- 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
- Model Averaging