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Screenshot of the Esther Cloud IDE, showing the Compiler and Esther code
Esther Compiler

Esther Compiler

Esther empowers clients across the full spectrum of Financial Services to develop and maintain proprietary risk and pricing analytics with unparalleled simplicity and numerical efficiency.


Esther's modelling language is a superset of a general-purpose programming language. It is based on language extensions that can be nested on top of the user's favourite language specification. We currently support C# as the base language and are planning to release support for TypeScript and Python, but other languages such as Java or Scala are also supportable in priciple. The addition of new languages does not impact the Esther Solver.


The Esther language extensions enable the user to formulate all problems in the Pricing and Risk domain in full generality. Modelers using Esther focus on the business logic (e.g. pay-offs, model parameters, aggregations, etc.) and delegate the complexities of model solution and orchestration to the Esther Solver platform.


The Esther Compiler is accessible through a web browser interface and supports debugging of all user codes, including the automatically generated GPU server logic. It also provides an integrated development environment for building bespoke models for applications such as derivatives, structured products, and counterparty credit risk. Esther Compiler is designed to make it easy for financial services professionals to quickly develop, debug and deploy models.

Esther Solver

Esther Solver

Mathematics and the low-level model execution are abstracted away from the model developer.  Esther takes care of the entire execution process using its universal Solver for all risk and pricing models emitted as intermediate language by the Esther Compiler. This means that quants do not have to spend time on performance optimisation.

The Esther Solver is entirely generic and does not rely on any model specific mathematical short-cuts such as solutions expressed as special functions or PDE solvers using sparse or banded matrix algebra. Esther Solver instead calculates all model scenarios for all time steps in full and accurately, without approximations. Furthermore, with its highly efficient memory architecture, scenarios can be retained for further analysis, such as reverse stress testing.

Both Esther Compiler and Esther Server are available in a Docker container and can be accessed via either a SaaS service from a public cloud providers or as a local installation with direct licensing. Hardware requirements for Esther solver range from Enterprise AI servers to professional laptops with high-end GPUs. This makes it easy to embed the Esther solver in the model development workflow.

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