PyQuant dropped 150+ scripts
PyQuant News published a GitHub collection of 150+ free Python scripts for downloading, manipulating, and analyzing market data — plus a companion post outlining seven beginner-friendly Python use cases like portfolio optimization, risk assessment, and algo trading. It's a ready-made codebase for building interview-ready projects and reproducible demos. ( )
PyQuant’s GitHub org profile describes itself as “Where practitioners come to get started with Python for quant finance & algorithmic trading” and lists 29 followers with a U.S. location.. The PyQuant-Newsletter repository contains a long file listing of runnable Jupyter notebooks including explicit files named build_your_own_risk_parity_portfolio.ipynb, value_at_risk_manage_your_portfolios_risk.ipynb, and how_to_use_the_sharpe_ratio_for_riskadjusted_returns.ipynb.. A specific Colab-hosted notebook, 82_HierarchicalRiskParity.ipynb, implements hierarchical risk parity using historical prices, Pearson-correlation clustering, and visualized risk contributions.. PyQuant’s public site states a 37K-subscriber audience and advertises a 13-module “Getting Started With Python for Quant Finance” course that ships with 40 pre-built code templates. (pyquantnews.com). The newsletter code repo shows ongoing maintenance (the repo metadata lists recent commits) and community traction with about 43 stars on GitHub for the PyQuantNewsletter project.. Interactive Brokers lists PyQuant News as a Campus contributor and describes the creator’s output as “hundreds of free, high-quality code tutorials” spanning backtesting, algorithmic execution, options, and machine-learning workflows..
