Resource: https://github.com/initial-d/ml-quant-trading
Paper: https://arxiv.org/abs/2507.07107
Suggested placement
GITHUB-FINANCE-REPOS.md, if it meets the maintainer's criteria for a hand-picked addition.
Why it may fit
This is an MIT-licensed PyTorch research stack for cross-sectional multi-factor trading. Its distinguishing technical focus is mask-first handling of limit-up, limit-down, halted, and missing observations before rolling factor computation. The repository also includes ML baselines, portfolio optimization, vectorized backtesting, tests, and synthetic/public-data reproduction paths.
I am opening an issue instead of editing the generated, star-sorted list directly so the maintainer can decide whether and how it belongs.
Disclosure
I maintain the repository and authored the accompanying paper; this is a transparent self-suggestion for curator review.
Resource: https://github.com/initial-d/ml-quant-trading
Paper: https://arxiv.org/abs/2507.07107
Suggested placement
GITHUB-FINANCE-REPOS.md, if it meets the maintainer's criteria for a hand-picked addition.Why it may fit
This is an MIT-licensed PyTorch research stack for cross-sectional multi-factor trading. Its distinguishing technical focus is mask-first handling of limit-up, limit-down, halted, and missing observations before rolling factor computation. The repository also includes ML baselines, portfolio optimization, vectorized backtesting, tests, and synthetic/public-data reproduction paths.
I am opening an issue instead of editing the generated, star-sorted list directly so the maintainer can decide whether and how it belongs.
Disclosure
I maintain the repository and authored the accompanying paper; this is a transparent self-suggestion for curator review.