philosophy
Philosophy of quantymacro's Blog
This blog is not focused on backtesting random entry/exit rules, or Python snippets on how to fit complex ML models on price data. The blog focuses heavily on understanding the inner workings of quantitative methods, as well as some pitfalls that the author has fallen into. Yes, sometimes this does mean that we will go back to the basics (you probably need it). My biggest nightmare is going into a quant interview with ML in my CV and bomb a linear regression question.
I'm hiring a junior quant and out of 30 applicants only 4 have passed my first round interview. Despite having tier 1 math/stats degrees and prior roles, most are weeded out if you simply ask them basic questions about regression. I'm talking chapter 1 linear model stuff here. https://t.co/6yhAlbyAe6
— fdf (@0xfdf) May 7, 2024
the growing popularity of machine learning had a large negative impact on the average quality of applied finance projects in the MFE program I taught in -- we would warn teams extensively of the pitfalls but still 30% of them turned into exactly this https://t.co/r8CYH4yckv
— Benn Eifert 🥷🏴☠️ (@bennpeifert) March 30, 2022
It’s a strong opinion of mine that really intuitively understanding the properties of time series filters and really understanding the properties of univariate and multivariate OLS will pay off many times over.
— cephalopod (@macrocephalopod) December 5, 2023