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.
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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])
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.LedoitWolfCovariance, HuberCovariance,
…).covariance_ (e.g. via to_frame().values) and use
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Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.
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Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
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Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.
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Online (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance. Use when code needs a covariance/correlation matrix updated per observation, recomputes np.cov/np.corrcoef in a rolling loop, must judge or compare covariance estimates, or proposes a new covariance methodology. Points to task-specific skills.
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Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
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