I construct a series of time-series features from the literature and apply a novel XGBoost model to predict the next days price of a number of assets. The concept is simple and can be expanded to many variables, incorporate many assets and be applied to different Machine Learning models.
I cover a number of portfolio optimisation models using R from the literature, the portfolio allocation models might be extended to the Black-Litterman model etc.
A simple backtested Bollinger-Band strategy and Directional Movement Index (ADX) strategy.
I break down some popular and fundamental models from quantitative finance & engineering, construct some factor analysis on a series of Assets and EFTs along with a randomly generated portfolio constructed from scraping tickers from the SPY500 wikipedia page.