与「2019萤火虫Cosplay」相关的搜索结果

Immigration and Customs Enforcement on Thursday released the wife of an active-duty U.S. Army soldier and Afghanistan war veteran after a monthlong detention, her husband, Sgt. 1st Class Jose Serrano, told CBS News. Serrano's wife, El Salvador native Deisy Rivera Ortega, was detained by ICE on April 14 during an immigration appointment in El Paso, Texas. Serrano, who has served in the Army for 27 years, including three deployments to Afghanistan, first revealed to CBS News last month that his wife had been arrested by ICE after living in the U.S. for roughly a decade. At the time, the Department of Homeland Security said ICE arrested Rivera Ortega because of a deportation order dating back to 2019. DHS also said she was convicted of entering the U.S. illegally, a federal misdemeanor.

信仰买家（Conviction Buyers，CB）为市场创造了BTC最底层的共识需求。在过去的每轮熊市中，他们都扮演“救世主”的角色。 越跌越买是他们的一贯风格，当价格跌入谷底，他们的持仓到达顶峰。也正因如此，才生生买出了熊底。 2019年2月：BTC：$3,400；CB持仓峰值：439.9w 枚BTC； 2022年12月：BTC：$16,000；CB持仓峰值：346.4w 枚BTC; 然而，2026年3月BTC $68,000时，CB持仓峰值高达 388.2w 枚BTC！甚至还超过了上轮周期。 尽管当前BTC的价格根本不算低，他们在本轮周期，依然表现的足够好。你们还不知足？反正我是觉得已经非常给力了。 后面随着价格逐渐回升，他们的持仓应该会逐步下降，直到牛市顶峰。把筹码派发给谁了？当然是后面再接盘的新买家咯。

CryptoQuant 的比特币牛熊周期指标，自2023年3月以来首次转绿！ 从历史上看，当该指标走出熊市区域并进入牛市初期阶段时，通常表明最糟糕的调整阶段已经过去，市场结构开始复苏，2019 和 2023 两次应验 不过2022年3月是一个例外，该指标转绿后，BTC 价格进一步下跌 这一次会更像 2019 和 2023, 还是 2022 呢，边走边看了

我觉得把大资金挪去炒美股、韩股甚至是A股是没毛病的 但一定要留一部分资金在场内，两个原因： 1/ Crypto也是承接流动性外溢的一环，优先级甚至高于很多传统赛道的标的 2/ 如果Crypto 有下一轮，就算你只留一小部分资金，行情来了你也可以炒成大资金，2019年、2022年底、2024年下半年都是这种情况 一定要留在牌桌上，保持对市场的感知

自从前年健身VIP套餐到期、加上自己比较喜欢的教练离职后，我就再也没能长期坚持健身了。刚开始还靠惯性撑了几个月，后来连“三天打鱼两天晒网”都做不到了。 其实我也不是买了课就会去，2019年买的几十节私教课，几年都没用完。我需要的是好的教练来引导和陪伴。抖音上也买过各种领域的课程，基本都是听一两次就落灰了。 我也知道网上有大量免费的优质健身视频，基础教程一应俱全。我本身现在也不缺“怎么练”的知识，各种细节也清楚不少。但如果那几个教练会来，我还是会愿意付费。 那问题来了：这到底是不是智商税呢？毕竟基础动作我都会，体脂率也一度降到12-13。好的信息是免费的，是不是就不值得花钱了？ 事实上，大多数优质信息都是免费的，多数人缺的并不是信息，而是“在正确的时间点被推动”的外部环境。信息可以免费，但执行力呢？如果你能免费驱动自己，那所有付费都是智商税；如果你自己驱动不了，那付费买好的“外部约束”反而是为数不多有效的增强执行力的方案，或者说就是付费买“他律”来增强自己比较弱的“自律”。 免费能解决信息，但解决不了行动，而且你什么都不懂的话，你也没能力筛选信息，当然，这也是付费买信息的一个坑，因为你不懂所以你容易被骗。所以，为认知付费可能是智商税，但为执行力付费不是。

