Population

A Population is a list of portfolios (Portfolio or MultiPeriodPortfolio or both). Population inherits from the build-in list class and extends it by adding new functionalities to improve portfolio manipulation and analysis.

Example:

In this example, we create a Population of 100 random Portfolios:

from skfolio import (
    PerfMeasure,
    Population,
    Portfolio,
    RatioMeasure,
    RiskMeasure,
)
from skfolio.datasets import load_sp500_dataset
from skfolio.preprocessing import prices_to_returns
from skfolio.utils.stats import rand_weights

prices = load_sp500_dataset()
X = prices_to_returns(X=prices)

population = Population([])

n_assets = X.shape[1]
for i in range(100):
    weights = rand_weights(n=n_assets)
    portfolio = Portfolio(X=X, weights=weights, name=str(i))
    population.append(portfolio)

Let’s explore some of the methods:

print(population.composition())

print(population.summary())

portfolio = population.quantile(measure=RiskMeasure.VARIANCE, q=0.95)

population.set_portfolio_params(compounded=True)

fronts = population.non_denominated_sort()

population.plot_measures(
    x=RiskMeasure.ANNUALIZED_VARIANCE,
    y=PerfMeasure.ANNUALIZED_MEAN,
    z=RiskMeasure.MAX_DRAWDOWN,
    show_fronts=True,
)

population[:2].plot_cumulative_returns()

population.plot_distribution(
    measure_list=[RatioMeasure.SHARPE_RATIO, RatioMeasure.SORTINO_RATIO]
)

population.plot_composition()

A Population is returned by the predict method of some portfolio optimization that supports multi-outputs.

For example, fitting MeanRisk with parameter efficient_frontier_size=30 will find the weights of 30 portfolios belonging to the efficient frontier. Calling the method predict(X_test) on that model will return a Population containing these 30 Portfolio, predicted on the test set:

from sklearn.model_selection import train_test_split

from skfolio import (
    RiskMeasure,
)
from skfolio.datasets import load_sp500_dataset
from skfolio.optimization import MeanRisk
from skfolio.preprocessing import prices_to_returns

prices = load_sp500_dataset()
X = prices_to_returns(X=prices)
X_train, X_test = train_test_split(X, test_size=0.33, shuffle=False)

model = MeanRisk(
    risk_measure=RiskMeasure.VARIANCE,
    efficient_frontier_size=30,
)
model.fit(X_train)
print(model.weights_.shape)

population = model.predict(X_test)