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Fractional Differencing Implementation (FD Part 3)

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Well...That took a lot longer than I expected it too. 6 weeks later and I finally have the last installation in these series of posts. It's also the longest one so you could say it was worth the wait. I recently found out that Python 2.7 (the python I've used for EVERY project) will soon be deprecated. In other words, any support or bug-fixes will cease to exist. In an effort not to repurpose all my Python 2.7 code into Python 3.x code at the same time, I thought I would get a head start. Dealing with sudo permission issues, homebrew and having multiple versions of python existing on my mac are only a few reasons why there was such a substantial delay. I've also been learning a new platform called MetaTrader5 for backtesting and live trading purposes. Although it's not as glamorous as TradeStation, NinjaTrader or AmiBroker that is seen most often with retail traders, it does have a fair bit of flexibility. Having integration with use cases outside of the program itsel
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Fractional Differencing Derivation Walkthrough (FD Part 2)

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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
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Welcome and Introduction to Fractional Differencing (FD Part 1)

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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
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