Resources

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)

Related literature

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