"""
US Congress Trading Disclosures (PIT) — collection recipe.

Contract (ziplime PIT recipe, design doc §9):

    async def fetch(since: datetime) -> pl.DataFrame

Returns rows in the PIT schema for `congress-trading`:
  system:  entity_id, event_date, knowledge_date, knowledge_estimated
  values:  representative, chamber, transaction_type, asset_ticker, amount_low, amount_high, disclosure_lag_days

The shared harness (ingest.py) stamps `ingested_at`, dedups against existing rows and
appends to the Delta bundle. This recipe only fetches and shapes.
"""
from __future__ import annotations
from datetime import datetime, timezone


async def fetch(since: datetime):
    import polars as pl  # noqa: F401
    import httpx
    import polars as pl

    # House: disclosures-clerk.house.gov  |  Senate: efdsearch.senate.gov
    rows = []
    for chamber, endpoint in CHAMBER_ENDPOINTS.items():
        resp = httpx.get(endpoint, params={"since": since.date().isoformat()}, timeout=60)
        for f in resp.json()["filings"]:
            rows.append({
                "entity_id": f["ticker"],
                "event_date": f["transaction_date"],
                "knowledge_date": f["filed_at"] or _statutory_cap(f["transaction_date"]),
                "knowledge_estimated": f["filed_at"] is None,
                "representative": f["member"],
                "chamber": chamber,
                "transaction_type": f["type"],
                "asset_ticker": f["ticker"],
                "amount_low": f["amount_range"][0],
                "amount_high": f["amount_range"][1],
                "disclosure_lag_days": f["lag_days"],
            })
    return pl.DataFrame(rows)


if __name__ == "__main__":
    import asyncio, polars as pl
    df = asyncio.run(fetch(datetime(2025, 1, 1, tzinfo=timezone.utc)))
    print(df.head())