financial-machine-learning
A curated list of practical financial machine learning tools and applications.
A curated, auto-updated directory of machine learning tools and resources for finance and trading, covering deep learning, NLP, reinforcement learning, portfolio optimization, and alternative data sources.
This repository is a curated directory of machine learning tools and resources focused on finance, trading, and investment. It does not contain software you run directly. Instead, it is an organized list of links to other repositories, research papers, and tools, each with a short description and metrics like star count and last commit date. The list is updated automatically on a daily basis.
The categories cover the main ways machine learning gets applied in finance: deep learning and reinforcement learning for automated trading, classical machine learning models for price prediction, natural language processing for analyzing financial text and news, alternative data sources like satellite imagery and web traffic, portfolio optimization, risk management, and more. Each section shows the top 15 highest-ranked entries in the README, with the full lists available in a linked wiki.
The project is connected to Sov.ai, a quantitative research platform that works with hedge funds and investment firms. The repository appears to have started as a general community resource and has since become partially affiliated with that company's research work. The README includes a section recruiting PhD researchers to collaborate on quantitative finance projects.
ML-Quant.com is mentioned as a related daily research feed covering machine learning and quantitative finance topics, also run by the same organization.
For someone exploring this space, the repository works as a map of what tools exist and which ones are actively maintained, letting you identify starting points without having to search from scratch. It is aimed at developers, researchers, and quants rather than casual users, since the linked tools generally require programming knowledge to use.
The full README is longer than what was shown.
Where it fits
- Browse the top-ranked open-source ML tools for algorithmic trading to find a starting point for a quant project without searching from scratch.
- Discover NLP libraries and datasets for analyzing financial news or earnings call transcripts.
- Find actively maintained reinforcement learning frameworks evaluated specifically for automated trading strategies.