`skfolio.population`.Population#

class skfolio.population.Population(iterable)[source]#

Population Class.

A Population is a list of Portfolio or MultiPeriodPortfolio or both.

Parameters:

iterablelist[BasePortfolio]: The list of portfolios. Each item can be of type Portfolio and/or MultiPeriodPortfolio. Empty list are accepted.

Methods

`append`(item)	Append portfolio to the end of the population list.
`clear`(/)	Remove all items from list.
`composition`([names, tags, display_sub_ptf_name])	Composition of the portfolios in the population.
`copy`(/)	Return a shallow copy of the list.
`count`(value, /)	Return number of occurrences of value.
`extend`(other)	Extend population list by appending elements from the iterable.
`filter`([names, tags])	Filter the Population of portfolios by names and tags.
`index`(value[, start, stop])	Return first index of value.
`insert`(index, item)	Insert portfolio before index.
`max_measure`(measure[, names, tags])	Returns the portfolio with the maximum measure.
`measures`(measure[, names, tags])	Vector of portfolios measures for each portfolio from the population filtered by names and tags.
`measures_mean`(measure[, names, tags])	Mean of portfolios measures for each portfolio from the population filtered by names and tags.
`measures_std`(measure[, names, tags])	Standard-deviation of portfolios measures for each portfolio from the population filtered by names and tags.
`min_measure`(measure[, names, tags])	Returns the portfolio with the minimum measure.
`non_denominated_sort`([first_front_only])	Fast non-dominated sorting.
`plot_composition`([names, tags, ...])	Plot the compositions of the portfolios in the population.
`plot_cumulative_returns`([log_scale, idx, ...])	Plot the population's portfolios cumulative returns.
`plot_distribution`(measure_list[, tag_list, ...])	Plot the population's distribution for each measure provided in the measure list.
`plot_measures`(x, y[, z, to_surface, ...])	Plot the 2D (or 3D) scatter points (or surface) of a given set of measures for each portfolio in the population.
`pop`([index])	Remove and return item at index (default last).
`quantile`(measure, q[, names, tags])	Returns the portfolio corresponding to the `q` quantile for a given portfolio measure.
`remove`(value, /)	Remove first occurrence of value.
`reverse`(/)	Reverse IN PLACE.
`set_portfolio_params`(**params)	Set the parameters of all the portfolios.
`sort`(*[, key, reverse])	Sort the list in ascending order and return None.
`sort_measure`(measure[, reverse, names, tags])	Sort the population by a given portfolio measure and filter the portfolios by names and tags.
`summary`([formatted, names, tags])	Summary of the portfolios in the population

append(item)[source]#: Append portfolio to the end of the population list.

clear(/)#: Remove all items from list.

composition(names=None, tags=None, display_sub_ptf_name=True)[source]#

Composition of the portfolios in the population.

Parameters:

namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.
display_sub_ptf_namebool, default=True: If this is set to True, each sub-portfolio name composing a multi-period portfolio is displayed.

Returns:

summaryDataFrame: Composition of the portfolios in the population.

copy(/)#: Return a shallow copy of the list.

count(value, /)#: Return number of occurrences of value.

extend(other)[source]#: Extend population list by appending elements from the iterable.

filter(names=None, tags=None)[source]#

Filter the Population of portfolios by names and tags. If both names and tags are provided, the intersection is returned.

Parameters:

namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

populationPopulation: A new population of portfolios filtered by names and tags.

index(value, start=0, stop=sys.maxsize, /)#

Return first index of value.

Raises ValueError if the value is not present.

insert(index, item)[source]#: Insert portfolio before index.

max_measure(measure, names=None, tags=None)[source]#

Returns the portfolio with the maximum measure.

Parameters:

measure: Measure: The portfolio measure.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuesBasePortfolio: The portfolio with maximum measure.

measures(measure, names=None, tags=None)[source]#

Vector of portfolios measures for each portfolio from the population filtered by names and tags.

Parameters:

measureMeasure: The portfolio measure.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuesndarray: The vector of portfolios measures.

measures_mean(measure, names=None, tags=None)[source]#

Mean of portfolios measures for each portfolio from the population filtered by names and tags.

Parameters:

measureMeasure: The portfolio measure.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuefloat: The mean of portfolios measures.

measures_std(measure, names=None, tags=None)[source]#

Standard-deviation of portfolios measures for each portfolio from the population filtered by names and tags.

Parameters:

measureMeasure: The portfolio measure.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuefloat: The standard-deviation of portfolios measures.

min_measure(measure, names=None, tags=None)[source]#

Returns the portfolio with the minimum measure.

