Is It Time To Give Up On Dogecoin And Shiba Inu? On-Chain Metrics Has Answers

bitcoinistPublicado a 2026-03-02Actualizado a 2026-03-02

Resumen

The on-chain metrics for Dogecoin (DOGE) and Shiba Inu (SHIB) indicate a strong bearish sentiment amid a broader crypto market downturn. Dogecoin's Price Daily Active Addresses (DAA) divergence has fallen to -49%, a two-month low, reflecting weak demand as its price dropped below $0.10. Its daily active addresses have significantly declined, with seven-day totals under 300,000. Similarly, Shiba Inu's Price DAA divergence is at -29%, its lowest this year, with active addresses remaining below 10,000 since January. Derivatives data also show reduced trading volumes and open interest for both meme coins, with a short-biased market sentiment. Further declines are possible due to ongoing market uncertainty and geopolitical tensions.

Dogecoin and Shiba Inu are currently facing bearish sentiment due to the crypto market downtrend. On-chain metrics also highlight the current sentiment, with market participants choosing to stay on the sidelines amid this downtrend.

On-chain Metrics Signal Bearish Sentiment Towards Dogecoin and Shiba Inu

Santiment data shows that Dogecoin’s Price Daily Active Addresses (DAA) divergence has dropped to -49%, signaling weak demand in the meme coin’s ecosystem even as price continues to drop. This figure marks a two-month low for DOGE and comes amid its recent drop below the psychological $0.10 level.

Furthermore, the Daily Active Addresses on the Dogecoin network continue to waver. Data from Santiment shows that the DAA on the network dropped from as high as 87,727 on January 31 to as low as 38,696 on February 28. The total Active addresses over the last seven days are below 300,000, which also signals the low demand for the meme coin at the moment.

Source: chart from Santiment

Like Dogecoin, Shiba Inu is also facing weaker demand amid the recent price downtrend. Santiment data shows that the Price DAA Divergence has dropped to -29%, the lowest level this year. This notably coincides with SHIB’s decline to its lowest level this year, with the meme coin now down 25% year-to-date (YTD).

Shiba Inu’s Daily Active Addresses have also remained flat since the start of the year, indicating that investors are opting against investing in the second-largest meme coin by market cap. For context, SHIB’s DAA on March 1 was just 1,984, down from the multi-month high of 377,000 recorded in October last year. Since the start of this year, the Daily Active Addresses have remained below 10,000.

It is worth noting that Dogecoin and Shiba Inu remain at risk of further declines as tensions between the U.S. and Iran escalate. Further declines in these meme coins are likely to lead to a drop in these on-chain metrics as market participants stay on the sidelines amid this uncertainty.

Derivatives Metrics In The Red As Traders Sit On The Sidelines

Dogecoin and Shiba Inu’s derivatives metrics are also in the red as crypto traders sit on the sidelines amid the current market sell-off. CoinGlass data shows that DOGE’s derivatives trading volume is down by over 34% down to $2.36 billion. Open interest is down over 9%, dropping to $907 million, while options trading volume has crashed 31%. The long/short ratio is below 1, signaling that most traders are shorting DOGE at the moment.

Similarly, Shiba Inu’s derivative metrics signal that sellers are currently dominating the market, as bulls remain cautious amid market uncertainty. CoinGlass data shows that SHIB’s derivative trading volume has crashed 28%, down to $132 million, while open interest is down to $54 million.

DOGE trading at $0.09 on the 1D chart | Source: DOGEUSDT on Tradingview.com

Preguntas relacionadas

QWhat is the current Price Daily Active Addresses (DAA) divergence for Dogecoin, and what does it indicate?

ADogecoin's Price Daily Active Addresses (DAA) divergence has dropped to -49%, signaling weak demand in the meme coin's ecosystem.

QHow have Shiba Inu's Daily Active Addresses (DAA) performed since the start of the year?

AShiba Inu's Daily Active Addresses have remained flat and below 10,000 since the start of the year, indicating low investor interest.

QAccording to the derivatives data, what does a long/short ratio below 1 for Dogecoin signify?

AA long/short ratio below 1 for Dogecoin signifies that the majority of traders are currently shorting the asset.

QWhat external factor is mentioned as a risk that could lead to further declines for Dogecoin and Shiba Inu?

AEscalating tensions between the U.S. and Iran are mentioned as a risk that could lead to further declines for these meme coins.

QHow much has Shiba Inu's derivative trading volume decreased, according to CoinGlass data?

AShiba Inu's derivative trading volume has crashed by 28%, down to $132 million.

Lecturas Relacionadas

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 11 min(s)

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

marsbitHace 11 min(s)

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbitHace 1 hora(s)

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbitHace 1 hora(s)

Trading

Spot
活动图片