qis.BenchmarkReturnsPositiveNegativeRegime

class qis.BenchmarkReturnsPositiveNegativeRegime(freq='QE', return_type=ReturnTypes.RELATIVE, regime_ids_colors=None)

Bases: RegimeClassifier

Regime classifier based on positive vs negative benchmark returns.

Classifies periods into two simple regimes based on benchmark return sign, useful for up/down market analysis.

Parameters:

• freq (str)

• return_type (ReturnTypes)

• regime_ids_colors (Dict[str, str])

__init__(freq='QE', return_type=ReturnTypes.RELATIVE, regime_ids_colors=None)

Initialize positive/negative regime classifier.

Parameters:

• freq (str) – Sampling frequency (default: ‘QE’ for quarter-end)

• return_type (ReturnTypes) – Type of returns to compute

• regime_ids_colors (Dict[str, str]) – Mapping of regime names to colors

Methods

__init__([freq, return_type, regime_ids_colors])	Initialize positive/negative regime classifier.
class_data_to_colors(regime_data)	Map regime IDs to colors for visualization.
compute_regimes_pa_perf_table(prices, ...[, ...])	Compute regime performance attribution table.
compute_sampled_returns_with_regime_id(...)	Classify periods by benchmark return sign.
get_regime_ids()	Get ordered list of regime IDs.
get_regime_ids_colors()	Get mapping of regime IDs to visualization colors.
to_dict()	Convert regime parameters to dictionary.

Attributes

REGIME_COLUMN

compute_sampled_returns_with_regime_id(prices, benchmark, include_start_date=True, include_end_date=True, **kwargs)

Classify periods by benchmark return sign.

Parameters:

• prices (DataFrame | Series) – Asset prices

• benchmark (str) – Benchmark column name

• include_start_date (bool) – Include first period

• include_end_date (bool) – Include last period

Returns:

DataFrame with returns and regime classification

Raises:

ValueError – If insufficient data for classification

Return type:

DataFrame

compute_regimes_pa_perf_table(prices, benchmark, perf_params, drop_benchmark=False, **kwargs)

Compute regime performance attribution table.

Parameters:

• prices (DataFrame) – Asset prices

• benchmark (str) – Benchmark asset name

• perf_params (PerfParams) – Performance parameters; perf_params.sharpe_convention selects the regime-Sharpe convention (PA default, ARITHMETIC/LOG exactly additive)

• drop_benchmark (bool) – Exclude benchmark from results

Returns:

Tuple of (performance table, regime data dictionary)

Return type:

Tuple[DataFrame, Dict[RegimeData, DataFrame]]