Mixture Model Trading (Part 5 - Algorithm Evaluation with pymc3)

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Post Outline

  • Recap

  • Chapter Goals and Outline

  • Links

  • Embedded Jupyter Notebook

Recap

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. 

Chapter Goals

  • 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.

Chapter Outline

  1. Read in Algorithm Portfolio Equity

  2. Choose the Best Algorithm Among 4 Variants

  3. Choose Best Bayesian Model of Algorithm Returns

  4. Compare Bayesian Cones for all Algos and all Return Models

  5. Compare Best Algo Predicted Portfolio Ending Values

  6. Compare Best Algo Predicted CAGR Distributions

  7. Model Averaging

Links

Embedded Jupyter Notebook