pecker

This has been a surprisingly popular request - an article on all the common mistakes beginner quants make. In my experience managing starry-eyed researchers eager to prove themselves, all mistakes are essentially a result of insufficient skepticism for the difficulty of finding alpha, a mistaken belief that complexity is correlated with performance and a poor research hygiene honed by school.

You read about index rebalance strategies everywhere, and it seems like a hedge fund darling, why? It's one of the few systematic* strategies that are extremely scalable (you can run BILLIONS in capacity), and where the economic rationale for working is easily understood. Index rebalance is all about predicting how much dollar flow is going into a stock relative to its average daily traded dollar volume. A high dollar flow relative to its usual traded dollar volume will mean the stock price will go up, and vice versa. It is beautifully simple conceptually, but VERY difficult to get right practically. A lot of PMs talk a big game about index rebal, but actually getting it right and managing your risks is NOT easy. Especially when so much of your gross returns can show up as momentum and short interest factors! --- I write below about a foundational methodology, a starting point for thinking systematically about index changes. You’ll build more sophisticated signals over time, but this framework captures the core mechanics. This methodology on systematic index change prediction crystallizes an approach many quant shops have traded for years. Comment AND retweet on THIS post to get a chance for a free article!

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!

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 :-).

You've heard of crypto trend signals. Have you wondered how they were created? Today, I actually show you guys how to create a performant, scalable trend signal. Hint: It's going to be a smart ensemble of weak trend estimators. Randomly giving out free articles for retweets!

If you've used machine learning to solve problems, one of the common questions you will have is how to do feature selection? One possibility is the Sequential Forward Selection (SFS), which builds a feature subset one feature at a time. However, it is not feasible when working with an enormous set of features AND has issues with path dependency! In today's article we cover a strictly superior variant of the Sequential Forward Selection! Find it at the bad place, or retweet for a chance at a free article!

You have produced multiple forecasts over multiple horizons, how do you trade them optimally? These are the things your PM (the monetization team) is trying to solve! When you have forecasts at multiple horizons, the natural instinct is to combine them somehow and trade toward the combined target. Perhaps you'll try equal weights. Maybe returns-weighted. Maybe volatility-adjusted. All of these are wrong! Want to understand how your PM monetizes multiple forecasts? I'm giving away free articles to random retweets as per usual!

@beniduboss If you are starting over again as a newly graduate w a finance degree and have no coding skills and limited math knowledge, what will you start learning to break into the quant field? Thanks Beni

Since I am still stuck in the train with no possibility of doing anything productive because I don’t have my Yubikey with me: Let’s do a Q&A My last one was like 2 years ago or something I’ll answer anything as long as it doesn’t dox my identity Have fun

@maruushae We eating gooodddd

LAST FIGHT

@maruushae Jackylove after aa more than 2 times

Yeah that exactly for that type of combo is scary on gen

@maruushae 🫡

31 in 25 hours

@maruushae jinxed it lmao

RIP, gona be a 3-1, fucked the multi legs

@maruushae R3 shoulda been rumble

never lets faker play again aurora

@maruushae T1 win man

FLY/GEN/BLG/HLE

@maruushae Bonk monke strong

Verify hang and creme recent bank transaction, there is no way they aren't paid for throwing that game

@maruushae Didnt interrupt any dashes the whole game but caught Ruler flying into 5 man lmao

Kael who see alistar/ksante/xin/kaisa pick on his poppy Chinesse poppy are demon raised from the lowest level of the hell

@maruushae Keksante after 10 nerfs and mini reworks

Reksai during teamfight since the start of world

@maruushae And they drafted 3 dashes into poppy kekw

Poppy first pick ?

@maruushae CFO when Faker steps up slightly

Beautifull game from CFO, desotryed FNC and T1, Doggo on draven is a fucking demon