| """ |
| Macro Indicators — Vintage / PIT (FRED-style) — collection recipe. |
| |
| Contract (ziplime PIT recipe, design doc §9): |
| |
| async def fetch(since: datetime) -> pl.DataFrame |
| |
| Returns rows in the PIT schema for `macro-indicators`: |
| system: entity_id, event_date, knowledge_date, knowledge_estimated |
| values: series_value, unit, native_frequency, release_kind |
| |
| 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 |
| import httpx |
| import polars as pl |
|
|
| rows = [] |
| for series in SERIES_CODES: |
| |
| obs = httpx.get(ALFRED_URL, params={"series_id": series, "since": since.date().isoformat()}, |
| timeout=60).json()["observations"] |
| for o in obs: |
| rows.append({ |
| "entity_id": series, |
| "event_date": o["period"], |
| "knowledge_date": o["realtime_start"], |
| "knowledge_estimated": False, |
| "series_value": o["value"], |
| "unit": o["unit"], |
| "native_frequency": o["frequency"], |
| "release_kind": o["release_kind"], |
| }) |
| 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()) |
|
|