Matrixport Research: After Five Consecutive Months of Bitcoin Decline, Conditions for a Market Rebound Are Gradually Forming

MatrixportPublicado a 2026-03-13Actualizado a 2026-03-13

Resumen

Matrixport Research: Conditions for a Market Rebound Gradually Forming After Bitcoin's Consecutive Five-Month Decline Amid low trading volumes and weak market sentiment, with many investors shifting focus to traditional assets like gold and oil, underlying market conditions are quietly improving. Bitcoin has declined for five consecutive months—a historically rare occurrence—which has often preceded阶段性反弹 (stage-wise rebounds) in the past. Similarly, the total market cap of altcoins has fallen to a range that has historically triggered multiple rebound initiations. Although the overall altcoin model has not yet turned bullish, the number of altcoins reclaiming their 30-day moving average and showing improved momentum through quantitative screening has significantly increased. With stablecoin funds flowing back into the market, overall liquidity conditions are also improving, pointing to a potential market inflection window. From a historical perspective, Bitcoin often experiences阶段性反弹 (stage-wise rebounds) after three consecutive months of decline in a bear market. A sustained decline of four to six months with little recovery is relatively rare. The market is currently in such an extreme sequence, increasing the probability of a short-term counter-trend recovery. Simultaneously, the valuation of the altcoin sector has entered a range where周期性反弹 (cyclical rebounds) have historically been more likely. When the total altcoin market cap deviates approximately 30% from its 90-...

"Amid low trading volume and weak sentiment, the market structure is quietly improving, and a potential rebound window may be forming."

Overall sentiment in the crypto market remains weak, with trading volume staying at relatively low levels. Many traders have shifted their attention to traditional assets like gold and oil. However, beneath the surface calm, some key changes are gradually emerging. Bitcoin has fallen for five consecutive months, which is relatively rare in history, and similar trends have often preceded阶段性 rebounds. At the same time, the total market cap of山寨币 has also fallen back to a range where反弹 have historically started multiple times. Although our山寨币 model has not yet officially turned bullish, the number of coins重新站上30日均线 and selected through momentum screening has significantly increased. As stablecoin funds重新流入 the market, overall liquidity conditions are also continuously improving. These phenomena collectively point to a potential market inflection window that may be forming.

Historically Rare Consecutive Pullback: Bitcoin's Potential Bottom is Being Built

From past experience, Bitcoin often experiences阶段性反弹 after three consecutive months of decline in a bear market, while持续下跌 for four to six months with little recovery is relatively少见. The current market is in such an extreme sequence, which increases the probability of a short-term逆势修复.

At the same time, the valuation level of the山寨币 sector has also entered a range where周期性反弹 have historically been more likely to occur. When the total market cap of山寨币 deviates from the 90-day moving average by about 30%, the market is often in an overall bottom-building phase, after which Bitcoin and the山寨币 sector usually experience sustained recovery. Although trading volume remains low, the price structure of some山寨币 has begun to improve, and Bitcoin is also building a potential阶段性底部 near $66,000. If the price can hold the current support range and gradually break through key resistance levels upward, the market recovery process is expected to continue.

同步改善的动能与流动性: Market Participation Begins to Expand

Although the overall performance of山寨币 has been weak in this cycle, some structural changes are emerging. More and more山寨币 are重新站上 the 30-day moving average and have begun to阶段性 outperform Bitcoin, which is often an early signal of overall market动能改善. At the same time, the number of山寨币 selected through quantitative momentum screening has significantly increased, with some标的 simultaneously possessing both improving momentum and fundamental catalysts.

More importantly, the market funding environment is also changing. The previous situation dominated by liquidations and capital outflows is gradually turning into capital回流. The重新扩张 of stablecoin liquidity is one of the important signals. In the past month alone, Circle's USDC recorded a net inflow of approximately $8 billion, indicating that new funds are重新 entering the crypto market. As liquidity gradually improves, the probability of funds being reallocated to Bitcoin and Ethereum is also rising, which will provide support for a broader market.

Overall, crypto market sentiment remains subdued, but multiple key conditions are gradually forming. After experiencing a historically relatively rare period of consecutive monthly declines, Bitcoin seems to be building a potential bottom; the回流 of stablecoin funds is also improving market liquidity conditions. At the same time, the breadth of the山寨币 market is beginning to expand, with more coins重新站上 the 30-day momentum boundary line. Although our山寨币 model has not yet officially turned bullish, the trading setup meeting the screening conditions has risen to its highest level in months. If Bitcoin confirms a trend breakout above key points, the probability of a broader market阶段性反弹 will further increase.

The above views are from Matrix on Target, contact us to get the full Matrix on Target report.

Disclaimer: The market carries risks, and investment requires caution. This article does not constitute investment advice. Digital asset trading may carry significant risks and instability. Investment decisions should be made after carefully considering personal circumstances and consulting financial professionals. Matrixport is not responsible for any investment decisions based on the information provided in this content.

Preguntas relacionadas

QAccording to the Matrixport report, what historical pattern suggests a potential rebound for Bitcoin after a prolonged decline?

AHistorically, Bitcoin has often seen a rebound after three consecutive months of decline. The current market is in an extreme sequence of five consecutive months of decline, which is relatively rare and increases the probability of a short-term counter-trend recovery.

QWhat is the significance of the total market cap of altcoins falling to a specific level relative to its 90-day moving average?

AWhen the total market cap of altcoins deviates by approximately 30% below its 90-day moving average, the market is often in a bottoming phase. This level has historically been a zone from which periodic rebounds in Bitcoin and the altcoin sector have started.

QWhat early signal of improving market dynamics is indicated by the behavior of altcoins?

AAn early signal of improving market dynamics is that a growing number of altcoins are reclaiming their 30-day moving averages and are beginning to outperform Bitcoin in phases.

QWhat key change in the capital environment is supporting the potential market turnaround?

AA key change is the shift from capital outflows dominated by events like exchange collapses to a trend of capital returning to the market. This is signaled by the re-expansion of stablecoin liquidity, with USDC alone recording a net inflow of approximately $8 billion in the past month.

QAround what price level is Bitcoin potentially building a阶段性 (stage) bottom, and what is required for the recovery to continue?

ABitcoin is potentially building a stage bottom around $66,000. For the recovery process to continue, the price needs to hold the current support zone and gradually break through key resistance levels upwards.

Lecturas Relacionadas

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Hace 11 min(s)

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

Odaily星球日报Hace 11 min(s)

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.

bitcoinistHace 1 hora(s)

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinistHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

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.

marsbitHace 1 hora(s)

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

marsbitHace 1 hora(s)

Trading

Spot
活动图片