sklearn & skfolio interop

Every estimator-shaped class in Fundcloud is a drop-in for both sklearn and skfolio — no adapters required. The same FeaturePipeline, MeanRisk, PurgedKFold, and Portfolio objects you'd use in a notebook are the ones you compose inside a sklearn.pipeline.Pipeline or nest under GridSearchCV. Fit/predict semantics, parameter-name conventions (__ separator for nested access), and get_params / set_params behaviour all match sklearn's estimator_checks suite, which means joblib serialisation, grid search, and cross-validated scoring round-trip cleanly.

Why this matters for research-to-production

The path from a notebook backtest to a scheduled production pipeline is almost always the place where ad-hoc code accumulates. By keeping everything on the sklearn estimator contract, the same object graph survives the move — the notebook code becomes the production code, not a rewrite of it.

Contracts

Component	Base class	Implements
Transformer (IndicatorSpec, FeaturePipeline)	sklearn.base.TransformerMixin, BaseEstimator	fit(X, y=None), transform(X)
CV splitter (PurgedKFold, EmbargoedKFold)	sklearn.model_selection.BaseCrossValidator	split, get_n_splits
Optimiser (MeanRisk, HRP, EqualWeighted, MVO)	skfolio BaseOptimization when [pf] is installed; sklearn BaseEstimator otherwise	fit(X), predict(X) -> Portfolio, score(X)

Nested pipelines

from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from fundcloud.features import FeaturePipeline
from fundcloud.features.indicators import RSI, SMA
from fundcloud.optimize import MeanRisk, RiskMeasure
from fundcloud.validate import EmbargoedKFold

pipe = Pipeline([
    ("features", FeaturePipeline([
        ("rsi", RSI(timeperiod=14)),
        ("sma", SMA(timeperiod=20)),
    ])),
    ("optim",    MeanRisk(risk_measure=RiskMeasure.CVAR)),
])

search = GridSearchCV(
    pipe,
    param_grid={"optim__min_weights": [0.0, 0.02, 0.05]},
    cv=EmbargoedKFold(n_splits=5, purge=3, embargo=1),
)
search.fit(returns_panel)

skfolio round-trip

from fundcloud.portfolio import Portfolio

# Fundcloud → skfolio
sk_pf = fc_pf.to_skfolio()

# skfolio → Fundcloud
fc_pf = Portfolio.from_skfolio(sk_pf)

Portfolio.from_skfolio preserves returns + weights (when skfolio exposes a per-period weights matrix), and Portfolio.to_skfolio builds a fresh skfolio.Portfolio with the correct shape.

Without the [pf] extra

If skfolio isn't installed:

• fundcloud.optimize.EqualWeighted / InverseVolatility resolve to the pure-Python fallback class (same fit/predict contract, long-only
• fully-invested constraints, scipy.optimize.minimize).
• fundcloud.optimize.MVO is always available as the pure-Python MVO.
• Every other skfolio optimiser raises ImportError with a clear install hint when first attribute-accessed.

The fallbacks also pass sklearn.utils.estimator_checks.check_estimator, so Pipeline / GridSearchCV round-trips are unchanged.