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

ambcrypto2026-03-09 tarihinde yayınlandı2026-03-09 tarihinde güncellendi

Özet

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.

İlgili Sorular

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.

İlgili Okumalar

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

NVIDIA's Rubin Ultra, the top-tier variant of the newly announced Rubin AI accelerators, has reportedly seen significant specification downgrades, according to an industry report from SemiAnalysis. Initially designed with four compute dies (4-die), the Rubin Ultra is now said to be reduced to a 2-die design. Key changes highlighted in the report include: * **No increase in peak theoretical compute performance**, remaining at 35 PFLOPs like the standard Rubin. * **Severe reduction in memory capacity** to 192GB using 8-Hi HBM stacks, which is less than the standard Rubin's 288GB using 12-Hi stacks. * **Negligible memory bandwidth improvement** of only 1 TB/s. * **Slightly higher chip-level power consumption**. * The **primary upgrade is a massive increase in scale-up interconnect capacity**, supporting connections for up to 576 GPUs via NVLink, compared to 72 for the standard Rubin. The report suggests the redesign is primarily a cost-optimization move driven by the sharp rise in HBM (High-Bandwidth Memory) prices. By reducing the expensive HBM content and shifting investment towards enhanced system-scale networking, NVIDIA aims to maintain the platform's value for large-scale AI training clusters while managing soaring material costs. The news reportedly triggered a sell-off in South Korean memory stocks, with SK Hynix and Samsung shares falling around 8%, as markets grew concerned that NVIDIA—a major HBM buyer—might be reducing its reliance on high-capacity memory, potentially capping future pricing power for memory makers.

Odaily星球日报11 dk önce

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

Odaily星球日报11 dk önce

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist1 saat önce

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist1 saat önce

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit1 saat önce

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit1 saat önce

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit1 saat önce

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit1 saat önce

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1 saat önce

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1 saat önce

İşlemler

Spot