Release pytimetk 1.2.0 · business-science/pytimetk

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).

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: v1.1.2...v1.2.0