Liberator filtering can also provide near full SQL functionality, enabling sophisticated dataset analysis, including the ability to span and combine datasets. This is an expert level feature at the moment.
Enterprise SQL-Style Server-Side Filtering: Unleash Advanced Analytics
Sophisticated SQL Power including Multi-Dataset Querying
Liberator's advanced SQL-style server-side filtering delivers enterprise-grade query capabilities that enable complex financial analysis with the full power and flexibility of SQL syntax. This sophisticated feature transforms how institutional users approach large-scale data analysis and cross-dataset research. Queries can now also span multiple datasets.
Revolutionary Capabilities
Full SQL Functionality: Leverage the complete SQL feature set including JOINs, UNIONs, aggregations, and advanced analytical functions to perform sophisticated calculations server-side.
Multi-Dataset Integration: Execute complex queries across multiple data sources simultaneously in a single, powerful operation. For example, this could allowing a user the ability to combine equity trades, options data, market quotes and other alternative data into a single query.
Expert-Level Performance: Designed for quantitative analysts, portfolio managers, and data scientists who require maximum flexibility and computational power for complex market analysis.
Example
-- Cross-dataset analysis combining multiple time periods and data sourcesfor batch in liberator.query(sql = """
select sum("<TRADE.VOL>") as total_trade_volume
from bids_quotes_trades
where symbol='SPY'
and muts >= 1750080600000000 and muts < 1750104000000000
union all
select sum(openinterest) as total_open_interest
from options_close_marks
where symbol='SPY'
and muts >= 1750089600000000 and muts <= 1750080600000000
"""):
print(batch.to_pandas())
This example demonstrates the power of cross-dataset querying by simultaneously analyzing trade volumes and options open interest across different time periods, consolidating complex market data into actionable insights.
Strategic Advantages
Computational Efficiency: Execute complex aggregations and calculations server-side, dramatically reducing data transfer and local processing requirements.
Analytical Flexibility: Combine disparate datasets with sophisticated logic, enabling comprehensive market analysis that would be impossible with traditional single-dataset approaches.
Scalable Architecture: Handle enterprise-scale queries across terabytes of financial data with optimized server-side processing.
Future-Ready Design: Built with AI integration in mind, ensuring your investment in advanced analytics capabilities will scale with emerging technologies.