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CHANGELOG.rst

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Changelog

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Version 0.1.5

  • Add the option to add a constraint on minimum portfolio return.
  • Add an example of how to add constraints on portfolio return and risk measures.

Version 0.1.4

  • Add Black Litterman with factors in two flavors: Black Litterman Bayesian model and Augmented Black Litterman model.
  • Implement factors_views, a function that allows to design views on risk factors for Black Litterman with factors.
  • Repair some bugs.

Version 0.1.2

  • Add Entropic Drawdown at Risk for Mean Risk Portfolio Optimization and Risk Parity Portfolio Optimization.
  • Repair some bugs.

Version 0.1.1

  • Repair some bugs in Portfolio related to Semi Variance and UCI.
  • Implement an option to annualize returns and risk in plot_frontier, Jupyter Notebook and Excel reports.
  • Add examples using Vectorbt for Backtesting and MOSEK for large scale problems.

Version 0.1.0

  • Repair some bugs in RiskFunctions.
  • Implement the Reports module that helps to build reports on Jupyter Notebook and Excel.
  • Implement plot_table, a function that resume some indicators of a portfolio.
  • Add Entropic Value at Risk for Mean Risk Portfolio Optimization and Risk Parity Portfolio Optimization.

Version 0.0.7

  • Implement normal assumption method to estimate box and elliptical uncertainty sets for Worst Case Optimization.
  • Implement elliptical uncertainty sets for covariance matrix.
  • Add Ulcer Index for Mean Risk Portfolio Optimization and Risk Parity Portfolio Optimization.
  • Implement functions to calculate Ulcer Index.

Version 0.0.6

  • Repair some bugs.
  • Implement bootstrapping methods to estimate box and elliptical uncertainty sets for Worst Case Optimization.
  • Implement Worst Case Mean Variance Portfolio Optimization using box and elliptical uncertainty sets.

Version 0.0.5

  • Repair some bugs.
  • Implement Risk Parity Portfolio Optimization for 7 convex risk measures.

Version 0.0.4

  • Repair some bugs.
  • Update to make it compatible with cvxpy >=1.1.0
  • Implement Principal Component Regression for loadings matrix estimation.
  • Add Akaike information criterion, Schwarz information criterion, R squared and adjusted R squared feature selection criterions in stepwise regression.

Version 0.0.3

  • Repair some bugs.
  • Implement an option for building constraints common for all assets classes.

Version 0.0.2

  • Repair some bugs.

Version 0.0.1

  • Implement robust estimates and ewma estimates.
  • Implement Black Litterman model and risk factors models.
  • Implement mean risk optimization with 10 risk measures.