KX Accelerator for Bloomberg BPIPE Overview

This page provides an overview of the KX Accelerator for Bloomberg BPIPE, use cases, and architecture and features.

Introduction

This Accelerator brings Bloomberg BPIPE data to kdb Insights Enterprise, coupled with Bloomberg EMRS-aware query and analytics capability. All ingested tick-level data is fully accessible and can be further queried using SQL, analyzed with enhanced Accelerator APIs using Python, or accessed from any downstream application through REST. Entitlements are applied on the Accelerator APIs as well as standard kdb Insights Enterprise getData queries.

The entire Accelerator can be configured and launched on an existing kdb IE installation in minutes. Quants, business users, and developers can get instant access to high quality Bloomberg data, paired with the power and performance of kdb Insights Enterprise, all without a lengthy implementation project.

Use cases

The Accelerator supports capital markets use cases where real-time data and tick-by-tick historical data is required, such as:

  • Real-time visibility and analytics

  • Pattern & trend analysis

  • Backtesting

  • Quantitative research

  • Pre and Post-trade anlaytics

Architecture and features

The KX Accelerator delivers a scalable, performant architecture including:

  • A pipeline for ingesting L1 equities data from the KX BPIPE feed handler: bbg-realtime-l1-pipeline

  • A pipeline for ingesting entitlement updates from the KX EMRS feed handler: bbgentitlements-pipeline

  • A pipeline for kdb Insights Enterprise to EMRS user mapping: bbgentpair-pipeline

  • Schemas for Quote and Trade data for the above

  • Schemas for EMRS updates, the latest entitlement snapshot, and user ID mapping

  • Standard FSI Accelerator analytic and query APIs available through the FSI Library

  • kdb Insights Enterprise entitlement filter functions to apply EMRS restrictions

  • An Insights View that includes tick and statistic retrieval, charting

  • An example Jupyter notebook, demonstrating Python and REST API access

Further details

For more information, refer to the following documentation: