A personal collection of quantitative and algorithmic investment strategies based in Python, sourced academic papers, GitHub repositories, and the internet. This repository is for educational purposes only. It's just some stuff that I thought to be cool to try and implement in code.
I'm really excited to build this repository from the ground up. The goal is to create submodules on backtesting, portfolio optimization, and machine learning prediction algorithms. As of right now, the repository consists of two modules, backtesting and ml-models. We have touched on a simple time series momentum trading strategy and an XGBoost time series forecasting model.