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.
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.
This post is a condensed guide on best practices for developing systematic macro trading strategies with links to related resources. The focus is on delivering proofs of strategy concepts that use direct information on the macroeconomy. The critical steps of the process are (1) downloading appropriate time series data panels of macro information and target returns, (2) transforming macro information states into panels of factors, (3) combining factors into a single type of signal per traded contract, and (4) evaluating the quality of the signals in various ways.
Best practices include the formulation of theoretical priors, easily auditable code for preprocessing, visual study of data before and after transformations, signal optimisation management with statistical learning, and a protocol for dealing with rejected hypotheses. A quick, standardised and transparent process supports integrity and reduces moral hazard and data mining. Standard Python data science packages and the open-source Macrosynergy package provide all necessary functionality for efficient proofs of concept.
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. Our 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.
