keyed-dynamic-universe

name	keyed-dynamic-universe
description	Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.

Keyed covariance over a changing universe

In streaming/finance settings observations are dicts keyed by name, and the set of names changes. keyed decorates any positional estimator to consume keyed dicts and emit keyed output.

pip install precise            # add [pandas] for to_frame()

from precise import keyed, EwaCovariance

est = keyed(EwaCovariance(r=0.05), dynamic=True)     # universe may change over time
est.partial_fit({"BTC": 0.01, "ETH": -0.02})         # river-style: also .update / .learn_one
est.partial_fit({"ETH": 0.00, "SOL": 0.03})          # BTC drops out, SOL enters

est.covariance_["ETH"]["SOL"]                         # dict-of-dicts over the live universe
est.to_frame()                                        # pandas DataFrame  (needs [pandas])

Fixed vs dynamic

• keyed(est) / dynamic=False → FixedUniverse: one wrapped estimator; missing keys are imputed. Use when the set of names is stable and you just want dict ergonomics.
• keyed(est, dynamic=True) → DynamicUniverse: tracks multiple live key-sets with staleness / longevity eviction and assembles a pairwise matrix. Use when names genuinely enter and leave.

Notes

• The adapter adds no covariance math of its own — it wraps the positional estimator, so any estimator from the estimate-online-covariance skill works inside it (LedoitWolfCovariance, HuberCovariance, …).
• The assembled matrix is projected to the nearest PSD; expect small adjustments when the live universe changes.
• To score/compare keyed estimators, extract the dense covariance_ (e.g. via to_frame().values) and use the score-covariance-estimate skill.