{"repo":"husainm97/quant-lab-alpha","free":true,"listed":false,"github":"https://github.com/husainm97/quant-lab-alpha","clone":"git clone https://github.com/husainm97/quant-lab-alpha.git","description":"Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.","language":"Python","stars":35,"topics":["block-bootstrap","correlation-analysis","data-science","drawdown-at-risk","factor-analysis","fama-french","fama-french-5-factor","investment-analysis","markowitz-frontier","markowitz-portfolio-analysis"],"license":"MIT","category":"trading","readme_excerpt":"📊 quant-lab-alpha — Portfolio Factor Regression & Analysis Toolkit Quant Lab Alpha is a desktop analytics suite designed to provide institutional-grade portfolio evaluation, combining factor-based risk attribution, portfolio optimisation, and long-horizon scenario simulations in one platform. Built on the Fama–French Five-Factor (FF5) model , the suite offers rolling regressions , block-bootstrap simulations , and stabilised Markowitz optimisation to uncover portfolio exposures, quantify potential outcomes, and model performance under both normal and stressed market conditions. --- 🎓 Technical Highlights - Factor Attribution : FF5 regressions with rolling window analysis reveal time-varying exposures - Robust Optimization : Ledoit-Wolf shrinkage addresses estimation error in covariance matrices - Realistic Simulation : Block bootstrap preserves autocorrelation structure vs. i.i.d. sampling - Stress Testing : Return/volatility shocks with leverage mechanics (margin interest on borrowed capital) 📓 Statistical Rigor : Methodologies are routinely demonstrated and validated against published benchmarks (see notebooks/ ). --- ⚠️ Disclaimer This repository is intended for quantifying risk exposures and statistical outcomes. All calculations, regressions, and results are not financial advice . Past performance does not guarantee future results . Any example portfolios included in this project are approximations of conceived factor exposure strategies , not investment recommendatio","default_branch":null,"files":null,"tree":[],"storefront":"/r/husainm97","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/husainm97/quant-lab-alpha/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}