38% of altcoins near all-time lows – Assessing 48% market cap wipeout

ambcryptoPublicado a 2026-03-09Actualizado a 2026-03-09

Resumen

Altcoins are experiencing severe market pressure, with liquidity drying up and capital outflows accelerating. The altcoin market capitalization has dropped 48%, from a peak of $1.9 trillion to $981 billion. According to analysis, 38% of active altcoins are trading near their all-time lows, with examples like LIT, ENA, and WLFI down between 69% and 93% from their peaks. Trading volume has plunged 58% since October 2025, and Open Interest has fallen sharply, indicating reduced risk appetite and dominant bearish sentiment. For a market reversal, a significant shift in sentiment and an influx of demand-side liquidity are required.

With the broader crypto market on a hedge, amid reduced liquidity, altcoins have suffered significantly. Altcoins saw a sharp drop in demand, and capital inflows dried up while the capitulation rate soared.

As the market continued to decline, most holders panicked and closed positions, causing massive downside pressure on the market.

That’s why these tokens declined massively, leading to substantial losses on their price charts.

38% of altcoins are trading near ATLs

Amid a prolonged market decline, the altcoins‘ market cap dropped by over 48%, from a peak of $1.9 trillion to $981 billion.

Such a massive drop reflected capital exits and increased rotation into other assets as investors sought better alternatives.

With capital leaving altcoins, most of these coins have declined significantly from their 2025 peaks and ATHs. According to Crypto Rover, over 38% of active altcoins are currently trading near their all-time lows.

Take Lighter [LIT], for example: the altcoin traded 2.6% above its all-time low and 86% below its ATH, as of writing. The same fate holds for Ethena [ENA], which traded 7% above its ATL and 93% below its ATH of $1.52.

Thirdly, World Liberty Financial [WLFI] traded only 6% above its ATL and 69% below its ATH at press time.

With these crypto coins experiencing such massive losses, it’s safe to say it’s not an altcoin season, as the Altcoin Season Index sat around 43% at press time.

Also, the CoinMarketCap Altcoin Season Index stood at around 38, suggesting that more altcoins have underperformed relative to Bitcoin.

Why the decline?

Undoubtedly, altcoins have continued to decline as some investors have stepped back from the market, while others have abandoned it entirely.

Altcoin trading volume plunged from a peak of $241 billion in October 2025 to $99 billion at press time. This marked 58% decline in trading volume, a clear sign of reduced demand and appetite for these assets.

AMBCrypto observed the same fate on the Derivatives market, with altcoins’ Open Interest (OI) falling from $170 billion to $69.5 billion. A drop in OI suggested a reduced risk appetite and increased risk-off sentiment across all market participants.

Such market conditions indicated dominant bearishness, with some scaling while others closing.

Traditionally, overwhelming bearish behavior has preceded poor market performances.

Therefore, if the prevailing sentiment persists, these tokens are most likely to continue declining, with some even at risk of dropping below their ATH.

For a wake-up, the broader market requires a significant change in sentiment, creating a path for demand-side liquidity.


Final Summary

  • Altcoins face broad market pressure as liquidity dries up and investors rotate capital into safer assets like Bitcoin.
  • Nearly 38% of altcoins now trade near their all-time lows.

Preguntas relacionadas

QWhat percentage of altcoins are currently trading near their all-time lows according to the article?

AAccording to the article, over 38% of active altcoins are currently trading near their all-time lows.

QHow much has the total market capitalization of altcoins dropped from its peak?

AThe altcoin market cap has dropped by over 48%, from a peak of $1.9 trillion to $981 billion.

QWhat are two key metrics mentioned that indicate a significant drop in demand and risk appetite for altcoins?

AThe two key metrics are a 58% decline in trading volume (from $241B to $99B) and a large drop in Open Interest (OI) on the derivatives market (from $170B to $69.5B).

QAccording to the Altcoin Season Index, are we currently in an 'altcoin season'?

ANo, it is not an altcoin season. The Altcoin Season Index was around 43%, and the CoinMarketCap Altcoin Season Index stood at 38, indicating that more altcoins are underperforming relative to Bitcoin.

QWhat does the article suggest is needed for the broader altcoin market to recover?

AThe article suggests the broader market requires a significant change in sentiment to create a path for demand-side liquidity.

Lecturas Relacionadas

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

Trading

Spot
活动图片