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@agpenas agpenas commented Feb 18, 2025

Main features added:

  • Trend labelling algorithms:
    • Binary CTL
    • Ternary CTL
    • Oracle Binary Trend Labeller
    • Oracle Ternary Trend Labeller
  • Label scaling utils.
  • Returns estimation with transaction costs and holding fees.
  • Parameter optimization with Bayesian optimization. Default bounds.
  • Unit and integration tests.
  • Example notebooks

agpenas and others added 24 commits November 23, 2024 19:13
- Create Dockerfile for Python environment with necessary dependencies
- Add devcontainer.json for VS Code integration
- Include requirements for production and development
- Implement OracleBinaryTrendLabeler class for trend labeling
- Add visualization function for trend labels using Matplotlib
- Introduced BaseReturnEstimator as an abstract base class for return estimators.
- Implemented SimpleReturnEstimator for basic return calculations based on price differences and labels.
- Added ReturnsEstimatorWithFees to account for transaction and holding fees in return calculations.
- Removed labels from the BaseReturnEstimator constructor and input validation.
- Introduced a separate method for verifying labels in SimpleReturnEstimator.
- Updated estimate_return methods to accept labels as parameters.
- Adjusted fee estimation methods to utilize the new label verification.
- Adapt documentation
- Introduced BaseLabeller as an abstract base class for trend labelling, providing common input validation.
- Updated ContinuousTrendLabeller, OracleBinaryTrendLabeler, and TernaryCTL to inherit from BaseLabeller.
- Integrated input validation in the get_labels methods of the trend labellers to ensure valid time series input.
@agpenas agpenas merged commit 9775758 into main Feb 18, 2025
@agpenas agpenas deleted the feature/oracle_binary_labeller branch February 18, 2025 14:13
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