Reservoir Sampling

How to randomly and uniformly smaple k items out of a population (of an unknow size)?

The Most Frequently Asked Question About Expectations

Source

Problem

Suppose we toss a fair coin, what is the expected number of tosses until we get two heads in a row?

I’ll show three ways of solving this problem.

Method I: Recursive Expectation in Two Steps

Let $X$ be the number of tosses until we get two heads in a row, then we use the recursive relations of expectations to write down an equation of $\mathbb{E}\left[X\right]$ as follows:

This recursive relation can be better explained by the probabilistic tree below:

For those who know Markov chains, we can also draw a state transition diagram as follows:

Method II: Recursive Expectation in One Step

This mehod is almost the same as Method I, except that we use the recursive relations of expections to write down a system of equations to solve:

As you see more examples in my next post, it is more common to formulate a system of equation, rather than a single equation, for the problem of recursive expectations.

Method III: Martingale Approach (Optional)

We introduce a random process $X_n,n\geq 1$ as a random process to represent the coin toss process, that is,

Note that $X_n$ is a martingale. And we use another random process $Y_n,n\geq 1$ to represent our trading strategy,

This means that we start our first bet with $1$, then we double our bet to $2$ if the previous coit toss turns out to be head and still bet $1$ otherwise.

Consequently, our wealth process $Z_n = Y_1X_1 + \sum_{i=2}^n Y_i(X_i - X_{i-1})$ is given by

Indeed, $Z_n$ is a discrete-time stochastic integral with respect to $X_n$, and therefore, $Z_n$ is also a martingale.

Now, we define a stopping time $\tau = \min\{n\geq 2: X_n = 1, X_{n-1}=1\}$. The definition of $\tau$ would imply that $X_1=\cdots=X_{\tau-2}=0,X_{\tau-1}=X_{\tau}=1$.

Finally, we invoke the optinal sampling theorem to get

How to Get a Financial Quant Job? // 如何找到一份金融宽客的工作？

In this post, I will share some resources and practical tips about how to get a quant job.

Preparation

Source

Generally, a quant needs to know:

• Programming
• Math
• Stats (Machine Learning)
• Finance

Note: math used to be importmant when derivative pricing was just becoming popular, however, nowdays I feel that the demand for quants who build mathematical models decreases dramatically. On the other hand, quants need to code more. Thus, I put programming in the first place.

An example of a quant job description is this.

Apparently, it is hard to be an expert in all four areas. Ideally, a candidate is strong in one area, while knowing a little bit about other areas.

For those STEM majors, it is worthwhile spending some time learning about finance. A good choice is to study the CFA Program. If you can pass at least its Level I Exam, you should already have a basic yet comprehensive knowledge of finance.

Now, statistics plus computer science become a new buzzword: machine learning. If you want to get your foot into the door, consider taking a MOOC course like Andrew Ng’s Machine Learning. To get some hands-on experience of machine learning, take a look at Kaggle, an excellent data science competition platform.

Application

Most firms in the quant space accept online applications, a great place to start is this quant firm list.

Source

Apply early. Big investment banks typically have their own recruiting cycles, so don’t miss their deadlines.

Resume

Learn from good examples: Princeton MFin students’ resume book.

Cover Letter

Not many firms ask for a cover letter. My cover letter simply expands my resume, and elaborates on my past experience on math, stats (machine learning), programming and finance.

Interview

Interview questions can be quite comprehensive, covering math, stats (machine learning), programming and finance.

Classic quant interview books include:

Before an interview, I would also check:

In this blog, I will constantly post some most commonly asked interview questions.

It usually take several rounds of phone interviews and one on-site interview to get an offer.

The more interviews your get, the stronger your become as a candidate. Persistence is key! I hope this blog post is useful to those who are interested in quant jobs.

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

• 编程
• 数学
• 统计（机器学习）
• 金融