Shadow Ingest API Reference¶

shadow-ingest exposes nine stable public Python APIs.

The SDK is intentionally split into:

Before You Run The Examples¶

Use the setup here first:

API	Use it for	Return type
`gather_daily_price(...)`	daily price history over `trade_date x stock_code`	`polars.DataFrame`
`gather_daily_snapshot(...)`	one-date cross-sectional market snapshot	`polars.DataFrame`
`gather_financial_snapshot(...)`	latest financial snapshot by statement type	`polars.DataFrame`

API	Use it for	Return type
`list_fields(...)`	discovering supported `fields` values for `gather_daily_price(...)`	`list[str]`
`list_market_calendar(...)`	discovering valid trading dates	`list[datetime.date]`
`list_universe(...)`	discovering valid stock codes for a specific date	`list[str]`

API	Use it for	Return type
`get_industry_standards()`	listing the available industry classification standards	`list[str]`
`get_industry_mapping(...)`	mapping stock codes to industry rows for one standard and date	`polars.DataFrame`
`get_industry_members(...)`	mapping an industry code or name to member stock rows	`polars.DataFrame`

If you are not sure what to query yet, use the APIs in this order:

use gather_daily_price for time-series daily price history
use gather_daily_snapshot for one-date cross-sectional market snapshots
use gather_financial_snapshot for the latest financial snapshot by statement type
use list_fields when you want the documented supported price fields
use list_market_calendar when you need valid trading dates
use list_universe when you need valid stock codes for a date
use get_industry_standards when you need the available taxonomy names
use get_industry_mapping when you already have stock codes and need their industry rows
use get_industry_members when you have an industry and need its member stock rows

The SDK is dataframe-first with polars.DataFrame as the default return type for the table APIs.

If you prefer pandas:

pdf = df.to_pandas()