Introductory Tutorials

Getting Started

These tutorials walk you through the essential steps of working with macro-quantamental data using the macrosynergy Python package. By the end, you will be able to retrieve indicators, visualize economic trends, and construct basic trading signals.

Prerequisites include a working Python environment (3.8+), familiarity with pandas and matplotlib, and access credentials for the JPMaQS API.

Retrieving Data

The first step in any analysis is loading the data. The macrosynergy package provides convenient functions for downloading indicators by ticker, category, or country. All downloads automatically respect point-in-time constraints, ensuring that the data you analyze matches what was available at each historical date.

Data Visualization

Effective visualization is essential for understanding macro data. The package includes purpose-built plotting functions for time series, cross-sectional comparisons, heatmaps, and correlation structures. These tools help you spot trends, anomalies, and relationships before moving to formal signal construction.

Basic Signal Construction

A signal is a numerical score that ranks assets (countries, currencies, or bonds) from most to least attractive. The simplest signals are z-scores of individual indicators, computed relative to a rolling historical window. More sophisticated signals combine multiple indicators using linear or non-linear methods.

Every signal must be evaluated out-of-sample to guard against overfitting. The package provides built-in support for expanding-window estimation, which ensures that only past data informs each period's signal.

Next Steps

With these fundamentals in place, you are ready to explore factor construction and systematic strategy development in the Strategies stage of the curriculum.