币圈丽盈:11.23索拉纳(SOL)短期均线走平却难掩长期均线深渊 最新行情分析及操作建议

金色财经Published on 2025-11-23Last updated on 2025-11-23

币圈丽盈:索拉纳(SOL)最新行情分析

文章发布时间2025.11.23—00点:30分

    索拉拉目前价格为127.1,丽盈判断目前索拉纳SOL当前市场处于明显的下跌趋势中,技术面显示价格位于均线系统下方,形成空头排列,短期均线虽有走平迹象,但长期均线依然向下整体趋势仍偏空头主导。最近在127附近形成整理形态整体下跌后的低位盘整。MACD空头力量减弱但尚未转强。EMA趋势偏弱,短期内行情走平周末意味着没有太大的波动出现,做超短波段可以尝试,

 

今日最新点位参考

做多点125,补123,止122,目标130

做空点130,补132,止133,目标125

 

  以上分析丽盈基于市场数据和盘口的趋势分析得出的结论,并不构成投资建议。供家人们参考。期望能助力其他怀揣梦想的人在这个波谲云诡的市场中找准自己的位置,开启属于自己的成功之旅。

  

  文章内容具有实时性,仅供参考,风险自担

 

 

Trending Cryptos

Related Reads

Ethereum's Next Decade in the Eyes of Vitalik

"Lean Ethereum" Long-Term Roadmap Unveiled by Vitalik Buterin On July 5, 2026, Vitalik Buterin published the "Lean Ethereum" roadmap, positioning it as Ethereum's third major evolution following the Merge. This multi-year, multi-phase upgrade aims to fundamentally transform Ethereum's core protocol through staged network upgrades extending to 2029. Key goals include achieving 1 gigagas per second L1 throughput (a massive increase from the current ~32 TPS), near-instant finality, and quantum-resistant cryptography. The plan involves transitioning Ethereum's security model from full transaction re-execution by all nodes to native verification via recursive STARK proofs. A major proposed change is replacing the EVM with a proof-friendly architecture like RISC-V or leanISA, though this remains a point of contention, especially with L2s like Arbitrum favoring alternatives like WASM. Other planned upgrades include a restructured state model with a large, cheap "warehouse" storage layer to drastically reduce fees for migrated applications, multi-dimensional gas pricing, and a new focus on making privacy a first-class, native protocol feature. While the roadmap significantly raises Ethereum's long-term technical ceiling, analysts note it does not directly address ETH's mid-term token economics or value capture. The plan's multi-year timeline means near-term price impact will likely depend on observable progress milestones, such as the successful deployment of the upcoming Glamsterdam gas limit increase, growth in L2 activity and blob usage, and trends in L1 fee revenue and ETH burn.

链捕手38m ago

Ethereum's Next Decade in the Eyes of Vitalik

链捕手38m ago

In Just 11 Days, Claude Rewrote Millions of Lines of Code, an Epic AI Engineering Feat Sparks Fury

In just 11 days, Bun's founder Jarred Sumner used Anthropic's Claude AI models to rewrite its million lines of code from Zig to Rust. This move sparked significant controversy, particularly from Zig's creator, Andrew Kelley, who publicly criticized Sumner's engineering practices and the decision to use AI for such a massive rewrite. Bun, a high-performance JavaScript/TypeScript runtime and rival to Node.js, was originally written in Zig. After Anthropic acquired Bun, the team encountered persistent stability and memory safety bugs in the Zig codebase. These issues, combined with Zig's strict policy against LLM-generated code, led to the decision to rewrite in Rust. The rewrite was executed using Claude AI tools at an estimated API cost of $165,000, dramatically reducing the expected time and financial cost. Andrew Kelley's response was scathing. He blamed the original bugs on poor engineering habits, calling Bun's Zig code a collection of "hacks on top of hacks." He expressed relief that Bun was no longer associated with Zig, fearing it would misrepresent the language and attract low-quality, AI-generated contributions. The tech community is divided; some view Kelley's critique as unprofessional, while others see it as a defense of engineering integrity. A major concern about the AI-driven rewrite is the resulting code quality. The translation from Zig left approximately 27,000 lines of unsafe Rust code, raising fears about long-term maintainability and technical debt. The debate centers on whether this project is a milestone in AI-assisted development or a future maintenance nightmare.

marsbit1h ago

In Just 11 Days, Claude Rewrote Millions of Lines of Code, an Epic AI Engineering Feat Sparks Fury

marsbit1h ago

From Auto Finance to Bitcoin to AI Engines: An Analysis of Cango's 'What Not to Do' Strategy

