Dataset metadata

Use liberator.metadata() to fetch concordance-backed metrics for one or more datasets — row counts, symbol counts, and start/end timestamps — without downloading source rows. This is a Liberator 2.3+ API. It calls POST /liberator/metadata and is separate from liberator.query() / liberator.get_dataframe(). Do not pass summary or symbol to query; those parameters belong only on metadata.

The Python SDK helper ships with Liberator 2.3 client packages. Download the latest liberator.py from your Liberator Profile page if your client does not yet expose liberator.metadata.

Parameters

Parameter	Description	Type	Default
`name`	Dataset name, or a list of dataset names	String or list	— (required)
`summary`	Collapse the per-day dimension into a whole-history summary	Boolean	`false`
`symbol`	Return per-symbol grain instead of per-dataset totals	Boolean	`false`
`symbols`	Restrict metrics to this symbol or list of symbols (also used for entitlement checks)	String or list	`null`
`back_to`	Beginning of the data-time window	Date string, microseconds UTC, or `null` (no lower bound)	`null`
`as_of`	End of the data-time window	Date string, microseconds UTC, or `null` (now)	`null`
`user`	Your CloudQuant-assigned user identifier	String	from `liberator.json` / env
`token`	Your authentication token	String	from `liberator.json` / env

Grain matrix (`summary` × `symbol`)

`summary`	`symbol`	Result grain
`false`	`false`	Per day, dataset totals
`false`	`true`	Per day, per symbol
`true`	`false`	Whole-dataset totals
`true`	`true`	Whole-history, per symbol

Basic example

import liberator

liberator.url = "https://api.cloudquant.ai"

metrics = liberator.metadata(
    name="daily_bars",
    summary=False,
    symbol=False,
)

# list of metric dicts, for example:
# [{"dataset": "...", "date": "...", "symbol_count": ..., "row_count": ..., "start": ..., "end": ...}, ...]
print(metrics)

Whole-dataset summary

metrics = liberator.metadata(
    name="daily_bars",
    summary=True,
    symbol=False,
)

Per-symbol metrics

# Per day + per symbol
daily_by_symbol = liberator.metadata(
    name="daily_bars",
    summary=False,
    symbol=True,
    symbols=["AAPL", "MSFT"],
)

# Whole-history per symbol
history_by_symbol = liberator.metadata(
    name="daily_bars",
    summary=True,
    symbol=True,
    symbols=["AAPL"],
)

Multiple datasets

metrics = liberator.metadata(
    name=["daily_bars", "minute_bars"],
    summary=True,
    symbol=False,
)

Windowed metadata

metrics = liberator.metadata(
    name="daily_bars",
    summary=False,
    symbol=False,
    back_to="2025-01-01",
    as_of="2025-01-31",
)

Return shape

liberator.metadata() returns a list of metric dictionaries (the metrics array from the streamed response). Typical fields include:

Field	Meaning
`dataset`	Dataset name
`date`	Calendar day (present when `summary=False`)
`symbol`	Symbol key (present when `symbol=True`)
`symbol_count`	Distinct symbols in the grain (when applicable)
`row_count`	Row count for the grain
`start` / `end`	Timestamp bounds for the grain

The HTTP response is streamed NDJSON (heartbeat frames, then a final {"metrics": [...]} object). The Python helper consumes heartbeats and returns only the metrics list.

Listing datasets — catalog, schema, and entitlement discovery via liberator.datasets
Query parameters — row-level queries via liberator.query (not metadata)
Checking dataset access — Python guide for exploring entitled datasets

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Excel Plug-In

Dataset Metadata

Dataset metadata

Parameters

Grain matrix (`summary` × `symbol`)

Basic example

Whole-dataset summary

Per-symbol metrics

Multiple datasets

Windowed metadata

Return shape

​Dataset metadata

​Parameters

​Grain matrix (summary × symbol)

​Basic example

​Whole-dataset summary

​Per-symbol metrics

​Multiple datasets

​Windowed metadata

​Return shape

​Related

Dataset metadata

Parameters

Grain matrix (`summary` × `symbol`)

Basic example

Whole-dataset summary

Per-symbol metrics

Multiple datasets

Windowed metadata

Return shape

Related