Theme 6: Generic Returns#

This collection of Jupyter notebooks introduces generic returns of liquid derivatives and some cash contracts across asset classes.

Generic returns are not really quantamental indicators but a high-quality data set of target variables to be predicted by quantamental indicators. By including such returns in JPMaQs, we provide quick and easy evaluation metrics of potential trading strategies.

Generic returns provide approximate daily profit and loss series’ of positions in stylized contracts as a percentage of notional or risk capital. They are available for a range of asset classes, including interest rate swaps, government bonds, FX forwards, equity index futures, commodity futures, and some CDS indices. Many types of generic returns also include returns on volatility-targeted and hedged positions.

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.