Introductory Notebooks

These introductory downloadable Jupyter notebooks provide researchers and investment professionals with a headstart to become more familiar with JPMaQS. They demonstrate how to import data through the J.P. Morgan DataQuery API into a standard Python environment, how to work with the standard pandas data frame format, and give examples of simple analysis using either standard packages or the specialized Macrosynergy package.

Introductory Tutorials

The nature and function of quantamental indicators

Introduction to a quantamental system architecture

How to download quantamental indicators

Macro-quantamental scorecards

Macro-quantamental scorecards are condensed visualizations of point-in-time economic information for a specific financial market. Their defining characteristic is the combination of efficient presentation and evidence of empirical power. This post and the accompanying Python code show how to build scorecards for duration exposure based on six thematic scores: excess inflation, excess economic growth, overconfidence, labour market tightening, financial conditions, and government finance.

All thematic scores have displayed predictive power for interest rate swap returns in the U.S. and the euro area over the past 25 years. Since economic change is often gradual and requires attention to a broad range of indicators, monitoring can be tedious and costly. The influence of such change can, therefore, build surreptitiously. Macro-quantamental scorecards cut information costs and attention time and, hence, improve the information efficiency of the investment process.