Ethena’s price slips 15% from its weekly high – Deeper losses ahead?

ambcryptoPubblicato 2026-03-07Pubblicato ultima volta 2026-03-07

Introduzione

Ethena (ENA) experienced a brief rally, reaching a local high of $0.12 on March 4th, accompanied by increased open interest and trading volume. However, this bullish momentum was short-lived, and the price has since fallen 15% from that peak. The longer-term trend remains bearish, with prices declining since August and a significant 50% drop over the past five weeks after losing the $0.21 support level. Technical indicators, including the Directional Movement Index and MFI, confirm strong bearish control. A recent bounce to $0.12 was rejected, and the next likely target is a drop to the $0.085 support level. A further decline of 7%-15% is anticipated if the price falls below $0.1.

Ethena [ENA] rallied to a local high of $0.12 on Wednesday, 04 March. An AMBCrypto report had detailed the swift surge in prices and the commensurate increase in Open Interest that signaled short-term bullishness.

The high trading volume on the day was also noted, but the longer-term bearish structure can be expected to prevail. Over the past 48 hours, the sellers regained control of the market too.

At the time of writing, ENA was 15% below the $0.12 local high water mark. Based on the evidence at hand, it would seem that a deeper drawdown was likely.

Ethena buyers cannot catch a break!

Any respite the buyers got over the past 8 months has lasted only a week or two, at best. Since topping out at around $0.8, Ethena prices have been falling since August. They fell below the $0.21 support in mid-January. At press time, the price was just above $0.1.

This represented a 50% drawdown in five weeks. It was a warning to altcoin investors that losing major support levels can lead to quick losses.

The Directional Movement Index has shown a strong downtrend on the weekly chart since October. The MFI has been falling lower, with the A/D making new lows too. Together, they highlighted the absolute control bears have enjoyed in recent months.

What next for ENA traders?

A bullish divergence with the momentum indicator led to a price bounce to $0.12. The aforementioned report had highlighted the longer-term trend and the liquidation cluster at $0.12 as a short-term price magnet.

The Fibonacci levels showed that the 78.6% level at $0.123 was not tested before the rejection. This may be another sign that sellers were in control and the market was firmly bearish. The technical indicators on this timeframe agreed as well.

The next target for ENA is the $0.085 extension level to the south.


Final Summary

  • Ethena’s long-term price action was bearish, and the short-term bounce reached its target and faced rejection.
  • A drop below $0.1 may be imminent, with a fall of 7%-15% likely over the next week too.

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

Domande pertinenti

QWhat was the local high price of Ethena (ENA) on Wednesday, 04 March, and how much has it fallen since then?

AEthena reached a local high of $0.12 on Wednesday, 04 March. At the time of writing, the price had fallen 15% from that high.

QAccording to the article, what is the next likely price target for ENA to the south (downside)?

AThe next target for ENA to the downside is the $0.085 extension level.

QWhat key support level did ENA fall below in mid-January, and what did this represent for investors?

AENA fell below the $0.21 support level in mid-January. This represented a warning to altcoin investors that losing major support levels can lead to quick losses, contributing to a 50% drawdown over five weeks.

QWhat technical indicators on the weekly chart were highlighted as evidence of the strong bearish control since October?

AThe Directional Movement Index (DMI) showed a strong downtrend, while the Money Flow Index (MFI) was falling lower and the Accumulation/Distribution (A/D) indicator was making new lows.

QWhat was the significance of the price not testing the 78.6% Fibonacci level at $0.123 during its recent bounce?

AThe failure to test the 78.6% Fibonacci retracement level at $0.123 was a sign that sellers were in control and that the market was firmly bearish, leading to the price rejection.

Letture associate

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.

marsbit2 min fa

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

marsbit2 min fa

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.

marsbit3 min fa

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

marsbit3 min fa

Trading

Spot
活动图片