finq Release Notes
This minor release introduces new optimization functionality for the Portfolio class. It supports minimizing virtually any objective function using either any of the supported scikit-learn optimizers, or a custom optimizer that you provide.
Take a look at the example available under the examples directory in the repository for information on the optimization api.
📋 Changelog:
- feat(finq): bump minor release 0.3.0 → 0.4.0 (#67)
- docs(image): OMXS30 sharpe ratio COBYLA plot (#66)
- docs(examples): mean variance optimization with COBYLA
- feat(portfolio): mean variance optimization with constraints (COBYLA)
- feat(opt): add exception information to user
- feat(plot): randomize portfolios and plot mean variance
- feat(optimize): add formulas, constraints, and objective functions
- Merge pull request #61 from wilhelmagren/feature/portfolio
- feat(portfolio): implement formulas, weight check decorator.
- Merge pull request #59 from wilhelmagren/feature/asset
- build(finq): add mplfinance and plot deps
- feat(dataset): index date handling, visualization
- fix(dataset): modularize data and info features
- feat(portfolio): implement computing quantities for portfolio
- feat(asset): implement eq and hash functionality
- build(test): ignore deprecation warnings when pytest
- feat(asset): implement str and pre-compute
- feat(asset): implement metrics and docs
- feat(asset): implement quantities