NEAR goes live on Solana – Why $2 is possible ONLY IF…

ambcryptoPublished on 2025-12-19Last updated on 2025-12-19

Abstract

NEAR Protocol's price fell below the key $1.83 support level amid persistent selling pressure, signaling a bearish trend. However, a potential reversal is indicated as the Stochastic RSI shows an oversold bounce. A new catalyst emerged with NEAR launching on Solana, boosting trading volume by $17.6 million to $83.5 million and expanding liquidity. This cross-chain exposure may increase volatility, with a significant liquidity pool near $2 acting as a potential upside target. For a sustained reversal, bulls must reclaim $1.83; otherwise, the bearish trend may continue.

NEAR Protocol [NEAR] has recorded a week of persistent selling pressure.

On the price chart, the token’s bears managed to push its price below the key support at $1.83, at press time, a support level that had initiated multiple reversals on the token’s most recent consolidation phase.

The same level served as a resistance during the months of ranging, but once broken, the overall bias turned bearish. After that, NEAR has struggled to find its footing, with selling pressure dominant in the shorter-term market.

However, the token’s Stochastic RSI defied all odds and was just bouncing off from an oversold region — a reversal sign as bears are out of gas.

That’s not all, a new catalyst has now been introduced. NEAR has gone live on Solana [SOL] trading, and much volatility is expected from the development.

NEAR debuts on Solana!

NEAR’s debut on Solana expanded the token into a new market and boosted its liquidity. Cross‐chain exposure often drives price volatility, particularly when a cryptocurrency is positioned at a critical technical level.

Early market trends reflect this shift. NEAR’s trading volume surged by about $17.6 million in the past day, reaching $83.5 million at press time. Listing announcements typically spark such increases as more participants enter the market.

The Solana listing could provide the catalyst for a potential reversal, with added liquidity and volatility flowing into NEAR.

Is $2 a key upside magnet?

Liquidity data from CoinGlass also brings useful insight. The liquidation heat map indicates a significant liquidity pool of about $839K at around the $2 price level. Such pools often become short-term magnets for prices, especially as volatility increases.

The cluster presence affirms the price level as a key market target if purchase momentum persists among NEAR bulls.

What’s next for NEAR?

The overall trend remains weak. While increased liquidity and trading could spark a reversal, strong demand is needed to push NEAR above its previous resistance.

Only then can the recent bearish momentum be rejected. For now, bulls must reclaim the $1.83 level for a new trend to take shape.

Market participants are watching closely. It remains uncertain whether NEAR’s new exposure on the Solana chain will shift sentiment or simply prove to be a temporary pause before its downward trend resumes.


Final Thoughts

  • NEAR price slipped below $1.83 after repeated rejections during long-term consolidation.
  • Trading volume jumped sharply following confirmation of Solana trading support, as liquidity data points to $2 as a near-term magnet amid rising volatility.

Related Questions

QWhat key support level did NEAR's price fall below, and why is it significant?

ANEAR's price fell below the key support level of $1.83. This level is significant because it had initiated multiple price reversals during the token's recent consolidation phase and previously served as a resistance level.

QWhat new development is expected to bring volatility to NEAR's price?

ANEAR has gone live on Solana trading, which is a new development that expands the token into a new market, boosts its liquidity, and is expected to drive significant price volatility.

QHow did NEAR's trading volume change following its listing on Solana?

ANEAR's trading volume surged by approximately $17.6 million in the past day, reaching $83.5 million at the time of the report.

QAccording to liquidity data, what price level is acting as a key upside magnet for NEAR?

ALiquidity data from CoinGlass indicates a significant liquidity pool of about $839,000 is clustered around the $2 price level, making it a key short-term upside magnet for the price.

QWhat must happen for NEAR's recent bearish momentum to be rejected and a new trend to form?

AFor the recent bearish momentum to be rejected, strong demand is needed to push NEAR above its previous resistance. Specifically, bulls must first reclaim the $1.83 support level for a new trend to take shape.

Related Reads

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

The article "a16z: AI's 'Amnesia' – Can Continual Learning Cure It?" explores the limitations of current large language models (LLMs), which, like the protagonist in the film *Memento*, are trapped in a perpetual present—unable to form new memories after training. While methods like in-context learning (ICL), retrieval-augmented generation (RAG), and external scaffolding (e.g., chat history, prompts) provide temporary solutions, they fail to enable true internalization of new knowledge. The authors argue that compression—the core of learning during training—is halted at deployment, preventing models from generalizing, discovering novel solutions (e.g., mathematical proofs), or handling adversarial scenarios. The piece introduces *continual learning* as a critical research direction to address this, categorizing approaches into three paths: 1. **Context**: Scaling external memory via longer context windows, multi-agent systems, and smarter retrieval. 2. **Modules**: Using pluggable adapters or external memory layers for specialization without full retraining. 3. **Weights**: Enabling parameter updates through sparse training, test-time training, meta-learning, distillation, and reinforcement learning from feedback. Challenges include catastrophic forgetting, safety risks, and auditability, but overcoming these could unlock models that learn iteratively from experience. The conclusion emphasizes that while context-based methods are effective, true breakthroughs require models to compress new information into weights post-deployment, moving from mere retrieval to genuine learning.

marsbit2h ago

a16z: AI's 'Amnesia', Can Continuous Learning Cure It?

marsbit2h ago

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

An individual manipulated a weather sensor at Paris Charles de Gaulle Airport with a portable heat source, causing a Polymarket weather market to settle at 22°C and earning $34,000. This incident highlights a fundamental issue in prediction markets: when a market aims to reflect reality, it also incentivizes participants to influence that reality. Prediction markets operate on two layers: platform rules (what outcome counts as a win) and data sources (what actually happened). While most focus on rules, the real vulnerability lies in the data source. If reality is recorded through a specific source, influencing that source directly affects market settlement. The article categorizes markets by their vulnerability: 1. **Single-point physical data sources** (e.g., weather stations): Easily manipulated through physical interference. 2. **Insider information markets** (e.g., MrBeast video details): Insiders like team members use non-public information to trade. Kalshi fined a剪辑师 $20,000 for insider trading. 3. **Actor-manipulated markets** (e.g., Andrew Tate’s tweet counts): The subject of the market can control the outcome. Evidence suggests Tate’sociated accounts coordinated to profit. 4. **Individual-action markets** (e.g., WNBA disruptions): A single person can execute an event to profit from their pre-placed bets. Kalshi and Polymarket handle these issues differently. Kalshi enforces strict KYC, publicly penalizes insider trading, and reports to regulators. Polymarket, with its anonymous wallet-based system, has historically been more permissive, arguing that insider information improves market accuracy. However, it cooperated with authorities in the "Van Dyke case," where a user traded on classified government information. The core paradox is reflexivity: prediction markets are designed to discover truth, but their financial incentives can distort reality. The more valuable a prediction becomes, the more likely participants are to influence the event itself. The market ceases to be a mirror of reality and instead shapes it.

marsbit3h ago

Can a Hair Dryer Earn $34,000? Deciphering the Reflexivity Paradox in Prediction Markets

marsbit3h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of SOL (SOL) are presented below.

活动图片