Jeff的这条回测其实很有意思。 很多 $BTC holder 想囤币生生息的时候都会觉得： “我反正长期持币，那我卖一点 OTM Call，收点权利金，不就能增币了吗？” 但回测结果显示： 第一种：每天卖一点 30 天后到期的 0.1 delta OTM Call。 结果：2019–2026 年，总收益只有 0.37 BTC，年化 1.8%，Sharpe 0.22，最大回撤 -0.63 BTC。 第二种：如果卖更近一点、更“肥”的 0.25 delta Call，结果更差。 总收益只有 0.02 BTC，年化几乎为零，Sharpe 0.0069，最大回撤反而达到 -1.38 BTC。 这种策略基本完全是个笑话，或者说不择时的做的话，完全是个笑话。 单纯的，不择时的，裸卖call，就是一种分不清主次，搞不明白你是来干毛的，抓不住重点的，又怕事儿的纯纯的懦夫策略。 原因也很简单： BTC 真正值钱的部分，恰恰是少数几次右尾暴涨。 无脑卖 Call，本质上就是长期把这部分凸性卖掉。 这也是为什么我非常不喜欢备兑，并且抵触卖call。 $BTC 持币生息这件事，真的不是一句“卖 Call 收息”能解决的。 那问题来了：卖方策略到底能不能赚钱？ 能。波动率风险溢价（VRP）是真实存在的，Leifu用1814天的数据验证过，三个市场周期VRP全部为正。卖方长期有edge，这个结论没问题。但edge存在不等于你能收割它，就像矿在地下，你没有工具就只能看着。 @JeffLia12309881 的波动率课程教的就是这套工具。不是“卖put还是卖call”这种入门问题，而是从底层往上搭建一整套风险管理框架： 第一层：曲面认知。 你不能只看一个IV数字就决定卖不卖。Jeff从SABR模型校准讲起，教你读懂整个波动率曲面，ATM水平是高还是低、Skew是陡还是平、曲率（Fly）是什么形态。同样IV 50%，Skew陡峭时卖put和Skew平坦时卖put，风险收益完全不同。 第二层：Greeks管理。 标准BSM的Greeks在曲面变形时会失真。Jeff讲的是SABR框架下的smile Greeks，VegaSkew和VegaFly，让你知道自己的仓位在Skew变化1个vol点时亏多少钱，在曲率压缩时赚多少钱。 有了这个你才能做精确的P&L归因，而不是每天看着不明盈亏抓瞎。 第三层：二阶波动率交易。 这是课程里门槛最高但最有价值的部分。Vanna、Volga不只是教科书上的定义，而是实际决定你在极端行情中是活着还是爆仓的关键变量。 Jeff讲Volga如何在尾部事件中非线性放大亏损、Vanna如何在spot-vol相关性翻转时让你的delta hedge失效，这些是回测里那些巨大回撤背后的真正原因。 第四层：期限结构和择时。 什么时候该卖近月，什么时候该卖远月，contango和backwardation下的策略选择完全不同。 @leifuchen 之前推文的数据显示VRP从+14.7压缩到+4.4，在这个趋势下你如果还用2021年的策略在2026年卖vol，安全垫已经薄到覆盖不了一次像样的尾部事件。 回测告诉你“裸卖不行”，但不会告诉你“怎么卖才行”。 Jeff的课程就是回答后面这个问题的——六节直播课，2000+小时蒸馏出来的体系，从曲面认知到Greeks管理到二阶波动率，是目前中文世界里最完整的期权波动率交易教育。想让卖方策略真正跑出来，工具得先到位。​​​​​​​​​​​​​​​​ @JeffLia12309881 的课程配合 @GreeksLive 的工具，是囤币人持币生息的不二之选。 欢迎对Crypto波动率交易策略感兴趣的朋友加入我们期权讨论社区：

感谢 Jeff 分享双币赢高卖策略的回测结果，我再补充一下双币赢的低买策略的回测结果（2019.6-2026.5）： 策略描述： 本金 3 BTC，每天卖出 0.1 张 30 天后到期的虚值 10 delta Put，不对冲。30 天积累 3 张卖权，此后新建仓与每天到期平衡。 收益分析（见左图）： - 总盈亏：0.82 BTC （vs 高卖 0.39 BTC ） - 胜率：66.7%（vs 高卖 65.8%） - 最大回撤：-0.66 BTC （vs 高卖 0.63 BTC） - 夏普： 0.32 （vs 高卖 0.23） 总体来看，双币赢的低买策略表现要比高卖更好，承担的尾部风险差不多但收益翻番。 如果把这双币赢的高卖和低买策略组合，结果会如何呢？ 策略描述： 本金 3 BTC，每天各卖出 0.1 张 30 天后到期的虚值 10 delta Put 和 虚值 10 delta Call，不对冲。30 天积累 6 张卖权，此后新建仓与每天到期平衡。 收益分析（见右图）： - 总盈亏：1.19 BTC （相当于低买和高卖收益累加 ） - 胜率：66.5%（胜率变化不大） - 最大回撤：-0.61 BTC （ 回撤没有累加） - 夏普： 0.40 （比高卖 0.23 和低买的 0.32 都要高 ） 低买和高卖策略合并体现了组合策略的优势，收益累加，波动没有累加，夏普得到了很大的提升。

