Will MYX set new all-time lows after the $1.81 rejection? Data shows…

ambcryptoОпубліковано о 2026-02-23Востаннє оновлено о 2026-02-23

Анотація

A recent analysis of MYX Finance (MYX) highlights a bearish outlook following a failed attempt to sustain momentum. After a brief rally to $1.81, the token experienced significant buyer exhaustion, closing the same session at just $1.02. This rejection has led to sellers taking control, with the loss of the $1 support level indicating potential further declines. Technical analysis suggests that, without nearby long-term support, MYX could potentially fall as low as $0.15. In the short term, any bounce to the $0.80-$0.85 range is viewed as a selling opportunity. Both long and short-term expectations remain bearish.

In a recent AMBCrypto report, the down-only price action of MYX Finance [MYX] was highlighted. A short-term bullish divergence was noted, and a bounce to $1.5 was expected at that time.

MYX bulls were able to drive the bounce as high as $1.81. In doing so, a local bottom at $0.80 was formed. This level was retested as support once again in recent hours of trading.

AMBCrypto reported that $3 and $5 were the major longer-term swing resistances overhead. MYX bulls need to overturn these levels to establish an uptrend. As things stand, the altcoin looks more likely to set new lows than reclaim the overhead supply zones.

MYX buyer exhaustion explained

The 1-day timeframe’s price action illustrated the extremely tough job bulls have on their hands.

On Friday, the 20th of February, the rally rose as high as $1.816, but it lasted only a few hours. The daily session close was at $1.02, a far way from the highs.

It was classic buyer exhaustion.

An upward candle on high volume hunted down the imbalances and short liquidations overhead, as an earlier report warned it might. Short-term buyer enthusiasm and forced short liquidations can only keep the rally going for so long.

Since then, sellers have seized control emphatically.

In August 2025, MYX rallied swiftly from $0.15 to $2.5. Towards the end of that month, the price came back to the psychological $1 level to test it as support.

Therefore, now that this level was ceded to the bears, there was no long-term support nearby. It might seem dramatic to say that $0.15 was the next target, but technical analysis showed that this outcome is possible.

On the 1-hour chart, the imbalance between $0.75-$0.85 was a short-term target. A bounce to this area would likely present a selling opportunity. The OBV was making new lows and the MACD formed another bearish crossover.

Overall, the long and short-term expectations remained bearish for MYX.


Final Summary

  • The failure to rclaim $1 as support meant that MYX could fall as far south as $0.15.
  • In the short-term, a bounce to $0.80-$0.85 should be considered a selling opportunity.

Disclaimer: The information presented does not constitute financial, investment, trading, or other types of advice and is solely the writer’s opinion.

Пов'язані питання

QWhat was the expected bounce level for MYX Finance according to the previous AMBCrypto report?

AA bounce to $1.5 was expected.

QWhat was the major longer-term swing resistance level that MYX bulls need to overcome to establish an uptrend?

A$3 and $5 were the major longer-term swing resistances overhead.

QWhat does the article identify as the classic sign of buyer exhaustion on February 20th?

AThe price rallied to a high of $1.816 but the daily session closed far from the highs at $1.02, which was a classic sign of buyer exhaustion.

QAccording to the technical analysis, what is the potential long-term downside target for MYX after it lost the $1 support level?

AThe potential long-term downside target is $0.15.

QWhat short-term price area does the article suggest would present a selling opportunity for MYX?

AA bounce to the $0.80-$0.85 area would present a selling opportunity.

Пов'язані матеріали

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.

marsbit30 хв тому

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

marsbit30 хв тому

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.

marsbit1 год тому

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

marsbit1 год тому

Торгівля

Спот
Ф'ючерси
活动图片