Bitcoin Sentiment Drops To Pre-Rally Levels As Traders Turn Bearish Post-ATH

bitcoinistОпубліковано о 2025-03-23Востаннє оновлено о 2025-03-23

Анотація

Bitcoin and US equities are facing mounting pressure as macroeconomic uncertainty and erratic policy decisions from US President Donald Trump...

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Bitcoin and US equities are facing mounting pressure as macroeconomic uncertainty and erratic policy decisions from US President Donald Trump continue to shake investor confidence. With unexpected tariff announcements and unstable foreign policy stances dominating headlines, markets have become increasingly volatile. Bitcoin, often seen as a hedge against traditional market instability, has entered a consolidation phase around the $85,000 level. After weeks of sharp price swings, BTC appears to be gathering momentum for its next major move—up or down.

Despite hopes for a strong recovery following its all-time high earlier this year, sentiment across the crypto space has grown increasingly bearish. According to new data from CryptoQuant, investor and trader outlook on Bitcoin has shifted significantly. The Bitcoin Sentiment Vote – Up or Down chart reveals a clear transition toward negative sentiment, with a majority now betting against further short-term gains. This trend mirrors conditions last seen in September 2024, just before the market’s last major rally.

With sentiment turning sour and price action narrowing, Bitcoin’s current position at $85K has become a battleground for bulls and bears. Whether this period of indecision resolves in a breakout or breakdown may depend heavily on broader economic developments and investor reaction to continued political instability.

Investor Sentiment Hits 6-Month Low As Bitcoin Stalls Below $90K

Investors face a crucial moment as Bitcoin trades in a tight range, struggling to reclaim key resistance levels while holding above critical support. Despite attempts to initiate a recovery, bulls have been unable to generate enough momentum to push prices meaningfully higher, while bears have failed to force a decisive breakdown. This ongoing stalemate has heightened market tension.

The failure to reclaim the $90K level and hold above $85K consistently has led some analysts to question whether the current cycle is still intact. The pressure on bulls to prove the continuation of the bull run is mounting, as sentiment begins to shift toward a more cautious—or even bearish—outlook.

Top analyst Axel Adler shared insights on X that paint a sobering picture. According to Adler, after Bitcoin reached its ATH, sentiment took a sharp turn for the worse. This shift is clearly illustrated in the Bitcoin Sentiment Vote – Up or Down chart. The current quarterly sentiment ratio has dropped to levels not seen since September 2024, just before the market’s last major rally.

Bitcoin Sentiment Vote Indicator | Source: Axel Adler on X
Bitcoin Sentiment Vote Indicator | Source: Axel Adler on X

While it’s possible that this bearish sentiment could serve as a contrarian indicator—signaling a bottom—many believe it reflects deeper uncertainty. With macroeconomic instability and geopolitical concerns on the rise, Bitcoin’s next move will be crucial in determining whether the broader market sees a renewed uptrend or enters a prolonged bearish phase. As traders watch the $85K–$90K zone closely, the coming days may be decisive for BTC’s trajectory in 2024.

Bulls Face Growing Pressure

Bitcoin is currently trading at $84,200, holding just below the critical $85,000 level where both the 200-day moving average (MA) and exponential moving average (EMA) converge. This area has become a significant resistance zone, and bulls have struggled to push past it. To initiate a strong recovery rally, BTC must break above the $88,000 level—this would confirm momentum and could trigger a swift move back toward the psychological $90,000 mark.

BTC trading below the 200-day MA & EMA | Source: BTCUSDT chart on TradingView
BTC trading below the 200-day MA & EMA | Source: BTCUSDT chart on TradingView

For now, price action remains range-bound and uncertain, with bearish sentiment still weighing on the market. While BTC has managed to hold above short-term support at $82,000, the inability to reclaim the 200-day MA/EMA cluster raises concerns about further downside pressure.

If bulls fail to defend current demand and the price drops below $82,000, a retest of the $81,000 level is likely. Losing that support could open the door for a deeper correction toward the $78,000–$75,000 range. This scenario would further shake investor confidence and reinforce the growing narrative that the market is transitioning into a longer consolidation or bearish phase.

The coming days are critical, and all eyes remain on BTC’s ability to flip $85K into support and target higher resistance zones.

