Melvin Alvarez
9a8032e4ee
feat(timeseries): add time-series integration helpers for financial analysis
- Implement 6 helper functions in src/timeseries_utils.rs:
* prepare_for_hmm: Feature engineering for HMM regime detection
* rolling_hurst_exponent: Mean-reversion detection (H < 0.5 = mean-reverting)
* rolling_half_life: Mean-reversion speed for pairs trading
* return_statistics: Risk metrics (mean, std, skew, kurt, sharpe)
* create_lagged_features: ML feature matrix creation
* rolling_correlation: Rolling correlation for pairs trading
- Add PyO3 bindings in src/timeseries_utils/python_bindings.rs:
* All functions exposed with _py suffix
* Proper signature decorators and error handling
* Registered in lib.rs module system
- Update Python module exports:
* python/optimizr/core.py: Import from _core
* python/optimizr/__init__.py: Re-export all functions
- Create comprehensive example:
* examples/timeseries_integration.py demonstrates all 6 functions
* Includes integrated pairs trading workflow
* Shows feature engineering for regime detection
- Technical details:
* Fixed Array1<f64> type conversions for ndarray compatibility
* Uses risk_metrics::hurst_exponent and estimate_half_life
* Built successfully with maturin develop --release (40.93s)
* All functions tested and working correctly
Part of Priority 3: Time-series integration helpers (Enhancement Strategy)
Addresses v0.3.0 roadmap: Bridge optimization with time-series analysis