Why macroeconomic data has struggled in systematic strategies
Financial markets are awash with economic data—GDP prints, labour figures, inflation snapshots—yet few systematic traders use this information in a way that mirrors its true release and relevance. That’s not for lack of value; it’s a reflection of operational reality. Historical data sets are often overwritten, models are applied with hindsight, and timestamps are disregarded. The result? Systematic strategies end up ignoring what markets actually knew at the time, and “macro information waste” becomes unavoidable.
As the Macro-Quantamental Handbook points out, markets are not macro information efficient. Information goes unused due to research costs, trading restrictions, and institutional inertia. Even when research is conducted, it only impacts prices if action is taken—and that’s frequently blocked by regulation or overlooked due to processing costs. In essence, the format and usability of macro data, not its relevance, have held it back from systematic use.
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The quantamental fix: point-in-time indicators
The remedy lies in quantamental indicators—specifically, macro-quantamental ones that capture the state of fundamental macro data exactly as it was known on any given date. These are not standard economic time series. They are point-in-time (PIT) representations, constructed from data vintages that store how a full economic time series looked at a specific moment. This structure avoids look-ahead bias, enabling genuine historical backtests.
A quantamental indicator is based on two intersecting timelines:
- Real-time dates: when the data became available to the market
- Observation dates: the economic periods the data refer to
Vintages allow traders to precisely replicate what the market knew, rather than what is now available after revisions. This rigorous format supports models, econometrics, and machine learning with data that reflects true historical states of knowledge.
Beyond single series: categories, factors, and signals
The quantamental framework defines a layered structure:
- Category: A set of similar indicators across multiple countries or markets
- Factor: A composite of indicators designed to forecast returns
- Signal: A time series of trading indications derived from factors and used for risk-taking
As of December 2024, JPMaQS—a collaborative system developed by J.P. Morgan and Macrosynergy—offered 986 quantamental categories and nearly 19,000 indicators. These are made available in a coherent format for institutional traders, enabling both algorithmic and discretionary strategies.
JPMaQS: building infrastructure for macro strategy
Constructing a reliable macro-quantamental database has long been prohibitively costly. JPMaQS solves this by processing over a billion raw data points into over 35 million high-quality vintages. It ensures consistency in timestamping, revision handling, and metadata management. The result is a foundation for building trading strategies with lower cost, greater transparency, and enhanced statistical credibility.
JPMaQS also integrates a layered hierarchy of themes, groups, and tickers, facilitating multi-market strategy construction. Access is tiered—some data are freely available (e.g. T-6 month delay), while full access is licenced to institutional clients.
Where investment value is generated
According to the handbook, macro-quantamental indicators support four major strategy styles:
- Macro trend exploitation: Forecasting persistent economic trajectories across asset classes
- Detection of implicit subsidies: Identifying non-commercial drivers of returns such as central bank interventions
- Price distortion estimation: Detecting valuation gaps caused by liquidity, regulation, or structural frictions
- Tracking endogenous market risk: Managing internally generated risks like crowded positions and setback exposure
These approaches aim not just at return generation but at improving the macroeconomic efficiency of markets themselves. They help prices reflect real-world developments faster and more accurately.
Proof of concept: from raw data to signal
Developing a systematic macro strategy involves a disciplined pipeline:
- Downloading relevant quantamental time series
- Transforming data into factor representations
- Combining factors into a signal model
- Evaluating signal quality via rigorous backtesting
Tools such as the open-source Macrosynergy Python package support this pipeline, offering modules for data import, visualisation, and factor construction through panel-based calculations and composites. Integration with statistical learning enables automated model selection, improves signal precision, and aligns backtests with realistic investor decision-making.
Obstacles to adoption
Despite the advantages, the adoption of macro-quantamental trading methods faces several headwinds:
- Institutional inertia: Firms may classify these as “just another dataset” in an already saturated procurement funnel
- Club good limitations: Systems like JPMaQS offer access primarily to participating institutions
- Field bias: Quant professionals often distrust macroeconomics, preferring data science over domain expertise
- Cost misconceptions: While licensing can seem expensive, it offsets modelling and data wrangling costs that would otherwise fall on users
Yet these challenges are waning as the benefits become more widely appreciated. JPMaQS, in particular, has started to reshape expectations about what is possible in macro-driven trading.
Putting theory into practice
The macro-quantamental approach turns a long-standing liability—the unusability of economic data in systematic strategies—into an asset. By delivering timestamped, tradable macro indicators at scale, it enables investors to systematise what discretionary traders have done for decades. Systems like JPMaQS provide the infrastructure to bridge the worlds of economic intuition and algorithmic rigour, opening the door to a more informed, more transparent, and ultimately more profitable era of macro investing.
About the team:
Macrosynergy is a London-based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Their quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets. Follow them on X / download their full (free) book here.