Featured image from Dall-E, chart from TradingView 

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Sebastian's journey into the world of crypto began four years ago, driven by a fascination with the potential of blockchain technology to revolutionize financial systems. His initial exploration focused on understanding the intricacies of various crypto projects, particularly those focused on building innovative financial solutions. Through countless hours of research and learning, Sebastian developed a deep understanding of the underlying technologies, market dynamics, and potential applications of cryptocurrencies. As his knowledge grew, Sebastian felt compelled to share his insights with others. He began actively contributing to online discussions on platforms like X and LinkedIn, focusing on fintech and crypto-related content. His goal was to expose valuable trends and insights to a wider audience, fostering a deeper understanding of the rapidly evolving crypto landscape. Sebastian's contributions quickly gained recognition, and he became a trusted voice in the online crypto community. To further enhance his expertise, Sebastian pursued a UC Berkeley Fintech: Frameworks, Applications, and Strategies certification. This rigorous program equipped him with valuable skills and knowledge regarding Financial Technology, bridging the gap between traditional finance (TradFi) and decentralized finance (DeFi). The certification deepened his understanding of the broader financial landscape and its intersection with blockchain technology. Sebastian's passion for finance and writing is evident in his work. He enjoys delving into financial research, analyzing market trends, and exploring the latest developments in the crypto space. In his spare time, Sebastian can often be found immersed in charts, studying 10-K forms, or engaging in thought-provoking discussions about the future of finance. Sebastian's journey as a crypto analyst and investor has been marked by a relentless pursuit of knowledge and a dedication to sharing his insights. His ability to navigate the complex world of crypto, combined with his passion for financial research and communication, makes him a valuable asset to the industry. As the crypto landscape continues to evolve, Sebastian remains at the forefront, providing valuable insights and contributing to the growth of this revolutionary technology.

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The Evolution Path of Physical Bitcoin

The Evolution of Physical Bitcoin Bitcoin's digital nature is its core strength, enabling self-custody and rapid global transfers. However, its intangibility also hinders mainstream adoption. For over a decade, creators have attempted to materialize Bitcoin while preserving its cash-like properties, yielding notable results. Casascius Coins, launched in 2011, were the first and most iconic physical Bitcoin. Creator Mike Caldwell generated private keys offline, printed them on coins, and sealed them with tamper-evident holograms. This model relied on user trust in the centralized issuer. Production ceased in 2013 due to regulatory pressure from FinCEN. RavenBit Coins emerged in 2014 aiming to decentralize minting by letting users generate and apply their own keys. However, this led to trust issues with numerous untrusted minters and insecure key generation methods. In 2016, Coinkite introduced Opendimes—a breakthrough in bearer asset technology. These USB-shaped devices generate and store keys internally. Funds can be received by checking the public key, but spending requires physically breaking the device to extract the private key. While innovative and open-source, its cost (~$20) and form factor limit its use for small, everyday transactions. Satochip's Satodime, a card-shaped device using similar secure chip technology, followed. It supports NFC interaction and comes in various forms. While potentially cheaper in bulk (~13€), it remains a high-security hardware wallet, not a low-cost cash substitute. A fundamental cost barrier exists. For physical Bitcoin to achieve widespread commercial use, hardware costs must drop below $1 to match the production cost of fiat banknotes. Current secure chips capable of running Bitcoin's cryptographic algorithms (like secp256k1) are too expensive. Chips like NXP's NTAG X DNA (~$3) show cost-reduction potential but lack native Bitcoin curve support. Projects like OfflineCash embed chips in banknote-like paper, but face challenges with durability, the need for custom Bitcoin-enabled chips, and the inherent requirement for users to verify balances online—which conflicts with Bitcoin's trustless ideal. Coinkite's Tapsigner, a ~$20 card with a proprietary Bitcoin NFC chip, is seen as a more practical step forward. It functions as a reloadable hardware wallet for contactless payments, solving the "change" problem and focusing on real-world retail integration, a direction also pursued by companies like Cash App and Square. In summary, the journey to physical Bitcoin has progressed from trusted centralized mints (Casascius) to user-generated keys (RavenBit) and finally to self-contained secure hardware (Opendimes, Satodime, Tapsigner). The core challenge remains developing a sufficiently low-cost, durable, and truly trustless physical bearer asset that can function like cash in daily transactions. Current solutions are either too expensive or introduce new trust assumptions, keeping the ideal of ubiquitous physical Bitcoin just out of reach for now.

