Metadata Routing

This document shows how you can use the metadata routing mechanism to route metadata to the estimators consuming them. For a complete explanation, you can refer to the scikit-learn documentation

A full example is available here: Using Implied Volatility with Metadata Routing

Let’s suppose you use the ImpliedCovariance estimator inside a MeanRisk estimator. In addition to the assets’ returns X, the ImpliedCovariance estimator also needs the assets’ implied volatilities passed to its fit method. In order to route the implied volatilities time series from the MeanRisk estimator to the ImpliedCovariance estimator, we need metadata routing.

First, a few imports and some random data for the rest of the script:

from sklearn import set_config

from skfolio.moments import ImpliedCovariance
from skfolio.optimization import MeanRisk
from skfolio.prior import EmpiricalPrior
from skfolio.preprocessing import prices_to_returns
from skfolio.datasets import load_sp500_dataset, load_sp500_implied_vol_dataset

prices = load_sp500_dataset()
implied_vol = load_sp500_implied_vol_dataset()

X = prices_to_returns(prices)
X = X.loc["2010":]

Metadata routing is available only if explicitly enabled:

set_config(enable_metadata_routing=True)

Then, in order to route the metadata, you must use set_fit_request:

model = MeanRisk(
    prior_estimator=EmpiricalPrior(
        covariance_estimator=ImpliedCovariance(
        ).set_fit_request(implied_vol=True)
    )
)
model.fit(X, implied_vol=implied_vol)
print(model.weights_)