Changelog

All notable changes to GRU Dynamic Beta will be documented here.

[0.2.0] - Unreleased

Added

  • High-level convenience API: estimate_beta(), compare_betas(), quick_report()

  • Named configuration presets: default, responsive, smooth, research

  • Command-line interface: python -m grubeta AAPL SPY

  • Custom exception classes with finance-friendly error messages

  • 4 Jupyter notebook tutorials (quickstart, hedging, comparison, research)

  • format_summary() for human-readable beta summaries

  • grubeta[data] optional dependency group for yfinance

Changed

  • README rewritten with finance-first framing

  • Project description updated in pyproject.toml

  • Research preset description clarified (enhanced model capacity, not full features via convenience API)

  • Walk-forward train window now derives from config instead of hardcoded 500

  • TemporalCertificate uses actual package version

[0.1.3] - 2026-02-20

Added

  • lambda_alpha_smooth hyperparameter for L2 temporal smoothness penalty on alpha

  • 4-component composite loss function (accuracy, beta stability, alpha sparsity, alpha stability)

Changed

  • CAPMLoss.create_composite_loss now accepts lambda_alpha_smooth parameter

  • Default lambda_alpha_smooth=0.1 (2x beta smoothness weight, reflecting that alpha should be smoother than beta under CAPM)

[0.1.2] - 2026-02-11

Fixed

  • Critical: pydantic dependency now correctly included in published package

  • Publish workflow no longer depends on deleted testpypi environment

  • Migrated to Pydantic V2 validators (@field_validator, ConfigDict)

  • Removed duplicate line in _create_return_features

  • Aligned minimum Python version to 3.9 across all metadata

  • Added initial_beta (default=1.0) and initial_alpha (default=0.0) config parameters

  • Removed stray verify_revert.py from repository

[0.1.1] - 2026-02-06

Added

  • Fixed missing pydantic dependency

  • Validated “Zero Defects” status

  • Improved build configuration

[0.1.0] - 2026-01-23

Initial release.

Added

  • Core DynamicBeta class for GRU-based beta estimation

  • DataPreprocessor for feature engineering with lookahead bias prevention

  • BetaEvaluator for comprehensive model evaluation

  • Walk-forward validation with anchored expanding window

  • Composite loss function (accuracy + stability + sparsity)

  • Multiple input modes (simple returns vs. full features)

  • Diagnostic tests for lookahead bias detection

  • Benchmark comparison utilities (rolling OLS, EWMA, static)

  • Model persistence (save/load)

  • GPU acceleration support

  • Comprehensive documentation and examples

Technical Details

  • Dual-pathway GRU architecture (beta + alpha pathways)

  • Configurable hyperparameters via DynamicBetaConfig

  • Feature engineering via FeatureConfig

  • Support for technical indicators (requires ta library)

  • Macroeconomic feature integration

Future Plans

[0.3.0] - Planned

  • Attention mechanism for beta pathway

  • Transformer-based alternative model

  • Regime detection integration

  • Real-time prediction mode

  • Extended benchmark comparisons

[0.4.0] - Planned

  • Multi-asset portfolio beta estimation

  • Factor model extension (Fama-French)

  • Confidence intervals for beta estimates

  • Online learning mode