Pump.fun: Can $1.8mln whale buying help PUMP target $0.0022?

ambcryptoPublicado em 2026-03-02Última atualização em 2026-03-02

Resumo

PUMP rebounded from $0.0016 to a local high near $0.0019, but remained below the $0.002 threshold. A newly created whale wallet accumulated 947.31 million PUMP tokens worth $1.86 million over two days, signaling confidence. Top addresses added 4.3 billion tokens in 24 hours, and the team spent $1.2 million on buybacks. However, the market faces significant selling pressure, with a negative Buy Sell Delta of -500 million and the Stochastic Momentum Index in the negative zone. If whale demand helps PUMP above $0.0019, it could target $0.0022; otherwise, it risks falling back to $0.0016.

Since reaching $0.0016, Pump.fun has shown upward momentum, reaching a local high of $0.0019. In fact, the altcoin, as of this writing, Pump.fun [PUMP] traded at $0.001906, down 3.02% on the daily charts.

Pump.fun whale scoops up 947 million PUMP tokens

With PUMP holding below $0.002, it has created a perfect buying opportunity for large entities, especially whales.

According to Lookonchain, a newly created wallet purchased 947.31 million PUMP worth $1.86 million. This whale has been on a buying spree for the past two days.

Usually, when a whale enters the market to accumulate during a period of weakness, it shows confidence, anticipating market recovery.

Significantly, this whale purchase is not an isolated case, as large holders have been aggressively accumulating. According to Nansen data, Top Addresses added 4.3 billion PUMP tokens over the past 24 hours.

Historically, increased whale accumulation has strengthened an asset’s upside momentum, leading to higher prices. Thus, if Pump.fun whales continue buying, the demand-side liquidity could help PUMP break out of the current bearish trap.

Coupled with that, the team has continued to pivot the market, absorbing the market pressure through buybacks. Over the past 24 hours, Pump.fun spent 99% of the daily revenue, or $1.2 million, and purchased 15.011 PUMP tokens.

The team’s continued buybacks show its commitment to the asset, which helps boost demand-side pressure, a key driver of price recovery.

Downside risk still remains elevated

While whales continue to accumulate, other market participants have shown a tendency to cash out even slight gains. On March 2, the altcoin saw 1.7 billion in Spot Sell Volume compared to 1.2 billion in Buy Volume.

This imbalance left the market with a negative Buy Sell Delta of -500 million, a clear sign of aggressive Spot selling. Often, increased selling pressure from the spot market accelerates downside momentum.

In fact, the altcoin’s downside momentum continues to hold the market strongly, as evidenced by the Stochastic Momentum Index (SMI). Looking at the indicator, it still sits within the negative zone, suggesting that sellers have total control of the market.

As such, while buyers, especially whales, have attempted to absorb pressure, their demand remains insignificant to lift PUMP from the prevailing trend.

Thus, the continuity of the prevailing sentiment could see PUMP drop towards $0.0016 again. However, if the whale demand helps the altcoin close above $0.0019, it will be strong enough to target $0.0022.

Final Summary

PUMP rebounded from $0.0016 to a local high near $0.0019, but remained below the $0.002 threshold.
A newly created wallet accumulated 947.31 million PUMP worth $1.86 million over two days.

Perguntas relacionadas

QWhat was the price of Pump.fun (PUMP) at the time of writing, and what was its daily change?

AAt the time of writing, Pump.fun (PUMP) traded at $0.001906, down 3.02% on the daily charts.

QHow many PUMP tokens did a whale purchase, and what was the total value of that transaction?

AA whale purchased 947.31 million PUMP tokens, worth a total of $1.86 million.

QAccording to Nansen data, how many PUMP tokens did Top Addresses add in the 24-hour period mentioned?

AAccording to Nansen data, Top Addresses added 4.3 billion PUMP tokens over the past 24 hours.

QWhat was the Buy Sell Delta for PUMP on March 2nd, and what does this indicate about market activity?

AOn March 2nd, the Buy Sell Delta for PUMP was -500 million, indicating aggressive spot selling pressure as sell volume (1.7B) exceeded buy volume (1.2B).

QWhat are the two potential price targets for PUMP mentioned in the article, and what condition is required to reach the higher target?

AThe two potential price targets are a drop to $0.0016 or a rise to $0.0022. To target $0.0022, the altcoin needs to close above $0.0019 with the help of whale demand.

Leituras Relacionadas

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

The article analyzes the possibility of TradeXYZ, a dominant project on Hyperliquid's HIP-3 real-world asset (RWA) perpetual contracts market, splitting off to build its own independent trading platform. With TradeXYZ commanding over 90% of HIP-3 volume and over 35% of Hyperliquid's total open interest, its leverage and influence are significant. The primary motive for a split is seen as capturing the underlying protocol fees, of which TradeXYZ currently receives only half. However, several factors strongly discourage such a move. First, replicating Hyperliquid's high-performance infrastructure would be challenging and could degrade TradeXYZ's product quality. Second, Hyperliquid serves as a crucial distribution channel, with most users accessing TradeXYZ's liquidity through its frontend. Third, the close, trusting relationship between the founders of both projects makes a hostile "betrayal" unlikely. The conclusion is that a split would likely result in a lose-lose scenario. Hyperliquid would lose its main growth narrative and see its valuation impacted, while TradeXYZ would face immense technical hurdles, lose its primary user channel, and damage its reputation. Instead of a full separation, the more probable path is for TradeXYZ to negotiate better terms while deepening integration, focusing on enhancing its own brand and user ownership.

marsbitHá 30m

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

marsbitHá 30m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

Anthropomorphic "Teletubby" robot offers cleaning services in San Francisco at $30/hour, but it's entirely remote-controlled. The robot, created by startup Tau Robotics, can perform household tasks like washing hands, mopping floors, and taking out trash. While the initial demo videos appear impressive and are notably shown at normal speed (unlike many sped-up robot demos), the company reveals the actions are performed via human teleoperation, not autonomous AI. Tau Robotics, founded in 2024, argues this "cheat" is a strategic way to bridge the current capability gap, ensure task completion, and collect real-world home data to eventually train autonomous systems. Their service features three robot models: Chelsea for kitchens/bathrooms, Elon for regular tidying with memory, and Tony for deep cleaning. Priced at $30 per hour, it's cheaper than average human cleaners in the US. The article discusses the broader challenge of deploying humanoid robots in homes, comparing Tau's approach to others like China's Ziliang and the US's 1X Neo, which also use teleoperation. A key reason for choosing a humanoid form is to make remote control more intuitive for human operators. The piece also notes the potential "emotional value" of having a humanoid servant. The service is currently invite-only in San Francisco.

marsbitHá 44m

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

marsbitHá 44m

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h

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

marsbitHá 1h

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.

marsbitHá 1h