheNewsCryptoОпубліковано о 2026-02-11Востаннє оновлено о 2026-02-11

Анотація

Monero (XMR) is showing signs of stabilization around $340.26 after a sharp sell-off from its January highs near $790. The cryptocurrency is currently consolidating within a range of $320 to $350. Despite the recent rebound from intra-week lows, the overall trend remains bearish, with XMR trading below key moving averages including the 200-day SMA at $363.14, which now acts as a major resistance level. Immediate support is found near the 7-day SMA at $326.43. The RSI indicates oversold conditions but suggests a potential short-term recovery. If support fails, XMR could retest the $270 level.

Monero (XMR), the privacy‐focused cryptocurrency, showing signs of stabilization on the daily chart after a significant sell-off from its January highs near $790. As of today, XMR trades around $340.26, consolidating in the $320 to $350 range following a sharp downtrend over the past week.

After being triggered by a technical break‐and‐fail pattern, the current pattern reflects a rebound from recent intra‐week lows. This move follows a surge earlier in the year and subsequent correction.

Monero Shows Bearish Trend, Key Levels in Focus

Technical indicators from Binance’s daily chart highlight a predominantly bearish trend. The XMR price remains below critical moving averages, including the 30-day SMA at $470.47, 50-day SMA at $464.86, 100-day SMA at $431.80, and 200-day SMA at $363.14. However, it is holding just above the short-term 7-day SMA at $326.43, which currently provides immediate support.

Zooming in, the Relative Strength Index (RSI) shows that momentum is still oversold, with a reading near -20.27, but recent RSI movements suggest a slight easing of bearish pressure, indicating a possible short-term recovery or consolidation phase.

If XMR continues the uptrend the Key resistance lies at the 200-day SMA around $363, with stronger resistance expected between $430 and $470, where it failed to boost the bull earlier. A break above these levels would be necessary to signal a sustained reversal from the current bearish trend.

If the price fails to maintain support near the 7-day SMA, it may retest recent lows near $270, marking another critical support zone.

Highlighted Crypto News:

‌Ethereum Slips Toward $1,900 as Selling Pressure Intensifies

TagsAltcoinCrypto MarketMoneroXMR

Пов'язані питання

QWhat is the current trading price of Monero (XMR) and what range has it been consolidating in?

AMonero is currently trading around $340.26 and has been consolidating in the $320 to $350 range.

QWhat is the key resistance level that Monero is facing according to the 200-day SMA?

AThe key resistance level is at the 200-day Simple Moving Average (SMA), which is around $363.

QWhat does the Relative Strength Index (RSI) reading near -20.27 indicate about Monero's momentum?

AThe RSI reading near -20.27 indicates that the momentum is still oversold, but recent movements suggest a slight easing of bearish pressure, pointing to a possible short-term recovery or consolidation.

QWhat could happen if Monero fails to maintain support near its 7-day SMA?

AIf Monero fails to maintain support near the 7-day SMA at $326.43, it may retest recent lows near $270, which is another critical support zone.

QBetween which price levels is stronger resistance expected for Monero, according to the article?

AStronger resistance is expected between $430 and $470, where it previously failed to boost the bull run.

Пов'язані матеріали

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit26 хв тому

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit26 хв тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit30 хв тому

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit30 хв тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit30 хв тому

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit30 хв тому

Торгівля

Спот
活动图片