Here's the #1# thing most people don't know about Warren Buffett: There is nothing special about Buffett’s stock picking. That doesn’t mean that Buffett wasn’t a great investor. He was! Buffett was, by far, the greatest investor in history, by a huge margin. Over 486 months between October 1976 and March 2017 –— 41 years –— Berkshire Hathaway’s Class A stock earned an average excess return of 18.6% per year above U.S. Tbills. Annualized volatility was 23.5%. Sharpe ratio: 0.79. Berkshire’s Sharpe ratio of (0.79) is roughly 1.6x times the broad U.S. stock market’s Sharpe ratio of 0.49 over the same period. Among all large-cap U.S. stocks and mutual funds with 30-plus-year continuous track records, those are unmatched numbers. A dollar invested in Berkshire on October 31, 1976, was worth more than $3,685 by March 31, 2017. A dollar invested in the S&P 500 with dividends reinvested over the same period was worth approximately $76. Buffett beat a passive index by a multiple of 48. But he didn’t do it with stock picking! Three researchers at AQR Capital Management –— Andrea Frazzini, David Kabiller, and Lasse Heje Pedersen –— dissected Berkshire’s 50 years of investments through 2013. They expanded and republished their findings in 2018 in the Financial Analysts Journal, which is the most highly respected industry financial journal. Their work won the Graham and Dodd Award for the best published paper of the year. The paper is called Buffett’s Alpha. They found, after accounting for cheap leverage (from the insurance float) and exposure to a handful of publicly documented factor premiums, Buffett’s investment skill –— the portion of his returns that cannot be explained by any mechanical strategy –— is 0.3% per year. That's statistically indistinguishable from zero. In other words, the alpha that Berkshire enjoyed for 50 years (as it compounded capital at 24% a year!) wasn’t due to Buffett’s stock picking. So, how did he do it? He did it by gaining access to a huge amount of investment capital that he did not own, for free. Buffett’s track record was built on leverage. That’s a dirty word for most investors, but it's the secret behind Berkshire. The AQR researchers had access to something most Buffett commentators do not: 40 years of Berkshire’s audited financial statements and the full quarterly history of the public 13F stock portfolio. The researchers asked a specific question: If I take Berkshire’s monthly stock returns from October 1976 through March 2017, and I run a linear regression against a set of well-documented risk factors –— market beta, size, value, momentum, and two newer factors called Betting-Against-Beta and Quality-Minus-Junk (detailed below) –— how much of Buffett’s performance can the factors explain? And after the factors have been stripped out, how much excess return remains? The data show clearly there are a few qualities that drove Berkshire’s results. First, Buffett has always preferred large-cap stocks, contrary to the popular image of him as a small-cap value investor. He buys elephants. Second, no surprise, Buffett buys cheap. Berkshire is almost six standard deviations away from neutral on the value axis. So far the picture is ordinary. Every large- cap value manager in America loads positively on size and on value. Buffett’s genius lies in the last two factors. These last two factors are a little complicated, but please stick with me. There’s a new factor, that, like value and size, characterizes Buffett’s strategy. It’s called Betting-Against-Beta (“BAB”). What it means is intentionally investing in stocks with very low volatility. The BAB factor captures the excess return that accrues to investors who own low-beta stocks. Low-beta stocks have historically earned higher risk-adjusted returns than high-beta stocks. Financial theory teaches that higher beta (higher risk) should mean higher return. But it doesn’t. The opposite occurs, in fact. And Buffett was one of the very first people to figure this out. Why does this factor persist? In an efficient market, once that factor is known to investors, then they should bid the price up on low- beta stocks until it no longer provides an edge. The explanation, per the theory of AQR’s Frazzini and Pedersen’s theory, is that because ordinary investors do not use leverage and seek high returns, they create persistent excess demand for more volatile stocks. (Having worked with retail investors for 30 years, I can assure you that is true.) But, an investor with access to cheap leverage –— Warren Buffett, for instance –— can exploit the mispricing by owning the low-beta names and levering them up to produce market-beating returns. And the last factor that matters to Buffett is quality. Buffett buys companies with high returns on invested capital. Quality-Minus-Junk (“QMJ”) is a factor described by Cliff Asness, also at AQR with Frazzini, and Pedersen, in a 2019 paper in Review of Accounting Studies. The QMJ factor captures the return to owning stocks of high-quality companies –— profitable, growing, safe, with high payout ratios –— against stocks lacking those characteristics. QMJ has been positive and statistically significant in every major developed equity market for which it has been measured. Berkshire’s loading is 0.37, with a t-statistic of 4.6. –– meaning it is highly significant to Berkshire’s results. In plain English: Buffett only buys large, high- quality, low-volatility stocks of the highest quality. But, Berkshire’s results were not, in any way, unusual. Any investor buying these same kinds of stocks would have earned those same returns –– about 16% a year over time. So how did Berkshire compound at 23% a year? To figure that out, AQR’s researchers built a Berkshire replica. They constructed a simple, rules-based, publicly investable portfolio that mechanically tilts toward large-cap, cheap, low-beta, high-quality stocks, and levers it 1.6- to- 1 to match Berkshire’s insurance float leverage. The correlation between their replica’s returns and Berkshire’s were virtually identical. The authors’ conclusion is unambiguous. “In summary, we find that Buffett has developed a unique access to leverage that he has invested in safe, high-quality, cheap stocks and that these key characteristics can largely explain his impressive performance.” Berkshire’s cost of insurance float has averaged almost three percentage points below the Treasury bill rate across 50fifty years of data. In roughly two-thirds of all years, Berkshire has been paid to hold other people’s money. That is not an investment strategy. That is a financing miracle. It is also the living, breathing heart of Berkshire Hathaway. It’s what Buffett built, starting in 1967 when he paid $8.6 million for National Indemnity’s $19.4 million of float. And it is the factor every retail investor admiring Berkshire’s returns has never paid any attention to. The 1.6-to-1 leverage that AQR measured over the full period, financed at this negative cost, explains the dollar magnitude of Berkshire’s returns. How do we know? An unleveraged version of the same stock portfolio –— which you can approximate by looking at the 13F holdings alone –— has earned an average excess return of 12% percent per year. It’s Berkshire’s leverage that magnifies this excess return to 18.6 %percent. How does this square with Berkshire’s reported gains? Berkshire’s 18.6% excess return, plus the T-bill rate that averaged roughly 4.7% over 1976–2017, gives you a total nominal return of roughly 23% per year, which is the figure you usually see quoted for Berkshire’s historical performance. The 23% tells you what Berkshire returned. The 18.6% tells you how much of that return was compensation for taking investment risk, as opposed to the baseline yield every lender to the U.S. government was earning anyway. With both of Berkshire’s “edges” –— systematic factor exposures to cheap, high-quality, low-volatility stocks and roughly 1.6-to-1 leverage delivered with insurance float –— you get Berkshire Hathaway’s 23% annual gains over 60 years. It’s the structure that’s genius, not the stock picking. And that's very important because it means the original Berkshire formula can work for any investor. I show you exactly how, in my new book.