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Samsung Relies on Technology Cycles, SK Hynix on HBM, How Did Micron Win a Trillion-Dollar Market Cap?

Micron Technology, the third-largest memory chip maker alongside Samsung and SK Hynix, recently saw its market cap surpass $1 trillion. Founded in 1978 in Boise, Idaho, Micron survived brutal industry cycles while American peers and Japan's memory sector faltered. Its survival is attributed to a dual strategy: leveraging political and legal avenues for critical breathing room, coupled with relentless manufacturing cost control. Historically, Micron sought U.S. government intervention three times. In 1985, it filed an anti-dumping complaint against Japanese firms, leading to the U.S.-Japan Semiconductor Agreement. Ironically, this created an opening for Samsung, which later became its toughest competitor. In 2002, Micron turned "whistleblower" in a DRAM price-fixing investigation, escaping penalties while rivals were fined. In 2017, it sued China's Fujian Jinhua, contributing to its placement on a U.S. entity list, stifling a nascent competitor. However, a major strategic misstep occurred in 2013 with the acquisition of bankrupt Japanese firm Elpida. Integrating Elpida's mobile-DRAM-focused technology diverted resources, causing Micron to miss the critical early decade of development for High Bandwidth Memory (HBM)—the high-performance memory essential for AI chips like NVIDIA GPUs. By the time AI demand exploded in 2022, SK Hynix, which launched the first HBM in 2013, held about 85% of the HBM3 market, leaving Micron with roughly 3%. Micron now faces a triple squeeze. In the high-end HBM market, it lags significantly behind SK Hynix and Samsung. In the mid-to-low end DRAM market, it faces aggressive price competition from China's CXMT. Furthermore, a 2023 Chinese cybersecurity ban on its products slashed its revenue from China, a once-core market, from over 10% to just 7.1% by FY2025, causing it to exit China's data center server business. Beneath its political maneuvering lies Micron's core strength: exceptional manufacturing efficiency and cost control. Decades of engineering have yielded DRAM chips with a smaller cell area than rivals, meaning more chips per wafer and lower unit costs. This efficiency, not subsidies, has allowed it to withstand price wars. While political leverage bought time, Micron is now paying a "time debt" in the HBM race. It is racing to ramp up HBM3E production and develop HBM4, but catching up to competitors who started a decade earlier is a monumental challenge. Its future hinges on whether its expertise in cost control and political strategy can compensate for the lost time in a technology race where early-mover advantage is decisive.

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New AMD Paper Overturns Conventional Wisdom: FP4 Training Instability's Cause Is Not Insufficient Randomness

AMD's new research challenges the conventional understanding of FP4 training instability. While reducing precision from FP8 to FP4 promises doubled computational throughput and is supported by new hardware like NVIDIA Blackwell and AMD MI350 series, training large language models natively with FP4 has been notoriously unstable, often attributed to insufficient stochasticity. The paper "Pretraining large language models with MXFP4 on Native FP4 Hardware" demonstrates successful end-to-end FP4 pre-training of Llama 3.1-8B on AMD MI355X GPUs using the MXFP4 format, achieving a 9-10% overall speedup over FP8. Crucially, it identifies the root cause of instability: not randomness, but the accumulation of *structural micro-scaling errors* along the sensitive weight gradient (Wgrad) path. Through controlled experiments, researchers found that quantizing the Wgrad operation to FP4 caused significant convergence degradation. Counterintuitively, common stochasticity-based mitigation techniques like stochastic rounding and randomized Hadamard transforms worsened performance. In contrast, applying a *deterministic* Hadamard transform successfully stabilized training by ensuring consistent error patterns, reducing the extra token cost from 26-27% to just 8-9%. This work has significant implications: 1) It provides a clear diagnostic for low-precision training instability, steering focus towards structural errors. 2) It pushes FP4 from a primarily inference-focused format into the realm of viable training. 3) It leverages the open OCP Microscaling (MX) standard, promoting cross-vendor compatibility. The research marks a critical step towards more economical large model training by further pushing the boundaries of low-precision computation.

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