Query parameter combinations

This guide explains the main parameters for CloudQuant Data Liberator queries and the expected results from different combinations.

Core parameters

Parameter	Description	Default
`name`	Dataset identifier (required)	—
`symbols`	Single symbol or list, e.g. `['AAPL','GOOG','MSFT']`	`None` (all symbols)
`back_to`	Query start date	`None`
`as_of`	Query end date	Current date/time
`fields`	Specify desired columns to reduce download time	All columns
`stats`	Set to `'Total'` for symbol counts instead of full results	—
`where`	Custom SQL predicate appended to the generated filters	`None`
`sql`	Full raw SQL query (replaces `name`, time range, and other construction params)	`None`

Use liberator.datasets(entitled=True) to list all datasets available to you.

See the Query Parameters Reference for the full parameter list, including superquery, N-Query, and authentication options.

Query examples

All parameters included

df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2024-07-24',
    back_to = '2024-07-16',
    symbols = ['AAPL', 'GOOGL']))

Returns data for specified symbols within the date range.

Without symbols

df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2024-07-24',
    back_to = '2024-07-16'))

Defaults to all available symbols.

Without `as_of`

df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    back_to = '2024-07-22'))

Defaults as_of to current time, returning data from present back to specified date.

Without `back_to`

df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2023-12-25'))

Returns the most recent data point per symbol as of the specified date. Look-back distance varies by dataset density.

Minimal query (name only)

df = liberator.get_dataframe(liberator.query(name = 'daily_bars'))

Returns the latest data point for all symbols in the dataset.

Custom `where` filters

Use where with a standard parameter-based query to append extra SQL predicates on top of the filters CloudQuant Data Liberator generates for time range and symbols. The clause is combined with AND — do not include the WHERE keyword.

df = liberator.get_dataframe(liberator.query(
    name='daily_bars',
    as_of='2024-07-24',
    back_to='2024-07-16',
    symbols=['AAPL', 'GOOGL'],
    where='volume > 1000000'
))

where works with time-range, LKV, and N-Query modes. Column names that match the dataset schema are automatically quoted when needed.

Multi-dataset / concordance-style filters

For datasets keyed by more than symbol (for example concordance tables with a dataset column), use where to narrow the result set:

df = liberator.get_dataframe(liberator.query(
    name='concordance',
    as_of='2025-01-01',
    back_to='2024-01-01',
    where="dataset = 'my_derived_dataset'"
))

where cannot be combined with sql. Use one approach or the other.

Raw `sql` queries

Pass a complete SQL SELECT statement via sql instead of name, back_to, as_of, and related parameters. When sql is present, parameter-based query construction is skipped.

df = liberator.get_dataframe(liberator.query(
    sql='''
        SELECT *
        FROM "daily_bars"
        WHERE muts >= 1704067200000000
          AND muts < 1704153600000000
          AND symbol = $$AAPL$$
    ''',
    user='my_user',
    system='API'
))

SQL requirements

The statement must be a SELECT (including UNION / INTERSECT / EXCEPT branches).
Every SELECT arm must include an explicit WHERE clause.
By default, the WHERE clause must include a time-range predicate on muts or another configured column (=, >, >=, <, <=, or BETWEEN). Use microsecond timestamps for muts values.
Quote dataset names with double quotes when they contain special characters.
Use $$symbol$$ dollar-quoting for string literals in filters.

Parameters allowed with `sql`

When using sql, only connection, auth, and transfer options are permitted alongside the statement — for example user, system, token, compress, json_xfer, and batch_size. Supplying non-empty values for name, back_to, as_of, where, or other construction parameters raises an error.

Raw SQL bypasses the convenience of name / symbols / back_to / as_of. Prefer parameter-based queries unless you need full control over the generated SQL.

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CloudQuant Data Liberator Python Guide

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Query Parameter Combinations

Query parameter combinations

Core parameters

Query examples

All parameters included

Without symbols

Without `as_of`

Without `back_to`

Minimal query (name only)

Custom `where` filters

Multi-dataset / concordance-style filters

Raw `sql` queries

SQL requirements

Parameters allowed with `sql`

​Query parameter combinations

​Core parameters

​Query examples

​All parameters included

​Without symbols

​Without as_of

​Without back_to

​Minimal query (name only)

​Custom where filters

​Multi-dataset / concordance-style filters

​Raw sql queries

​SQL requirements

​Parameters allowed with sql

Query parameter combinations

Core parameters

Query examples

All parameters included

Without symbols

Without `as_of`

Without `back_to`

Minimal query (name only)

Custom `where` filters

Multi-dataset / concordance-style filters

Raw `sql` queries

SQL requirements

Parameters allowed with `sql`