New Functions
- `augment_drawdown()`: The augment_drawdown function calculates the drawdown metrics for a financial time series using either pandas or polars engine, and returns the augmented DataFrame with peak value, drawdown, and drawdown percentage columns.
- `augment_rolling_risk_metrics()`: The augment_rolling_risk_metrics function calculates rolling risk-adjusted performance metrics for a financial time series using either pandas or polars engine, and returns the augmented DataFrame with columns for Sharpe Ratio, Sortino Ratio, and other metrics.
- `augment_fip_momentum()`: Calculate the "Frog In The Pan" (FIP) momentum metric over one or more rolling windows using either pandas or polars engine, augmenting the DataFrame with FIP columns.
- `augment_stochastic_oscillator`: The `augment_stochastic_oscillator()` function calculates the Stochastic Oscillator (%K and %D) for a financial instrument using either pandas or polars engine, and returns the augmented DataFrame.
- `augment_adx()`: Calculate Average Directional Index (ADX), +DI, and -DI for a financial time series to determine strength of trend.
- `augment_hurst_exponent()`: Calculate the Hurst Exponent on a rolling window for a financial time series.
- `augment_ewma_volatility()`: Calculate Exponentially Weighted Moving Average (EWMA) volatility for a financial time series.
- `augment_regime_detection()`: Detect regimes in a financial time series using a specified method (e.g., HMM).
Bug Fixes and Speed Improvements
- `summarize_by_time()`: polars engine rebuild. Columns should match pandas engine.
- `__init__.py`: updated to fix circular imports
- `get_date_summary()`: Fixed issues with polar tz
- `augment_hilbert()`: Improve polars engine and fix error with groupby()
- `augment_ewm()`: fix example `from pytimetk import augment_ewm`
- `test_plot_timeseries`: Fix broken test
**Full Changelog**: https://github.com/business-science/pytimetk/compare/v1.1.2...v1.2.0