From Auto Finance to Bitcoin and Now AI: Cango's "What Not to Do" Strategy Cango, a Chinese auto finance platform that went public on the NYSE in 2018, is undergoing its third major transformation. After selling its entire auto business in 2024, it pivoted to become a large-scale Bitcoin miner, acquiring 50 exahash of mining rigs from Bitmain. However, its true goal was never Bitcoin, but owning and controlling energy infrastructure. Now, Cango is pivoting again. While most listed Bitcoin miners are leasing power to giant hyperscalers for AI training clusters, Cango is taking the opposite path. It has launched an AI inference subsidiary called EcoHash, focusing not on training but on distributed inference. The company's strategy hinges on the insight that over 70% of mining industry power is controlled by small, independent sites (10-50 MW), which are too small for hyperscalers but ideal for low-latency AI inference. Cango aims to partner with these small operators, providing the AI technology, customers, and financing through its EcoLink software layer, which can distribute workloads across sites for reliability. Cango maintains a hybrid model, running roughly 31.7 EH/s of Bitcoin mining for cash flow while aggressively cleaning its balance sheet—slashing long-term debt by 94.5% to $30.6 million and raising $75 million for its AI venture. Its first AI deployment will be at a 50 MW site in Georgia. The strategy faces skepticism, given the high costs of converting mining sites and the potential for an AI bubble. However, Cango's leadership believes discipline around "what not to do"—avoiding direct competition with hyperscalers in training—positions it to capture the long-tail demand for distributed AI inference power.

Foresight News1h ago

From Auto Finance to Bitcoin to AI Engines: An Analysis of Cango's 'What Not to Do' Strategy

Foresight News1h ago

Strategy's Bitcoin Sales Cap Far Exceeds $1.25 Billion: A Detail the Market Overlooked

The article discusses how MicroStrategy's potential Bitcoin sales go far beyond the announced $1.25 billion "reserve-building capacity." It clarifies a key distinction in the company's "BTC Monetization Program": selling Bitcoin to *build* a new dollar reserve (the $1.25B cap) versus selling to *replenish* the existing USD Reserve after it's used for expenses like preferred share dividends. The recent $216M BTC sale for dividend payments was a "replenishment," leaving the headline $1.25B building quota untouched. The plan actually outlines three potential funding pools from BTC sales: 1) Building the reserve ($1.25B cap), 2) Covering preferred share/ debt costs (no specified cap), and 3) Funding buyback programs (up to $20B). This means the structured sales potential exceeds $30 billion, not including uncapped replenishment sales. The piece argues this marks MicroStrategy's shift from a passive "buy-and-hold" Bitcoin proxy to an actively managed entity using BTC as a balance-sheet tool to manage its complex capital structure (common stock, preferred shares, debt, reserve). This creates new dynamics and potential conflicts, as actions benefiting one part (e.g., selling BTC to pay dividends) may pressure another (e.g., undermining the "never sell" narrative). Investors must now parse the company's specific terminology ("build" vs. "replenish") to understand the true scope of future BTC sales, which is significantly larger than the market initially perceived.

marsbit2h ago

Strategy's Bitcoin Sales Cap Far Exceeds $1.25 Billion: A Detail the Market Overlooked

marsbit2h ago

Goldman Sachs Report Deconstructs the Competitive Landscape of China's AI Large Models: Who Will Be the Long-Term Winner?

Goldman Sachs analyzes China's AI large language model (LLM) landscape, identifying key players and a strategic shift towards efficiency and global expansion. The report highlights that Chinese open-source/open-weight models are closing the performance gap with top global proprietary models at significantly lower cost, driven by architectural innovations like MoE. This enables a "two-tier" market: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with pricing at ~$1 per million tokens, and a low-end, price-sensitive global segment. Open-source strategies aid adoption but limit monetization, as deployments via third-party platforms (e.g., AWS Bedrock, Alibaba Cloud) may not generate direct revenue for model creators. The industry is thus moving towards "open-weight + community license" models with revenue-sharing to improve unit economics. Internationally, the focus is shifting from "token maximization" to ROI-driven enterprise adoption, particularly in non-U.S. markets. Major cloud platforms are integrating Chinese models (e.g., DeepSeek, MiniMax). Using a competitive framework based on pricing power, cost advantage, and financial strength, Goldman Sachs identifies **Zhipu AI** and **DeepSeek** as leaders in foundational text models, and **ByteDance** (with Seedance) leading in multimodal/video generation. **MiniMax** and **Kuaishou** are also rated favorably. The firm forecasts China's AI model API/subscription revenue growing from ~RMB 35bn (2026E) to RMB 879bn by 2030.

marsbit2h ago

Goldman Sachs Report Deconstructs the Competitive Landscape of China's AI Large Models: Who Will Be the Long-Term Winner?

marsbit2h ago

Trading

Spot

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