CSP 全称：Cash-Secured Put（现金担保卖权）
承诺在某个价格买股票，如果没跌到就收权利金
两种结局：
当前股价：$429
卖出 $400 执行价 Put 期权，到期日 10/31（28 天后）
可收权利金约 $1,530
要么低价买入 TSLA，要么白拿权利金
Max Return：$1,530（最大收益）
Max Loss：$38,470（理论最大亏损）
Breakeven：$384.7（盈亏平衡点）
Delta：0.3（约 30% 行权概率）
Theta：0.44（时间衰减利好卖方）
Probability of Profit：69.5%（获利概率）
LEAPS = Long-term Equity Anticipation Securities，长期股票期权
到期时间长：> 1 年，常见 2-3 年
我的最爱：回报高、风险可控、出场灵活
比 Covered Call 更赚钱，也更安心
LEAPS = 期权中的玄铁重剑
录制当天股价 = $170.29
买 100 股 = $170.29 x 100 = $17,029
买 LEAPS = $47.82 x 100 = $4,782
节省资金 = $12,247
Delta ~= 0.75 -> 股价涨 $1，期权涨 $0.75 ~= 持有 75 股
期权总价 = $4,782
内在价值 = ($170.29 - $145) x 100 = $2,529
外在价值 = $4,782 - $2,529 = $2,253
In this chapter, you are asked to design a news feed system. What is news feed? According to the Facebook help page, “News feed is the constantly updating list of stories in the middle of your home page. News Feed includes status updates, photos, videos, links, app activity, and likes from people, pages, and groups that you follow on Facebook” [1]. This is a popular interview question. Similar questions commonly asked are to: Design Facebook news feed, Instagram feed, Twitter timeline, etc.
A notification system has already become a very popular feature for many applications in recent years. A notification alerts a user with important information like breaking news, product updates, events, offerings, etc. It has become an indispensable part of our daily life. In this chapter, you are asked to design a notification system.
A notification is more than just mobile push notification. Three types of notification formats are: mobile push notification, SMS message, and Email. Figure 1 shows an example of each of these notifications.
In this chapter, we focus on web crawler design—an interesting and classic system design interview question.
A web crawler is known as a robot or spider. It is widely used by search engines to discover new or updated content on the web. Content can be a web page, an image, a video, a PDF file, etc. A web crawler starts by collecting a few web pages and then follows links on those pages to collect new content. Figure 1 shows a visual example of the crawl process.