quantinsti quantitative learning
Code and backtest a multi-factor portfolio strategy Create a strategy using a
clustering algorithm Apply simulation methods such as Bootstrapping and Monte
Carlo Use Unsupervised Learning, clustering algorithms, and understand concepts
like PCA, euclidean distance, WCSS, elbow curve and dimensionality Calculate the
expected returns for an asset Allocate capital using the Kelly criterion, modern
portfolio theory, and risk parity Evaluate portfolio performance using Sharpe
ratio, maximum drawdown and monthly performance Allocate weights to a portfolio
based on a hierarchical risk parity (HRP) approach
