fedfred
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fedfred is a feature-rich Python client for the Federal Reserve Economic Data (FRED) API, designed to make working with economic time series ergonomic and production-ready. It provides full coverage of the FRED, ALFRED, and GeoFRED/Maps endpoints behind a clean, typed interface, with first-class support for returning data as pandas, polars, or dask objects (and GeoDataFrames for geospatial series). Built for serious use, it includes both synchronous and asynchronous clients, built-in rate limiting and caching to respect API limits, and defensive type-safe handling of responses, making it suitable for everything from interactive research notebooks to automated data pipelines.
cultivars
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cultivars is a research-grade Python SDK for autoregressive time-series modeling, built to cover the Bayesian and time-varying methods that existing Python tooling handles poorly or not at all. Where reduced-form VAR and ARIMA are treated as table stakes, its focus is the harder surface: Bayesian VAR at scale (Minnesota, NIW, SSVS, horseshoe, and hierarchical priors), time-varying-parameter VAR with stochastic volatility, and structural identification beyond Cholesky — sign, narrative, and proxy/IV schemes passed as composable strategy objects rather than bespoke classes. Every model, from univariate AR through TVP-SVAR-SV, composes through a single state-space substrate and decomposes into an immutable Spec, a transient Estimator, and a serializable Result, giving a typed, dataclass-shaped API with consistent fit, forecast, IRF, and FEVD surfaces. Designed to integrate directly with fedfred and edgar-sec, it forms the modeling layer of a FRED-to-model-to-analysis stack that no other Python library currently offers.
edgar-sec
