Chapter Goals and Outline
Embedded Jupyter Notebook
See <Mixture Model Trading (Part 1, Part 2, Part 3, Part 4, Part 5, Github Repo)>. This research demonstrates a systematic trading strategy development workflow from theory to implementation to testing. It focuses on the concept of using Gaussian Mixture Models as a method for return distribution prediction and then using a simple market timing strategy to take advantage of the predicted asset return outliers.
Demonstrate how to extract algorithm portfolio equity from Quantconnect backtest
Demonstrate how to predict future return paths using bayesian cones.
Demonstrate how to estimate distribution of algorithm CAGRs.
Demonstrate how to use model averaging to aid predictions.
Read in Algorithm Portfolio Equity
Choose the Best Algorithm Among 4 Variants
Choose Best Bayesian Model of Algorithm Returns
Compare Bayesian Cones for all Algos and all Return Models
Compare Best Algo Predicted Portfolio Ending Values
Compare Best Algo Predicted CAGR Distributions