Kid Quant

Just a quick warning before I start, this post is going to be math heavy. Those who are not brave enough to traverse these waters, be forewarned! Let's get right to it: To recap, last time I talked about a few basic statistical concepts regarding time series. Stationarity, Memory and reconciling them both using an idea called fractional differencing. This post walks through how we do this mathematically and gets down to the brass tacks'. Before starting, I need to explain a few intermediate steps to avoid confusion. 1. The Taylor series expansion for the function f(x) = (1+x)^d for any complex number d,  is the binomial series. Don't worry too much about the complex number part. Essentially, what this is saying is the following: We have a function (1+x)^d that's a bit messy to deal with so we use a mathematical technique called Taylor-series approximation to get a bit of insight into how we construct this function as a sum of polynomials. We'll come

So somehow you've wandered into this hazy corner of the internet and found my blog. Not sure how...or why you're exactly here but I hope you'll stay. Let me introduce myself, I'm just your run of the mill budding algorithmic trader. I studied Mathematical Economics and Computer Science at the University of Richmond and was looking for an outlet to apply these tools to finance. I grew up in Indonesia (which is where I currently reside) and am actively looking for jobs to leverage these skills (I have been interviewing for a fair number of quant trading companies so don't hesitate to reach out if you need interview tips!). Unlike most of the quants on the blogosphere, I don't have a phD nor am I considering getting one in the near term future. Nevertheless, I did take every possible statistics course my school offered and I have experience applying many of these concepts (think stationarity, non-parametric sampling, forecasting, boosting) to an algorithmic t