Starling is a commercially friendly open source pricing and market risk platform built upon on the OpenGamma 2.x platform as used in CCPs, hedge funds, broker dealers and pension funds around the world.
Starling evolved organically from our customer's requirements for a stable, fully featured platform with an open source codebase.
Starling's highlights are:
- Fully featured and highly integrated analytics library covering a wide range of asset classes
- A calculation engine that can support both streaming and on-demand calculations
- Persistent storage of trades, securities, portfolios, legal entities and more
- The ability to use live market data, historical time series or files as a market data source
- A simplified API and workflow to ease integration
- A validation framework for configurations and conventions
- Increased modularization to better support embedding
- Pre-built Docker containers
- Bug fixes
A new, much simpler way to get your positions and market data into Starling. The focus is now on ease of use and getting valuable results quickly and make extracting those results as simple as possible.
The great asset class coverage of the OpenGamma analytics library.
Stability and backwards compatibility are paramount. With Starling you will not see API entry points be removed outside of exceptional circumstances (such as security vulnerabilities).
Ability to embed in existing applications with minimized dependencies - web and remoting configurations are now optional allowing Starling to be embedded easily in JEE applications.
Support for Quandl as a data source. Historical data can now be easily imported from Quandl, a free and low-cost subscription premium data provider.
Support for the FinMath analytics library, a Java quant library developed by Christian P. Fries in Germany. Aside from adding functionality, it makes an excellent example of supporting an external analytics library.
Pre-built Docker containers. Get multi-server installations up and running more quickly and cloud deploy in minutes.
We welcome and encourage developers to contribute support for new analytics libraries, asset classes and features. Please email us or visit GitHub to get involved!