Qlib is an open-source, AI-oriented quantitative investment platform developed by Microsoft. It covers the full pipeline for quantitative research and trading: data ingestion and feature engineering (Alpha158, Alpha360, high-frequency), model training (traditional ML like LightGBM/XGBoost, deep learning in PyTorch including LSTM, GRU, Transformer, GATs, and reinforcement learning), backtesting with realistic costs and slippage, portfolio optimization, and online serving for production-like deployment. The platform is highly modular and config-driven, with a core library (qlib) and a separate contrib layer for community extensions. It includes a CLI (qrun) for running full workflows from YAML configs, extensive experiment tracking via MLflow, and a rich set of evaluation metrics. While primarily a library/application, it also functions as a research benchmark suite with many example models and data collectors for global markets. The codebase is active and well-maintained, though some components (e.g., TFT model, older RL code) show signs of technical drift due to dependency version locks and deprecated patterns.
This repository is a curated list (awesome-list) of open-source quantitative finance libraries, packages, and resources. It provides a structured README.md organized by category, with inline language tags, and a pipeline that parses the list, enriches it with GitHub metadata (stars, last commit dates), and generates a static website deployed to GitHub Pages. The repo also includes automated tooling for pull-request review: a validation script that checks entry format, section placement, URL security, duplicate detection, and a full CI-driven PR review workflow. The core logic resides in parse.py (multi-threaded GitHub API enrichment), site/generate.py (static site generator), scripts/validate_readme.py (entry validation), and scripts/review_pr.py (automated PR review with offline URL reachability checks and cross-PR duplicate detection). The project is primarily a reference list, not an application or data pipeline, but it includes high-quality supporting tooling for maintainers and contributors.
yfinance is a mature Python library that provides programmatic access to Yahoo Finance market data. It is primarily an application/library, not a data research project, though it includes data repair and cleaning utilities. The library exposes a high-level API (Ticker, download, Search, Lookup, Market, Screener) that fetches historical prices, fundamentals, financial statements, holders, analysis, and more from Yahoo's various JSON endpoints. It handles Yahoo's quirks (cookie/crumb authentication, timezone issues, data errors) and includes a sophisticated price repair pipeline that detects and corrects common data anomalies like 100x unit mixups, bad stock splits, zero values, and dividend double-counting. The architecture is monolithic with modular scrapers and utilities. It has extensive test coverage (though currently failing in CI), active maintenance, and a large user base (25k+ stars). The codebase is well-structured with reusable utility functions, but some legacy artifacts (Travis CI, disabled pytest workflow) and heavy reliance on heuristic repair logic introduce medium technical drift.