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