Theme 5: Stylized Trading Factors#

This collection of Jupyter notebooks introduces quantamental indicators of stylized trading factors.

Trading factors here refer to daily information states of composite indicators of economic and market information that are widely accepted as a valid basis for taking financial market positions. At present this theme is still at a basic stage and the focus is mainly on types of cross-asset carry. Carry generally is defined as the return on an asset in a state where all market prices are unchanged, In order to arrive at meaningful measures this requires the consideration of macro estimates, such as forward earnings growth and inflation.

Indicators are organized in categories, i.e. panels of one type of indicator over as many currency areas or markets as are available. Then the categories are grouped by similarity and each group is presented in a notebook.

The notebooks define and document the categories, describe their panels of time series, and provide some examples to illustrate their relevance for trading and algorithmic strategies. Most importantly, the notebooks are downloadable and can be used as a basis for exploring the respective categories interactively and relating them to generic financial returns with a few lines of Python code. All notebooks use the Macrosynergy Python package of standard functions for downloading, plotting, and analyzing data in standard JPMaQS format.