allocation

Online (streaming) portfolio construction.

scikit-learn / skfolio-compatible estimators (fit / partial_fit / weights_ / predict) on a keyed dynamic-universe layer, so they survive reconstituting universes where assets come and go. Two methods are novel; the rest are the standard toolkit, re-engineered to stay smooth under streaming updates.

Methods

Novel

Thurstone portfolio

Weights are the winning probabilities of a Thurstonian race. Abilities calibrate the race to a benchmark; the tilt re-runs it under the estimated correlation. A common-seed transport keeps streaming updates low-turnover.

Schur-complementary

The Schur gamma coupling (HRP at 0, minimum variance toward 1), but ordered by Fiedler seriation instead of a dendrogram — so the hierarchy, and the weights, are a smooth function of the covariance. Background and bibliography at schur.microprediction.org.

Standard, kept smooth

Read weights = allocator(cov(data)) as two factors. For the closed-form linear allocators (minimum variance, max diversification / decorrelation, inverse variance, mean–variance) the allocator is already smooth in the covariance — pair them with a smooth online covariance and you are done. The novelty in Thurstone and Schur is the cases where the allocator itself is rough: sampling noise (common-seed transport), combinatorial ordering (Fiedler seriation), and long-only kinks (kept interior).

Estimators

Every method has a batch estimator and a streaming twin (Streaming…) for a changing universe.

Batch	Streaming	Kind	Notes
ThurstonePortfolio	StreamingThurstone	novel	ability tilt; common-seed transport
SchurComplementary	StreamingSchur	novel	Schur `gamma` coupling; Fiedler seriation
HierarchicalRiskParity	StreamingHRP	standard	HRP; the `gamma=0` case of Schur
RiskParity	StreamingRiskParity	standard	equal risk contribution; interior, warm-started
MinimumVariance	StreamingMinimumVariance	standard	`Σ⁻¹1`; `shrinkage`; signed
MaximumDiversification	StreamingMaximumDiversification	standard	`Σ⁻¹σ`; signed
MaximumDecorrelation	StreamingMaximumDecorrelation	standard	min-variance on the correlation; signed
MeanVariance	StreamingMeanVariance	standard	tangency `Σ⁻¹μ`; μ from EWMA mean or supplied
EqualWeight	StreamingEqualWeight	standard	`1/n`
InverseVariance	StreamingInverseVariance	standard	`w ∝ 1/σ²`

Usage

pip install allocation

from allocation import SchurComplementary, RiskParity, MinimumVariance

SchurComplementary(gamma=0.5).fit(returns).weights_    # novel
RiskParity().fit(returns).weights_                     # standard
MinimumVariance(shrinkage=0.1).fit(returns).weights_

# streaming, over a changing universe
from allocation import StreamingSchur
est = StreamingSchur(gamma=0.5)
for x in stream:               # x = {asset_id: return}
    est.learn_one(x)           # assets may enter / leave
    w = est.predict_one()      # {asset_id: weight}

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 — partial_fit, a keyed dynamic universe (in the spirit of precise), and turnover control by construction — while staying API-compatible so each estimator can be contributed upstream.