pybroker.indicator module

Contains indicator related functionality.

class Indicator(name: str, fn: Callable[[...], ndarray[Any, dtype[float64]]], kwargs: dict[str, Any])

Bases: object

Class representing an indicator.

Parameters:

• name – Name of indicator.

• fn – Callable used to compute the series of indicator values.

• kwargs – dict of kwargs to pass to fn.

__call__(data: BarData | DataFrame) → Series

Computes indicator values.

iqr(data: BarData | DataFrame) → float

Generates indicator data with data and computes its interquartile range (IQR).

relative_entropy(data: BarData | DataFrame) → float

Generates indicator data with data and computes its relative entropy.

class IndicatorSet

Bases: IndicatorsMixin

Computes data for multiple indicators.

__call__(df: DataFrame, disable_parallel: bool = False) → DataFrame

Computes indicator data.

Parameters:

• df – pandas.DataFrame of input data.

• disable_parallel – If True, indicator data is computed serially. If False, indicator data is computed in parallel using multiple processes. Defaults to False.

Returns:

pandas.DataFrame containing the computed indicator data.

add(indicators: Indicator | Iterable[Indicator], *args)

Adds indicators.

clear()

Removes all indicators.

remove(indicators: Indicator | Iterable[Indicator], *args)

Removes indicators.

class IndicatorsMixin

Bases: object

Mixin implementing indicator related functionality.

compute_indicators(df: DataFrame, indicator_syms: Iterable[IndicatorSymbol], cache_date_fields: CacheDateFields | None, disable_parallel: bool) → dict[IndicatorSymbol, Series]

Computes indicator data for the provided pybroker.common.IndicatorSymbol pairs.

Parameters:

• df – pandas.DataFrame used to compute the indicator values.

• indicator_syms – Iterable of pybroker.common.IndicatorSymbol pairs of indicators to compute.

• cache_date_fields – Date fields used to key cache data. Pass None to disable caching.

• disable_parallel – If True, indicator data is computed serially for all pybroker.common.IndicatorSymbol pairs. If False, indicator data is computed in parallel using multiple processes.

Returns:

dict mapping each pybroker.common.IndicatorSymbol pair to a computed pandas.Series of indicator values.

highest(name: str, field: str, period: int) → Indicator

Creates a rolling high Indicator.

Parameters:

• name – Indicator name.

• field – pybroker.common.BarData field for computing the rolling high.

• period – Lookback period.

Returns:

Rolling high Indicator.

indicator(name: str, fn: Callable[[...], ndarray[Any, dtype[float64]]], **kwargs) → Indicator

Creates an Indicator instance and registers it globally with name.

Parameters:

• name – Name for referencing the indicator globally.

• fn – Callable[[BarData, ...], NDArray[float]] used to compute the series of indicator values.

• **kwargs – Additional arguments to pass to fn.

Returns:

Indicator instance.

lowest(name: str, field: str, period: int) → Indicator

Creates a rolling low Indicator.

Parameters:

• name – Indicator name.

• field – pybroker.common.BarData field for computing the rolling low.

• period – Lookback period.

Returns:

Rolling low Indicator.

returns(name: str, field: str, period: int = 1) → Indicator

Creates a rolling returns Indicator.

Parameters:

• name – Indicator name.

• field – pybroker.common.BarData field for computing the rolling returns.

• period – Returns period. Defaults to 1.

Returns:

Rolling returns Indicator.