莱特币:决定LTC短期命运的两个关键因素

币界网Pubblicato 2024-08-22Pubblicato ultima volta 2024-08-22

币界网报道:
    如果山寨币季开始,机构买家增加购买量,莱特币可能会突破。莱特币的哈希率飙升至历史新高,但短期价格会得到一些看涨的缓解吗?

莱特币(LTC)可能还没有像其较大的兄弟公司那样拥有ETF,但这并没有阻碍机构需求。

Grayscale是加密货币领域最值得注意的机构投资者之一,一直在其投资组合中增加更多的LTC。

最近的数据显示,Grayscale一直在积累莱特币,而不管市场如何。在8月初的崩盘期间,机构投资者没有削减其持有量。

相反,它保持了正平衡,在过去四周从175万LTC增长到185万LTC。这是Grayscale有史以来持有的最高数量的莱特币。

Grayscale的Litecoin袋占LTC当前供应量的0.024%。虽然这可能不多,但它突显了一个重要的观察结果,即鲸鱼和机构投资者仍然对此感兴趣。

就在一个月前,莱特币网络证实,管理着超过12万亿美元资产的投资公司富达开始向其客户提供长期资本敞口。

这些发展可能会吸引零售商的更多兴趣。

莱特币哈希率飙升至新的ATH

莱特币在其他关键领域也在增长。最引人注目的是其哈希率,多年来一直在稳步增长。哈希率在过去24小时内达到1.29 PH/S的历史新高。

在从长期阻力位回落后,莱特币在过去五天一直看跌。然而,它可能正在为看涨的缓解做准备。

其1小时图最近与RSI形成了看涨背离模式。

看涨的背离表明LTC可能会转向上行。这一结果可能会导致对66美元价格区间下跌的再次测试。

截至发稿时,其交易价格为63.32美元,接近之前测试的支撑位。

缩小,特别是在1日图表上,显示突破的可能性很高。这是因为莱特币处于楔形模式,支撑和阻力将其挤压到突破或崩溃区域。


你的投资组合是绿色的吗?查看LTC利润计算器


由于机构需求正在积极积累,结果可能有利于看涨。然而,这些观察结果并不一定能保证这一结果。

市场最近表现出很多不可预测性,市场目前处于全球经济状况的边缘。这些因素可能会影响未来几个月的流动性流动。

Letture associate

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Auto Research Era: 47 Tasks Without Standard Answers Become the Must-Test Leaderboard for Agent Capabilities

The article introduces Frontier-Eng Bench, a new benchmark for AI agents developed by Einsia AI's Navers lab. Unlike traditional tests with clear answers, this benchmark presents 47 complex, real-world engineering tasks—such as optimizing underwater robot stability, battery fast-charging protocols, or quantum circuit noise control—where there is no single correct solution, only continuous optimization towards a limit. It shifts AI evaluation from static knowledge retrieval to a dynamic "engineering closed-loop": the AI must propose solutions, run simulations, interpret errors, adjust parameters, and re-run experiments to iteratively improve performance. This process tests an agent's ability to learn and evolve through long-term feedback, much like a human engineer tackling trade-offs between power, safety, and performance. Key findings from the benchmark reveal two patterns: 1) Improvements follow a power-law decay, becoming harder and smaller as optimization progresses, and 2) While exploring multiple solution paths (breadth) helps, sustained depth in a single path is crucial for breakthrough innovations. The research suggests this marks a step toward "Auto Research," where AI systems can autonomously conduct continuous, tireless optimization in scientific and engineering domains. Humans would set high-level goals, while AI agents handle the iterative experimentation and refinement. This could fundamentally change research and development workflows.

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