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
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edgar-sec is a research-grade, fully-typed Python SDK for the entire SEC EDGAR REST API — the structural twin of fedfred for federal securities-disclosure data. It provides ergonomic, discoverable access to the full endpoint surface (submissions, company concepts, company facts, frames, and CIK/ticker resolution) through a small public front door, with every response returned as a typed dataclass rather than a raw dict for IDE autocomplete and mypy safety. Built to the same quality bar as its sibling, it offers strict parity between synchronous and asynchronous clients, a built-in rate limiter that respects EDGAR’s fair-access rules, optional local caching, and defensive type-safe handling of responses throughout. Distributed on PyPI, conda-forge, and Anaconda, it is designed as a durable data-infrastructure layer — the foundation for planned higher-order tooling like an XBRL parser, a pandas-subclassed financial dataframe, and a taxonomy×value×time tensor representation of filings.
ns-sdn
COMING SOON
toros
toros is a pandas-backed data representation library that turns SEC filing data into self-validating financial objects. It uses registered DataFrame accessors to enforce financial semantics — type, unit, period, and taxonomy constraints — directly within the pandas backend, and layers specialized frame types (such as financial-statement representations) on top through inheritance. It serves as the interactive, semantically-aware data container that edgar-sec materializes filings into, giving researchers pandas ergonomics with domain-correct structure and validation rather than raw, untyped tables.
autonomous-fed
Autonomous Fed is a reinforcement learning research project that reframes monetary policy as a control problem, in which an RL agent learns the central bank’s reaction function rather than having a fixed Taylor rule imposed on it. Benchmarked against Hinterlang & Tänzer’s Bundesbank work (Discussion Paper No. 51/2021), it replicates their RL environment and extends it by training policy agents inside two distinct economic environments — a linear one built on TVP-SVAR and a nonlinear one built on the NSSM state-space formulation that links to the NS-SDN project — then comparing learned policy behavior across both. The aim is a clean replication-plus-extension contribution targeting Computational Economics, demonstrating how optimal monetary policy under uncertainty changes when the agent’s model of the economy moves from linear structural dynamics to a richer nonlinear spectral state space.
