[Key interpretation] $30 billion bet on strong shock in cryptocurrency market, ETH bears took the lead in betting down

HuobiPublished on 2022-10-11Last updated on 2022-10-12

Abstract

The BTC price is close to the support level, paying attention to the variable expectations.

1. BTC local turnover rate increased

BTC's 4-hour K chart shows that although the recent price decline is limited, the trading volume has obviously rebounded. That is to say, BTC has a clear volume signal in the local price range, driving the price to lower prices. This shows that investors began to gradually increase the trading frequency at the decline stage, but did not obtain the corresponding low suction returns. Therefore, short-term investors can be the driving factor for BTC to decline.

2. BTC whale selling remains high

Recently, the proportion of BTC's whale sales has remained at a high level, and the value in the shock remained around 0.48, which means that BTC still faces great downward pressure. Although BTC's recent decline is limited, it is already the seventh trading day in the adjustment stage. At the same time, BTC dropped to about 19000 US dollars, and the rebound has not been confirmed. At present, the possibility of effective support under BTC is reduced, and attention is paid to the risk of sign retreat.

3. The trend of ETH is sluggish, seeking for further exploration

In terms of the daily K line, the ETH price continued to fall, and the closing price did not drop much, but the trend of downturn continued. At present, ETH maintains horizontal operation at the platform of 1280 dollars, and the horizontal trading time has reached 20 trading days, indicating that ETH still tends to shrink. In terms of trading volume, the current low trading volume suggests that investors are less enthusiastic about entering the market, and they still need to pay attention to the downside risk when holding currencies.

4. ETH multi empty ratio is still lower than 1

The strength of the long contract relative to the short contract is still low. After the long short ratio reached 0.897 on October 8, the value is still lower than 1. From October 9 to October 11, the long short ratio of ETH was 1.001, 0.951 and 0.932. According to this judgment, the strength of short sellers has obviously occupied an advantage, making it possible for ETH to break its position and fall for some time. Before ETH continues to fall below $1200, we have to pay attention to the position risk.

5. The overall market position rebounded

Recently, although the market is in the adjustment stage, the position data has not dropped significantly, but maintained at a short-term high. Numerically, the overall position data displayed on October 11 remained at $29.98 billion, the highest level since May. According to this judgment, although BTC, ETH and other mainstream currencies are in a weak operation stage, the market is on the eve of change. Therefore, in view of the low volatility of BTC and ETH at present, it may already be an important position for position adjustment to anticipate price fluctuation.

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

The article "Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?" argues that Ethereum, like Linux before it, will triumph over closed, proprietary systems in finance due to its open, permissionless, and credibly neutral nature. It draws a historical parallel: just as the open internet defeated corporate private networks and Linux outcompeted proprietary Unix systems, open financial infrastructure like Ethereum will surpass private blockchains. The core advantage lies in the "bazaar" development model (as described in Eric Raymond's "The Cathedral and the Bazaar"), where decentralized, permissionless innovation by a global community of developers outpaces the controlled "cathedral" approach of centralized entities. This model fosters rapid innovation, as seen with Ethereum standards like ERC-20 and applications like Uniswap, which were built without needing permission. Ethereum's key, irreplicable strength is its credible neutrality: transparent, equally applicable, immutable rules that allow anyone to participate. This ensures sovereign independence, meaning no single entity (company, government) can control or change its core rules—a critical feature for global financial infrastructure. In contrast, private blockchains and consortium chains (like SWIFT or various bank-led projects) suffer from platform risk, central control, and an inability to attract broad developer ecosystems, leading to frequent failures. The article notes that major institutions (e.g., BlackRock, JPMorgan, Coinbase, Robinhood) are already building on Ethereum or its Layer 2 networks, recognizing its security, developer ecosystem, and network effects. While critics argue finance requires accountable, controlled systems, the response is that compliance (KYC, regulations) can be built at the application layer on top of a neutral settlement layer like Ethereum, just as secure commerce was built on the open internet via HTTPS. Ultimately, the thesis is that attempting to build walled-garden, proprietary financial networks is a flawed strategy that stifles innovation. The winning approach is to build applications on top of open, credibly neutral infrastructure like Ethereum, which is poised to become the foundational settlement layer for global finance.

Foresight News4m ago

As Channel Dividends Fade, What Can DeFi Protocols Rely On to Resist Giant Harvesting?

The article discusses how major tech companies like Coinbase, Stripe, and Kraken are increasingly acquiring or building their own underlying infrastructure to capture profits, moving away from relying on external DeFi protocols. It uses the example of Coinbase's Base blockchain, which generates sequencing fees for Coinbase from protocols like Morpho that deploy on it. Similarly, Stripe acquired Bridge to capture stablecoin yield instead of paying it to Circle, and Kraken bought NinjaTrader for its derivatives licenses. The core argument is that when channel-based advantages (like user traffic) fade, control over the profit-generating infrastructure becomes critical. While this could lead to a future dominated by a few integrated giants, the article suggests a more balanced outcome is possible. Protocols that have already achieved deep, multi-chain integration and embedded themselves into corporate backends create high switching costs, making them difficult to replace. Examples include Morpho (despite reliance on Base, it's widely deployed) and Uniswap. The piece concludes that the race between institutional expansion and open-source protocol proliferation will ultimately shape the industry's structure.

marsbit10m ago

As Channel Dividends Fade, What Can DeFi Protocols Rely On to Resist Giant Harvesting?

marsbit10m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbit11m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit11m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbit15m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbit15m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbit16m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

marsbit16m ago

Trading

Spot

Futures