Ethereum Bearish Signal: Profit-Taking Transfers Spike

Bitcoinist2023-06-06 tarihinde yayınlandı2023-06-06 tarihinde güncellendi

Özet

On-chain data shows the Ethereum profit-taking transactions have surged recently, a sign that could be bearish for the asset's price....

On-chain data shows the Ethereum profit-taking transactions have surged recently, a sign that could be bearish for the asset’s price.
Ethereum Profit-Taking Volume Has Spiked To Highest Levels Since January
According to data from the on-chain analytics firm Santiment, a large amount of profit-taking seems to have been going on in the Ethereum market recently. The relevant indicator here is the “ratio of on-chain transaction volume in profit to loss,” which, as its name already suggests, tells us about how the profit-taking transfers currently compare with the loss-taking ones.

The indicator separates these two volumes by looking at the on-chain history of each coin being sold/moved to see what price it was last sold at. If this previous price for any coin was less than the price that it’s now being moved at again, then the metric naturally counts this sale inside the profit-taking volume.
On the other hand, the last selling price being more implies that the coin is being sold at a loss, and hence, the indicator adds its movement to the loss-taking volume.
Now, here is a chart that shows the trend in this ratio for Ethereum over the last half a year:

The value of the metric seems to have been quite high in recent days | Source: Santiment on Twitter
As displayed in the above graph, the Ethereum ratio between the profit and loss transaction volumes has observed an uplift during the past couple of days. The metric now has highly positive values.

Whenever the indicator has positive values, it means that the profit-taking volume is more than the loss-taking one right now. Thus, since the metric has green values currently, it would suggest that profit-taking transactions are the dominant force in the market.
During the spike in the ratio yesterday, the metric hit a peak value of 1.3, which means that there were 2.3 times as many profit-taking transfers happening on the blockchain as the loss-taking ones.
These metric values were the highest seen since back in January of this year when the rally had gone through its first leg. Historically, whenever investors have done a large amount of selling with the intent of harvesting profits, the cryptocurrency’s price has felt a bearish pressure.
This phenomenon can also be seen in the chart, as back in January the Ethereum rally slowed down its rapid upward trajectory when the profit-taking hit its peak.
When the profit-taking shot up yesterday, the price was above the $1,900 level. Since then, however, the asset has slid down below the mark, implying that the profit-taking may already be showing its impact.
Santiment believes that the ratio would have to cool back down if the price of Ethereum has to build a charge towards the $2,000 level.
At the time of writing, Ethereum is trading around $1,800, down 2% in the last week.

ETH hasn't moved much in the last few days | Source: ETHUSD on TradingView

İlgili Okumalar

Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

Jensen Huang, alongside AI leaders like Peter Norvig, Boris Cherny, and Andrew Ng, is advocating for a shift from "prompt engineering" to "loop engineering" as the new paradigm for AI development. Instead of manually crafting individual prompts, the focus is now on designing autonomous loops—systems where AI agents execute tasks, self-validate results, and iterate until completion without constant human oversight. A loop is a management framework that enables agents to operate independently. Key implementations are seen in Claude Code (with features like /loop, /goal, and /schedule) and OpenAI Codex, which employ multiple agents working in parallel within isolated environments. A core principle is the separation of roles: one agent (or model) performs the task, while an independent agent (or a smaller, separate model) validates the output to ensure objectivity. The article outlines a practical roadmap for implementing loops, starting with a "four-condition test" to assess suitability, building a minimal viable loop, and emphasizing critical pitfalls to avoid, such as lacking hard stop conditions or allowing loops to handle tasks requiring human judgment. This evolution is framed as the fourth major shift in AI interaction: from Prompt Engineering (crafting instructions) to Context Engineering (providing background information), then to Harness Engineering (building tool-enabled environments), and finally to Loop Engineering (creating self-sustaining systems). This progression reflects a consistent trend of increasing abstraction, moving human involvement from direct instruction to system design and rule-setting. The concept has academic roots in frameworks like ReAct, which formalized the "reason-act-observe" cycle. While loop engineering promises greater automation, experts caution about managing token costs and warn against outsourcing understanding—AI can assist, but deep problem comprehension remains essential.

marsbit48 dk önce

Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

marsbit48 dk önce

MemeCore Token Crashes As ZachXBT Warning Puts Insider Supply Back In Focus

MemeCore's M token experienced a severe crash, plunging over 70% in a single session. This sudden sell-off revived concerns highlighted by on-chain investigator ZachXBT regarding tokens with concentrated insider supply and thin liquidity. The incident underscores the fragility of many memecoins, where large paper valuations can vanish quickly when selling pressure hits illiquid markets. It also brings exchange listing standards into question, as listings can imply a legitimacy not supported by the underlying token structure. The crash serves as a stark warning for traders that social traction and high market capitalization do not guarantee resilient liquidity, emphasizing the risks inherent in high-beta crypto assets.

