A skeptic’s curated guide to machine learning for trading

What it does This is a curated awesome-list that indexes books, academic papers, video lectures, blogs, and practitioner interviews on machine learning for quantitative and algorithmic trading. The maintainer explicitly excludes low-quality material and marks personal favorites with a star icon.

The interesting bit The list is opinionated in a useful way. The author notes that most online courses covering ML for trading are “very poor,” so the syllabus leans heavily toward primary sources—peer-reviewed papers, canonical textbooks, and long-form talks. It is essentially a reading list assembled by someone who has already filtered out the noise.

Key highlights

• Heavy emphasis on foundational texts and academic research over quick tutorials.
• Starred recommendations highlight specific authorities like Marcos López de Prado and Howard Bandy.
• Topics span reinforcement learning, deep portfolios, market microstructure, and volatility trading.
• Includes podcast interviews and academic thesis presentations alongside traditional references.

Caveats

• This is pure curation: a hand-maintained index of external links with no executable code, backtesters, or datasets of its own.
• The README source is truncated, so the full depth of the list is not visible.

Verdict Worth bookmarking if you are a developer or researcher looking for a vetted on-ramp to quant-ML. Skip it if you need ready-to-run Python libraries or implementation guides.