QuantOffice
Cloud
Details, Documentation and GUI
Algo Trading
Flexible and powerful environment for creating and running of custom trading bots in Jupyter Notebooks.
Trade All Major Currencies on Popular Venues
Trade all popular cryptocurrencies on dozens of trading venues.
Back-Testing and Paper Trading
Back-test trading bots before going live on a proprietary execution simulator. Run trading bots in a live mode using simulated accounts.
FinMath Libraries and Reports
Get access to a proprietary advanced financial library (FinMath) in Java and .NET for time-series data analysis and reporting.
Risk Rules and Monitoring
Set up risk limits at a trading bot level, currency/asset level, and at system level. Monitor trading strategies, active orders, and P&L in real-time.
VPS Deployment and Support
Run trading bots on high-performance and secure virtual private servers on highly available datacenters.
Event-Drivel Model with Flexible API
Event-driven API lets you take immediate actions based on market events.
High Performance Proven Back-End
Take advantage of QuantOffice's powerful back-end components written in Java and C#.
About QuantOffice
QuantOffice Cloud is a specialized, Web-based environment for the development, prototyping, back-testing of different execution strategies. Integration with Jupyter Notebook provides an advanced, flexible, and user-friendly environment to program trade strategies in Python programing language, optimize, test and deploy them to production. QuantOffice provides flexibility to create custom trading strategies or use strategies supplied by Deltix. You can easily back-test your strategy on historical data before going live. For live trading, QuantOffice provides ready-to-go integrations with more than 30 trading venues and numerous market data vendors covering all major traditional and crypto asses classes. You can use our trade simulator to run your test your strategy on live data before switching to the real trading account.
Trading Bots
Trading Bots are algorithms implemented on Python that allow to execute a specific trading strategy.
Integration with Jupyter Notebook provides an advanced, flexible, and user-friendly environment to program trade strategies in Python programing language, optimize, test and deploy them to production. You can also use the Thea plugin to develop, adjust and debug execution strategies without leaving JuputerHub.
Use Thea IDE plugin right within JupyterHub to develop, optimize and debug your strategies.
Run your strategies on historical data supplied by TimeBase. Execute strategy in back-testing mode from Jupyter Notebook, see reports, visualize, edit code.
Run your strategy on live data that is supplied by numerous market data connectors provided by QuantOffice out-of-the-box. Use our trading simulator to test strategy on real-time data before going live. Execute paper trading from Jupyther Notebook, see reports, visualize, edit code.
QuantOffice provides trade connectors to more than 30 exchanges. Having back-tested your strategy and run it on live data in paper trading mode, you can seamlessly shift to live trading right from Jupyter Notebook.
You can monitor your strategies' performance in real-time using Execution Server Monitor and Strategy Server Monitor Web applications.
