Backward areas always play with what others have left behind

The textile industry emerged in Britain around the 18th century, triggering a global wave of technological revolution (industrial textile…

  • The textile industry emerged in Britain around the 18th century, triggering a global wave of technological revolution (industrial textile production), and driving a global wave of cotton cultivation;
  • In the southern United States, British India… cotton cultivation began on a large scale around the world; the raw materials were then transported to Britain for textile production, and the textiles were then sold globally;
  • During this process, southern American plantation owners made a fortune by using black slaves to grow cotton, creating numerous wealthy individuals around the world in the textile industry;
  • Subsequently, Britain began to phase out this low-end industry, from Britain to the United States, the United States to Japan, and then to other British colonies, and then to Shanghai, China. By the early 20th century, there was a gap of 200 years;
  • The last group is South Asia, Africa, and some of the most backward countries, which became the final destination for the transfer of this low-end industry at the end of the 20th century;
  • Now in the backward areas of South Asia, the price of textiles is cheaper than in other developed areas, with the price of a piece of clothing being only a few yuan.

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相关系数异常告警系统 - 滚动窗口Beta增强

一、核心概念 1.1 什么是滚动窗口Beta? 滚动窗口Beta(Rolling Window Beta) 是一种动态计算Beta系数的方法,通过在时间序列上滑动固定大小的窗口,计算每个时间点的Beta值,从而捕捉Beta系数随时间的变化趋势。 核心思想: 静态Beta: [========全部数据========] → 单一β值 滚动Beta: [窗口1] → β₁ [窗口2] → β₂ [窗口3] → β₃ ... → ... 1.2 为什么需要滚动窗口Beta? 问题:静态Beta的局限性 当前方案计算的是静态Beta,使用全部历史数据得到单一β值: # 当前方案 beta = Cov(全部ALT收益, 全部BTC收益) / Var(全部BTC收益) # 结果: β = 1.25 (一个固定值) 但现实中,Beta系数会随时间

By SHI XIAOLONG

皮尔逊相关系数异常值影响

一、执行摘要 1.1 问题概述 在加密货币市场的相关性分析中,皮尔逊相关系数(Pearson Correlation Coefficient)作为衡量两个资产收益率线性相关性的核心指标,对异常极端值高度敏感。当市场出现闪崩、闪涨、交易所故障或数据错误等异常情况时,这些极端值会显著扭曲相关系数的计算结果,导致: * ❌ 相关性被低估:当只有一个序列出现异常值时 * ❌ 相关性被高估:当两个序列同时出现异常值且方向一致时 * ❌ 分析结果不可靠:基于失真的相关系数做出的决策存在风险 1.2 核心发现 1. 异常值影响显著:单个异常值可能导致相关系数变化超过 50% 2. 加密货币市场异常值频繁:闪崩、闪涨、流动性枯竭等事件常见 3. 当前系统缺乏保护:hyperliquid_analyzer.py 中未实现异常值处理机制 4. 解决方案成熟:Winsorization 方法可以有效缓解此问题 1.3 建议措施 * ✅ 立即实施 Winsorization

By SHI XIAOLONG

如何找到、训练、养成英文独特的韵律感呢?

🎧 方法核心:模仿“音乐”,不是模仿“单词” ① 不看字幕,先听节奏 选一小段(10–15 秒): * 美剧 * 播客 * 演讲 先不管意思,只听: * 哪些地方重? * 哪些地方快? * 哪些地方拖长? ② 跟读时“夸张重音” 刚开始一定要夸张: * 重的地方用力 * 弱的地方糊过去 宁愿像“表演”,也不要像“念书”。 ③ 用“哼”的方式练 一个秘密方法: 把句子当旋律 先用 “da da DA da da DA” 哼出来 再把词塞进去 这是训练韵律感最直接的方式。

By SHI XIAOLONG

英文独特的韵律感

很多中国人英语“听得懂、单词会、语法也对”,但一开口就不像英语,问题几乎都不在音标,而在你说的这个——韵律感(rhythm & melody)。 一、为什么英语有“音乐感”,而中文人普遍缺? 1️⃣ 英语是重音节奏语言(stress-timed) 英语的节奏不是“一个字一个字平均念”,而是: 重音像鼓点,不重音的部分被压缩、弱化、连过去 例如一句话里: * 只有少数词被“敲响” * 其余词快速滑过、模糊处理 你听母语者说话,会感觉: 哒 —— 哒 —— 哒 中间的东西像流水一样带过去 2️⃣ 中文是音节节奏语言(syllable-timed) 中文更接近: 每个字时长差不多 所以中国人说英语时容易变成: I / WANT / TO / GO / TO / THE / STORE (每个词都一样重)

By SHI XIAOLONG