Parameters:

measureMeasure: The portfolio measure.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuesBasePortfolio: The portfolio with minimum measure.

non_denominated_sort(first_front_only=False)[source]#

Fast non-dominated sorting. Sort the portfolios into different non-domination levels. Complexity O(MN^2) where M is the number of objectives and N the number of portfolios.

Parameters:

first_front_onlybool, default=False: If this is set to True, only the first front is sorted and returned. The default is False.

Returns:

frontslist[list[int]]: A list of Pareto fronts (lists), the first list includes non-dominated portfolios.

plot_composition(names=None, tags=None, display_sub_ptf_name=True)[source]#

Plot the compositions of the portfolios in the population.

Parameters:

namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.
display_sub_ptf_namebool, default=True: If this is set to True, each sub-portfolio name composing a multi-period portfolio is displayed.

Returns:

plotFigure: Returns the plotly Figure object.

plot_cumulative_returns(log_scale=False, idx=None, names=None, tags=None)[source]#

Plot the population’s portfolios cumulative returns. Non-compounded cumulative returns start at 0. Compounded cumulative returns are rescaled to start at 1000.

Parameters:

log_scalebool, default=False: If this is set to True, the cumulative returns are displayed with a logarithm scale on the y-axis and rebased at 1000. The cumulative returns must be compounded otherwise an exception is raise.
idxslice | array, optional: Indexes or slice of the observations to plot. The default (None) is to take all observations.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

plotFigure: Returns the plot Figure object.

plot_distribution(measure_list, tag_list=None, n_bins=None, **kwargs)[source]#

Plot the population’s distribution for each measure provided in the measure list.

Parameters:

measure_listlist[Measure]: The list of portfolio measures. A different distribution is plotted per measure.
tag_listlist[str], optional: If this is provided, an additional distribution is plotted per measure for each tag provided.
n_binsint, optional: Sets the number of bins.

Returns:

plotFigure: Returns the plotly Figure object.

plot_measures(x, y, z=None, to_surface=False, hover_measures=None, show_fronts=False, color_scale=None, names=None, tags=None, title='Portfolios')[source]#

Plot the 2D (or 3D) scatter points (or surface) of a given set of measures for each portfolio in the population.

Parameters:

xMeasure: The x-axis measure.
yMeasure: The y-axis measure.
zMeasure, optional: The z-axis measure.
to_surfacebool, default=False: If this is set to True, a surface is estimated.
hover_measureslist[Measure], optional: The list of measure to show on point hover.
show_frontsbool, default=False: If this is set to True, the pareto fronts are highlighted. The default is False.
color_scaleMeasure | str, optional: If this is provided, a color scale is displayed.
titlestr, default=”Portfolios”: The graph title. The default value is “Portfolios”.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

plotFigure: Returns the plotly Figure object.

pop(index=-1, /)#

Remove and return item at index (default last).

Raises IndexError if list is empty or index is out of range.

quantile(measure, q, names=None, tags=None)[source]#

Returns the portfolio corresponding to the q quantile for a given portfolio measure.

Parameters:

measureMeasure: The portfolio measure.
qfloat: The quantile value.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuesBasePortfolio: Portfolio corresponding to the q quantile for the measure.

remove(value, /)#

Remove first occurrence of value.

Raises ValueError if the value is not present.

reverse(/)#: Reverse IN PLACE.

set_portfolio_params(**params)[source]#

Set the parameters of all the portfolios.

Parameters:

**paramsAny: Portfolio parameters.

Returns:

selfPopulation: The Population instance.

sort(*, key=None, reverse=False)#

Sort the list in ascending order and return None.

The sort is in-place (i.e. the list itself is modified) and stable (i.e. the order of two equal elements is maintained).

If a key function is given, apply it once to each list item and sort them, ascending or descending, according to their function values.

The reverse flag can be set to sort in descending order.

sort_measure(measure, reverse=False, names=None, tags=None)[source]#

Sort the population by a given portfolio measure and filter the portfolios by names and tags.

Parameters:

measureMeasure: The portfolio measure.
reversebool, default=False: If this is set to True, the order is reversed.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

valuesPopulations: The sorted population.

summary(formatted=True, names=None, tags=None)[source]#

Summary of the portfolios in the population

Parameters:

formattedbool, default=True: If this is set to True, the measures are formatted into rounded string with units. The default is True.
namesstr | list[str], optional: If provided, the population is filtered by portfolio names.
tagsstr | list[str], optional: If provided, the population is filtered by portfolio tags.

Returns:

summarypandas DataFrame: The population’s portfolios summary

skfolio.population.Population#

`skfolio.population`.Population#