Frequently asked questions

Questions that we are often asked by our 🚀 clients.

Why Ganymede as JupyterLab environment name?
Ganymede is one of Jupiter moons and represents the largest moon in our solar system and the only moon with its own magnetic field.
Which data vendors do Systemathics works with?
We continually partner with exchanges and data vendors to provide a representative coverage of trusted financial data. Our robust workflow enables us to have a provider-agnostic approach and better meet our clients expectations: collect raw provider data, normalise, cross-validate and make available on-demand data.
Do you support tick by tick data?
Yes, processing tick-by-tick data is a natural result as our mother company provides automated trading solutions covering the whole investment process from strategy selection to orders execution, through backtest and execution simulation on tick by tick data.
Which asset classes do you cover?
We have a multi-asset approach including including: Equities, Futures, Forex, …
Can I use my own dataset within your solution?
Yes, our open and secure data workflow ensures seamless integration of new datasets. Client dataset will be integrated as a provider data; normalized, cross-validated with trusted data sources and made available in a secure manner to respect client privacy and data ownership.
Can I call your API directly from our internal tools?
Yes, we provide several easy ways to use our data API using the the language of your choice. In addition to the web-portal access where the API is available within a jupyterlab environment, we provide direct access to the API from your internal and 3rd party tools.
How can I use your data solution?
We provide instant access to the on-demand data based on your request. Please do not hesitate to reach out and provide few coverage elements: asset classes, exchanges, data types (tick data, reference data, corporate actions, …).
How can I adjust my subscription?
We provide a flexible subscription to historical on-demand data. Our approach consists on defining a watchlist coupled with a history depth so you access the data you need in a normalized and validated format that enables bespoke calculations, analytics and seamless integration within your own workflow.