Bloomberg has introduced BQuant Assistant, a conversational AI coding tool integrated into its BQuant analytics platform, enabling financial professionals to use natural-language instructions to develop investment strategies, query financial datasets and build analytical applications without requiring extensive programming expertise.
Available through BQuant Desktop and BQuant Enterprise, the assistant operates within the platform’s existing Python development environment, supporting users across investment research, quantitative analysis and portfolio management. It can generate and debug Python code, construct queries using Bloomberg Query Language (BQL) and PyBQL, and create data visualisations using open-source libraries including Plotly and Jupyter Widgets.The new functionality provides access to Bloomberg data spanning more than 22,000 fields across equities, fixed income, foreign exchange, commodities, derivatives and other asset classes. These include pricing, fundamentals, valuations, risk metrics, estimates and economic indicators.
Unlike general-purpose coding assistants, BQuant Assistant draws on Bloomberg’s own documentation, example notebooks and technical libraries to provide responses informed by the platform’s analytical workflows. Users can also ask the assistant to explain existing code or suggest modifications to ongoing projects.
The assistant can insert and execute code directly within an active BQuant notebook, although users must approve proposed actions before the system reads from or writes to their projects. Execution takes place within BQuant’s secure sandbox environment.
“Financial professionals are working with increasingly large and complex datasets, but turning an investment idea into a repeatable analysis can still require significant time and specialized coding knowledge,” says Shawn Edwards, Bloomberg’s Chief Technology Officer.
Extending BQuant’s Analytical Capabilities
BQuant has evolved into an established environment for combining Bloomberg’s financial information with proprietary datasets and open-source analytical tools. The platform was named the Best AI Solution for Historical Data Analysis in A-Team’s AI in Capital Markets Awards 2025, recognising its capabilities for handling large-scale financial datasets and supporting quantitative research.
Its development has included Bloomberg’s collaboration with Man Group to integrate ArcticDB, the Python-native database designed for high-volume time-series analysis.
The latest enhancement also complements Bloomberg’s broader efforts to make financial data accessible through AI-driven workflows. These include ASKB, its conversational interface for the Bloomberg Terminal, and Bloomberg Enterprise Model Context Protocol (MCP), which provides AI agents with governed access to licensed market data.
As Tony McManus, Global Head of Enterprise Data and Indices at Bloomberg, recently discussed with Market & Alt Data Insight, Bloomberg’s Enterprise MCP initiative emphasises the importance of metadata, semantic context and entitlements in supporting reliable institutional AI applications.
BQuant Assistant extends this approach into the analytical development process, potentially broadening participation in quantitative research while allowing experienced developers to automate routine coding tasks.
Bloomberg announced general availability for BQuant Enterprise and a limited beta release for BQuant Desktop at the end of October 2026.
Subscribe to our newsletter