比特币四次减半 完整周期规律对照表（纯历史复盘，不构成投资建议） 先记住核心周期公式，万年通用： 熊市大底（行情最差+启动起点）→ 减半前12～18个月 主升浪加速 → 减半前6个月 牛市历史大顶 → 减半后12～18个月 一、四轮减半完整时间+节点复盘 第一轮减半：2012-11-28 1. 上轮牛市顶部：2011年6月 2. 熊市终极大底：2011年12月 3. 见底距离减半：提前11个月 4. 行情启动：2011年12月（全网绝望、行情最差） 5. 加速上涨：2012年5月（减半前6个月） 6. 本轮牛市顶部：2013年11月（减半后12个月） 第二轮减半：2016-07-09 1. 上轮牛市顶部：2013年12月 2. 熊市终极大底：2015年8～10月 3. 见底距离减半：提前9～11个月 4. 行情启动：2015年四季度 5. 加速上涨：2016年1月（减半前6个月） 6. 本轮牛市顶部：2017年12月（减半后17个月） 第三轮减半：2020-05-11 1. 上轮牛市顶部：2017年12月 2. 熊市终极大底：2018年12月 3. 见底距离减半：提前17个月 4. 行情启动：2018年12月（极致恐慌） 5. 加速上涨：2019年11月（减半前6个月） 6. 本轮牛市顶部：2021年11月（减半后18个月） 第四轮减半：2024-04-20 1. 上轮牛市顶部：2021年11月 2. 熊市终极大底：2022年11月 3. 见底距离减半：提前17个月 4. 行情启动：2022年11月（FTX暴雷，情绪最差） 5. 加速上涨：2023年10月（减半前6个月） 6. 本轮牛市顶部：历史规律推算 → 2025年4月～2025年10月 二、总结你最关心的核心规律 1. 完美印证你的判断：每一轮都是行情最惨、情绪最绝望的时候，就是底部，也是新一轮行情的启动点 2. 四年一轮完整节奏： • 筑底启动：下次减半前 12-18个月 • 主升爆发：下次减半前 6个月 • 巅峰见顶：下次减半后 12-18个月 三、下一次减半时间（第五轮） 第五次减半：预计 2028年4月左右 按历史规律倒推： • 下一轮超级大底、新一轮行情起点：2026年中～2027年初 • 下一轮主升加速：2027年10月左右 • 下一轮牛市终极顶部：2029年中前后 你可以直接收藏这个时间轴，整个加密周期的节奏全部清晰了。 温馨提示：历史规律不代表未来必然复刻，仅作周期参考，不做任何交易指引。