finscrapers 0.1.0

Ready-to-run FinField scrapers: SEC EDGAR fundamentals, Stooq equity prices, CoinGecko crypto — pure Python, stdlib HTTP, normalized to FinFacts

finscrapers

Source adapters that turn open financial data into deterministic finfacts — scaled integers, per-fact provenance, byte-identical CIDs on every node that reads the same upstream bytes.

Ready scrapers

kind	coverage	rate / ToS notes
sec-companyfacts	US fundamentals — every XBRL fact filed by every US-listed reporter (audited, full history, per-fact accession provenance), public-domain JSON from data.sec.gov	SEC asks for <=10 req/s and a descriptive User-Agent (both built in: default throttle 0.12 s, UA with contact); companyfacts.zip bulk mode for full-universe ingest
stooq-eod	Equity end-of-day close/volume — US, DE, GB, JP, HU, PL composite tickers, plain CSV with exact decimal strings	stooq.com free tier, no API key; be polite, cache aggressively
coingecko-market	Crypto daily close/volume (USD) for the liquid core (BTC CRYPTO, ETH CRYPTO, …; pass id_map for the long tail); JSON parsed with parse_float=Decimal, no float round-trip	CoinGecko free tier, no API key, low rate limits — cache and space out calls

Crypto (and any cross-venue) prices are continuous quantities: two nodes scraping at different moments legitimately mint different facts for the same day. Each observation is published as-is; the field converges through vank voting in finknit.vote, never by pretending the number was exact.

Adding a scraper: the FactSource contract

from finscrapers import FactSource

class MySource(FactSource):
    kind = "my-source"                                  # unique source id

    def covers(self, entity) -> bool: ...               # can I supply facts for it?
    def fetch(self, entity):                            # -> FactSet | None
        ...  # normalize upstream numbers via to_scaled — never floats

Register it in finscrapers.registry.READY (or pass your own dict to the runner). all_sources(cache_dir) instantiates every ready scraper, each with its own cache subdirectory.

Install & fetch

pip install "finscrapers @ git+https://github.com/FinField/scrapers"   # pulls finfacts

from pathlib import Path
from finscrapers import SecEdgarSource
from finfacts.model import Entity

src = SecEdgarSource(cache_dir=Path("~/.cache/finfield/sec").expanduser())
fs = src.fetch(Entity(ticker="AAPL US", cik="320193"))
print(len(fs.facts), fs.facts[0].cid)   # audited XBRL facts, ff1:… CIDs

On 5mart.ml/finfield these scrapers run unattended as knitting agents via agents: each agent fetches, derives, and weaves signed facts into the pulse fabric on a schedule.

Part of the FinField field: facts · knit · agents · signals · crypto