learner of things @decentvb
Joined November 2025-
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learner of things retweeted
Almost all of us will deal with tick data at some point in our careers. Even a MFT book will likely deal with tick data to transform it into some aggregated form. Unlike aggregated price data, it can be tricky to deal with the large volume of data in real time. Most practitioners will implement some kind of filter for both historical/live to catch any outliers. There are "goldilocks" methods that allow us to approximate "optimal filtering" when dealing with millions and millions of rows, and this article discusses exactly one of them! As usual, happy to share the article with some who retweet and comment on THIS post!
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@systematicls Hopefully i get to read this!
learner of things retweeted
As systematic traders, we are often working with tens of thousands of features where we need to find out what are the most predictive, uncorrelated features. Even at 10,000 features, a standard correlation matrix is 50 million calculations, and is near untenable. At 1,000,000 features, almost all uncorrelated feature selection methods break down. Not this one. In today's article we discuss a pretty cool feature selection method that removes all redundant features and still find the most predictive features. As usual, retweet and comment for a free chance at a free article :-).
learner of things retweeted
I’ve spent 10 years teaching math to machine learning engineers. 80% of university math is irrelevant to your actual job. Luckily, I've created a FREE roadmap to teach you the 20% you actually need. Like, retweet, and comment "roadmap" and I'll DM you the link.
@systematicls Can i get the link please?
@annanay How can this be modified to work in less liquid markets?
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