Bloomberg has introduced a new enterprise-level economic dataset designed to improve how quantitative investors model macroeconomic conditions and test trading strategies, expanding its suite of investment research data products for systematic and data-driven workflows.
The new Economic Releases and Surveys Point-in-Time (PiT) dataset provides time-stamped macroeconomic information across more than 100 economies, offering historical records of how data was originally released and subsequently revised. The dataset includes over 3,000 global economic indicators and government auction events, with coverage extending back to 1997.
The key aim is to help systematic investors more accurately reconstruct historical market environments, a long-standing challenge in macro-driven quant strategies where revisions and shifting consensus expectations can distort backtests when not properly accounted for.
By capturing economic releases, survey expectations and revision histories as they were known at specific points in time, the dataset allows investors to better align backtested signals with real-world decision conditions faced by markets at the moment of data release.
Bloomberg said the dataset is designed to support macro strategy development across asset classes including equities, fixed income and foreign exchange, where expectations and surprise dynamics often drive short-term price action.
According to Bloomberg’s global head of investment research data, the product enables more robust modelling of how markets form and adjust expectations in response to incoming macro information, supporting both signal generation and strategy validation within systematic investment frameworks.
The dataset includes three main components: forward-looking calendars of scheduled macro events, point-in-time actuals and consensus forecasts with full revision history, and updates to economist survey expectations as they evolve ahead of official releases. Each data point is enriched with metadata such as geography, economic category and indicator classification to support cross-market analysis.
The launch is integrated with Bloomberg’s broader real-time macro data infrastructure, ensuring consistency between historical datasets used for backtesting and live market data used in execution environments. The firm said this alignment is intended to reduce discrepancies that can arise when strategies transition from research to production.
The new offering sits within Bloomberg’s wider Investment Research Data suite, which includes financial fundamentals, estimates, pricing history, transaction analytics and sector-specific datasets aimed at supporting systematic investing workflows across global markets.
