Python
https://github.com/ranaroussi/yfinance (calling Yahoo!Finance API for historical prices)
https://github.com/bukosabino/ta (technical indicators)
https://scikit-learn.org/stable/ (classical ML)
https://github.com/skorch-dev/skorch (PyTorch + sklearn API)
Data
https://github.com/ranaroussi/yfinance (historical prices)
https://data.nasdaq.com/ (tickers, ratios)
https://www.frbsf.org/economic-research/indicators-data/daily-news-sentiment-index/ (sentiment)
https://fred.stlouisfed.org/ (rates, spreads, other macros)
https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html (risk free rate)
Gu, Kelly & Xiu (2020), Empirical Asset Pricing via Machine Learning.
https://academic.oup.com/rfs/article/33/5/2223/5758276
Fieberg, Metko, Poddig & Loy (2023) Machine learning techniques for cross-sectional equity returns’ prediction https://link.springer.com/article/10.1007/s00291-022-00693-w
Drobetz & Otto (2021) Empirical asset pricing via machine learning: evidence from the European stock market https://link.springer.com/article/10.1057/s41260-021-00237-x
Rapach & Zhou (2022), Asset Pricing: Time-Series Predictability.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3941499
Rossi (2018) Predicting stock market returns with machine learning.
https://mendoza.nd.edu/wp-content/uploads/2019/07/2018-Alberto-Rossi-Fall-Seminar-Paper-1-Stock-Market-Returns.pdf