Pre-Selection Transformers

A Pre-Selection transformer performs a pre-selection on the initial assets universe.

It follows the same API as scikit-learn’s estimator: the fit_transform method takes X as the assets returns and returns a new X with only the pre-selected assets.

X can be any array-like structure (numpy array, pandas DataFrame, etc.)

Available transformers are:

• DropCorrelated

• SelectKExtremes

• SelectNonDominated

Example:

from sklearn import set_config

from skfolio.datasets import load_sp500_dataset
from skfolio.pre_selection import DropCorrelated
from skfolio.preprocessing import prices_to_returns

set_config(transform_output="pandas")

prices = load_sp500_dataset()
X = prices_to_returns(prices)
print(X.shape)

model = DropCorrelated(threshold=0.5)
new_X = model.fit_transform(X)
print(new_X.shape)

Pre-Selection transformers are fully compatible with sklearn.pipeline.Pipeline:

Example:

from sklearn import set_config
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline

from skfolio.datasets import load_sp500_dataset
from skfolio.optimization import MeanRisk
from skfolio.pre_selection import DropCorrelated
from skfolio.preprocessing import prices_to_returns

set_config(transform_output='pandas')

prices = load_sp500_dataset()
X = prices_to_returns(prices)
X_train, X_test = train_test_split(X, shuffle=False, test_size=0.3)

pipe = Pipeline([('pre_selection', DropCorrelated(threshold=0.9)),
                 ('mean_risk', MeanRisk())])
pipe.fit(X_train)

portfolio = pipe.predict(X_test)
print(portfolio.annualized_sharpe_ratio)