【重磅解读】12.1万枚BTC大额交易消失后,行情准备变盘

jinjin说币Publicado a 2022-11-25Actualizado a 2022-11-28

Resumen

BTC短线反弹强度较弱,关注调整表现。

1、BTC冲高回落

BTC短线反弹强度较小,冲高回落走势出现以后,价格仍然表现相对弱势。日K线图中,BTC在16812美元位置冲高回落,价格回撤意味着调整的到来。目前来看,没有迹象表明BTC已经摆脱下跌趋势,特别是11月8日和11月9日价格大幅度回撤以后,近期价格小幅反弹,或许只是短线回抽表现,下跌趋势仍然可能出现加速表现。

2、BTC大额交易量回落

目前BTC链上大额交易量已经在低位运行,并且持续了多个交易日。从11月12日开始,BTC链上大额交易笔数已经在158笔以下运行。这说明,资金主力链上交易笔数从400笔附近下降到了158笔以下。数量上,从每日20万枚BTC下降到了每日7.9万枚BTC,每日大额交易数减少了12.1万枚BTC。据此判断,近期BTC价格走势仍然可能在低迷中前行,价格在震荡中下跌的可能性较大。

3、ETH走势低迷

随着ETH价格小幅反弹以后回调,目前没有迹象表明价格可能强劲回升。因此,低迷行情中注意抛压变化。目前来看,斐波那契78.6%对应的1106美元支撑仍然有效,目前需要注意ETH价格回调的力度。本次价格回调力度不能太大,一旦轻松跌破1106美元,那么还是会大幅度看跌。对待调整行情里的ETH价格,目前已谨慎为主。

4、LTC已经在前期高位以上

从日K线图来看,LTC不仅在短线走强,而且已经在收盘点位上突破前期5月份价格平台高位对应的73.2美元。因此,LTC近期价格上涨走势相对成功,对于趋势的判断可以更为乐观。但是短线BTC、ETH等主流币表现不佳,因此LTC进一步上涨或许需要整体市场走强来配合才行。成交量方面显示,LTC仍然维持高交易量,对价格上涨有利。

5、LTC低位反弹预期较好

从LTC长期的量价表现看,越是到价格低位,其成交量萎缩迹象越明显。这说明,LTC仍然处在低迷期间的放量阶段。因此,对涨幅潜力的判断仍然较大。前期早在2021年的成交量较高,更多投资者成本价在150美元以上。因此,LTC的吸引力仍然较高。

Lecturas Relacionadas

Beyond the Model Lies the Harness: Deepseek Enters the Arena, Why Has the Main Battlefield of China's AI Competition Shifted?

In mid-to-late May 2026, Deepseek internally established a new Harness team focused on code agent products, internally benchmarked against Anthropic's Claude Code. This move, marked by the formula "Model + Harness = Agent" in their job postings, signals a major shift in China's AI competition: the main battlefield is transitioning from developing large models to building toolchains and achieving workplace integration. Deepseek's direct involvement in Harness development aims to secure control over interface design and training data feedback loops, moving beyond open-sourcing powerful models. Harness, the runtime infrastructure for AI agents, handles everything beyond model reasoning—task orchestration, tool calling, context management, safety checks, and error recovery. It is crucial because agent products are not just outputs of model capability but also training grounds for it. Real-world task failures recorded by Harness can feed back into model training, creating a flywheel effect. Engineering Harness is more critical than optimizing prompts, as poor context management or error handling can drastically reduce agent success rates in multi-step, real-world scenarios. This shift is not isolated. Other major Chinese tech companies are also pursuing differentiated toolchain strategies. Tencent leverages its enterprise ecosystem (WeChat Work, Tencent Cloud) to build connectors for organizational-level AI collaboration and complex task delivery. Alibaba focuses on lowering automation barriers on the web with a front-end, browser-based GUI Agent framework, PageAgent. This diversification shows the industry recognizes that success lies not in a perfect general agent, but in vertically focused solutions built with robust engineering. The trend is validated by overseas success, such as Poland's Viktor, an AI coworker on Slack achieving $20M ARR by autonomously executing complex, multi-step tasks. This proves a shift in enterprise willingness to pay—from "AI-assisted generation" to "AI-autonomous execution." As Harness matures to provide safety guards and reliability, AI transitions from a human-supervised intern to an independent outsourcer. The competition now faces key engineering challenges: preventing "token explosion" through intelligent context compression, and building "thick frameworks" with features like sandbox isolation and checkpoint recovery for enterprise-grade stability. Geopolitical restrictions on tools like Claude Code further create a significant market vacuum for domestic solutions like Deepseek's Harness. For enterprises and developers, the focus must shift from comparing model benchmarks to evaluating a vendor's engineering capabilities, error recovery mechanisms, context management, and ecosystem compatibility when choosing AI products and platforms.

marsbitHace 50 min(s)

Beyond the Model Lies the Harness: Deepseek Enters the Arena, Why Has the Main Battlefield of China's AI Competition Shifted?

marsbitHace 50 min(s)

Soaring Export Data for Memory Chips, Market Is Redefining the Valuation Anchor for Memory Stocks

Korean storage export data for the first 20 days of June shows substantial year-on-year increases in both value and price-per-kilogram for categories like DRAM, NAND, and SSDs. This signals a potential shift beyond simple demand recovery, indicating rising prices and a product mix shift towards higher-value items, possibly influenced by AI infrastructure needs. A key point is that the surge in price-per-kilogram is not simply a uniform chip price hike. It reflects a combination of actual price increases and, more importantly, an export structure increasingly dominated by high-value-density products like HBM (High-Bandwidth Memory) and advanced DRAM, which are critical for AI servers. This suggests AI-driven demand may be spilling over from just HBM into broader memory markets. SK Hynix stands to benefit directly due to its leading HBM position. For Samsung and Micron, the implication is potential for greater margin elasticity if the tightness in high-end memory spreads to enterprise SSD and NAND prices. However, the storage sector remains cyclical. Risks include supply expansion, inventory changes, and potential slowdowns in broader AI capital expenditure. Ultimately, while the strong export data supports upward revisions for storage company earnings and fuels discussion of an "AI infrastructure bottleneck premium," a definitive valuation shift from a cyclical to a structural story depends on upcoming quarterly reports. Investors need confirmation from SK Hynix, Samsung, and Micron that improvements in average selling prices, product mix, and, crucially,毛利率 are sustained over multiple quarters.

marsbitHace 2 hora(s)

Soaring Export Data for Memory Chips, Market Is Redefining the Valuation Anchor for Memory Stocks

marsbitHace 2 hora(s)

Trading

Spot
Futuros
活动图片