bitcoinist1 saat önce

MemeCore Token Crashes As ZachXBT Warning Puts Insider Supply Back In Focus

bitcoinist1 saat önce

GPT Designs GPT

OpenAI has unveiled its first custom AI chip, Jalapeño, a move signaling a strategic shift beyond being a mere model company. While many see it as a challenge to NVIDIA, its core aim is to control the entire intelligent production pipeline—from models and chips to data centers and energy. The key driver is the evolving competitive landscape: model advantages are shrinking, while the computational gap in areas like cost-per-token, system throughput, and energy efficiency is becoming the true long-term barrier. Jalapeño is primarily an inference chip, targeting the massive and growing "inference tax"—the daily operational cost of generating tokens for services like ChatGPT and APIs. By designing its own hardware optimized for its specific workloads and future product roadmaps (even using AI to aid the chip design process), OpenAI aims to drastically reduce token generation costs and improve system efficiency. This creates a potential flywheel: better models help design better chips, which lower costs for running next-generation models, supporting more users and products, which in turn provides more data to refine future chips. The strategy mirrors Apple’s integrated approach, building a closed loop where hardware, software, and applications are co-optimized. In the long term, OpenAI is not trying to become the next NVIDIA (a supplier of "shovels" to all AI companies) but to own and operate the entire "mine"—selling the end product of intelligence itself. This move marks OpenAI's ambition to evolve from creating the smartest models to controlling the foundational infrastructure of AI production.

marsbit1 saat önce

marsbit1 saat önce

Ethereum Foundation Interim Executive Director Speaks Out: What Is Our Mission?

The Ethereum Foundation's core mission is to ensure Ethereum remains a truly permissionless, censorship-resistant, private, and open infrastructure for large-scale, sovereign coordination. The article clarifies the EF's focus and dismisses irrelevant objectives, such as pursuing institutional popularity or short-term speculation. Its core work centers on eliminating systemic weaknesses. This involves fortifying Ethereum across multiple layers—protocol, access, user, and institutional—against exploitation, control, or surveillance. Key initiatives include minimizing harmful MEV and preventing privileged control over transaction flow, making unconditional privacy a foundational default, ensuring staking remains permissionless and decentralized, and strengthening user-facing access points to uphold autonomy. Concurrently, the EF aims to seize strategic opportunities. These include leading the transition to post-quantum cryptography, achieving a fully verifiable protocol stack, establishing Ethereum as private digital cash, integrating user-owned AI agents with personal wallets, and demonstrating that trusted-neutral infrastructure can competitively handle disintermediated coordination at an institutional scale. The article also addresses recent organizational changes, stating that personnel departures were due to strategic realignment, role fit, or natural evolution. It clarifies the approach to spin-outs, emphasizing that external funding will be provided only for work critical to the EF's mission that reduces Ethereum's dependency without creating new risks or mission drift. Ultimately, the EF is committed to building an enduring, neutral system that reshapes global coordination, focusing relentlessly on the principles of censorship resistance, openness, privacy, and sovereignty (CROP).

链捕手1 saat önce

Ethereum Foundation Interim Executive Director Speaks Out: What Is Our Mission?

链捕手1 saat önce

Switching Research Fields in the Final Year of My PhD, I Landed an Offer from OpenAI: My Interview Journey Was Full of 'Surprises'

Yong Zheng-Xin, a final-year PhD student at Brown University, secured an OpenAI offer after pivoting from multilingual models to AI safety research in his last year. In his blog post "Surprising lessons from my research scientist job search," he shares six unexpected insights from his job hunt. He found that often only one or two key papers matter for securing an interview, not the total number. Interview formats were highly diverse, including unexpected rounds on system design and AI agent usage. "Work trials," common in AI startups, involved paid, sometimes week-long collaborative tasks that disrupted other interview preparations. Timing proved crucial, with opportunity windows sometimes lasting less than a month. Return offers for research roles were rare compared to software engineering. Surprisingly, many interview rounds were unrelated to his specific AI safety focus, instead testing broader research competencies. Zheng-Xin concludes that the ability to demonstrate relevant expertise in a new field can unlock opportunities, regardless of one's previous publication record.

marsbit2 saat önce

Switching Research Fields in the Final Year of My PhD, I Landed an Offer from OpenAI: My Interview Journey Was Full of 'Surprises'

marsbit2 saat önce

İşlemler

Spot

Futures