14 June 2021
Strategic technological choices and workflow decisions need to be made all along the financial data lifecycle. In this article, we are delivering our feedbacks and sharing our experience on dealing with different financial data types and coming from multiple sources.
Financial data management can be costly and time consuming especially when incorporating multiple data types such as reference data, corporate actions with daily/tick data or when dealing with multiple sources. Even when you find out a suitable solution, you might encounter tight technical parameters, limited customization ability or non-smooth integration:
- You might be limited in term of input and output parameters: requests in time, tickers per request, replies volume, non-flexible symbology used to request data
Lack of customization
- Your use-cases are specific and you might need extensive customization capabilities to best meet your requirements: analytics, simulation, visualization...
- Even when you overcome technical limitations, you might still be unsatisfied with data formats:: not normalized, not event-driven and hard to intergate within your workflow
These are few reasons why you should challenge your financial data workflow:
- In-house solution requires heavy investments in addition to development/maintenance costs and lack of independence
- Third party vendors may suggest data offers but how customizable can it be? and how long is your time-to-market?
- Being tied to one vendor may limit your flexibility, competitivity and ability of adding value to internal/external clients
We help you focus on your core business, level-up your R&D data driven projects and bring your ideas live. Our data analytics and visualization solutions rely on the following points:
- Immediately available solutions: web cloud based JupyterLab environment and API
- Fully-managed from data collection, cross-validation and storage to exploitation features
- High level of features customization, capacity to ineract with internal systems and to integrate client data
No technical limits
- Create unlimited requests in term of tickers, requests count over the time and look back period. Retrieve deep history data using our API
Customizable and extensive
- Benefit from wide range our analytics, calculation and transformation extensive services to support your investment and risk management
- Integrate internal or vendor data of your choice and use our data solutions to seamlessly integrate analytics and bespoke datasets within your workflow
Our fully managed solutions ease data access and make available intuitive tools to design analytics and seamlessly integrate bespoke datasets within your own workflow. For immediate integration, please contact our team to learn more about Ganymede, our JupyterLab web cloud environment and our API:
Web access to on-demand data
Fully managed web portal making available on-demand financial data and analytics from trusted sources. Wide range of extensive data calculation and visualization services within a JupyterLab environment. Intuitive features and rich documentation to build on provided samples.
API access to on-demand data
Use your preferred implementation language to call our API and seamlessly integrate normalized datasets within your workflow. Wide range of extensive data calculation and transformation services to support you investment and risk management. Up-to-date documentation and building blocks.
We are continually partenering with data vendors and dealing with heterogenous data offers which helps us building the right solutions to overcome financial data challenges. Please do not hesitate to discover integrated data vendors and to request partners integration calendar.