allocation 0.0.1

Online (streaming) portfolio construction: scikit-learn / skfolio-compatible estimators over a keyed dynamic universe

allocation

Online (streaming) portfolio construction. A home for portfolio methods as scikit-learn / skfolio-compatible estimators (fit / partial_fit / predict / weights_), on top of a keyed dynamic-universe layer so they survive reconstituting universes.

from allocation import ThurstonePortfolio

est = ThurstonePortfolio(calib="market", phi=1.0)
est.fit(returns)            # returns: (n_obs, n_assets)
est.weights_                # long-only weights on the simplex

for batch in stream:        # streaming / rebalancing
    est.partial_fit(batch)  # transports a fixed seed ensemble -> low turnover
    w = est.weights_

score(X) returns the portfolio's Sharpe ratio (skfolio's scoring convention), and to_portfolio(X) wraps the fitted weights as a skfolio Portfolio (pip install allocation[skfolio]) so the output drops into skfolio's metrics, Population, and cross-validation.

Why a separate package

skfolio is the scikit-learn-native portfolio library, but it is batch: a changing universe is handled by NaN/incomplete-data mechanics, not by carrying per-asset state across streaming updates. allocation is the online complement — estimators with partial_fit, a keyed dynamic universe (in the spirit of precise), and turnover control by construction — while staying API-compatible so each estimator can also be contributed upstream to skfolio.

Methods

Estimator	Status	Notes
ThurstonePortfolio	working	Ability tilt: weights are winning probabilities of a correlated race; calibrate to a benchmark under a reference correlation, tilt under the estimate; smooth common-seed transport for partial_fit. Built on thurstone.
SchurComplementary	planned	Online version of the Schur-complementary construction (the batch version is in skfolio).
baselines	planned	Online equal / cap / minimum-variance / risk-parity for benchmarking.

Design

allocation/
  base.py        # BaseOnlinePortfolio: fit / partial_fit / predict / weights_
  moments.py     # EwmaCovariance (default); any partial_fit/covariance_ estimator plugs in
  universe.py    # keyed dynamic-universe state (planned)
  thurstone.py   # ThurstonePortfolio
  _thurstone/    # calibration + transport engine

Covariance is pluggable: the default is a light EWMA, but any online estimator exposing partial_fit and covariance_ (e.g. a precise skater) can be passed via covariance=.

Status

Early. The Thurstone estimator and the streaming transport work and are tested; the keyed dynamic-universe layer and the Schur estimator are next. The theory behind the Thurstone method (feasibility, redundancy consistency, the implied regularized objective, smoothness/turnover) is written up in the accompanying paper.

License

MIT © Peter Cotton