Tick database
Time series databases are typically optimized for metrics data but lack the essential features required to ingest, store, and analyze financial markets data — for example, the ability to scale horizontally and manage sparse data across a range of different securities. Goldman Sachs has invested significant resources into developing a horizontally scalable, in-memory time series database that is optimized for financial markets as it can handle thousands of updates per second across millions of securities at millisecond latency. We are making the same technology used everyday across our trading floor available to our institutional clients.
Data modeling and integration
Standardized and normalized financial reference data is a foundational building block of any financial analytics engine. Our market data engineers, experts in market microstructure, company lineage, and corporate actions processing, have built a unifying framework to map data into a consistent data model. You can now discover reference data and market data from a centralized data catalog. Access and analyse it in your development environment with GS Quant, our open source Python SDK for financial analytics.
Deep history
Easily backtest your trading strategy and validate your risk models by accessing over 10 years of historical data.
Standardization
Our Global Equities reference data maps across a variety of identifiers.
Consistent data models
A common format across data sources, based on proprietary internal data models and our first hand experience trading the underlying securities products.
EXPLORE OTHER DATA SOLUTIONS
Curated Data
Access institutional-grade GS-curated financial markets data, including select third-party data products sourced via AWS Data Exchange – all mapped to a consistent data model.
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Solutions
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