This is my third use case for how to do quantitative analysis with Chat GPT 4. The two others, on Eurozone inflation and times series regression with macro data, can be found here and here. I started in the industry as Head of Research for Variant Perception, a research shop that specialises, among other things, in quantitative trading models, asset allocation tools, and trade signalling analysis. One tool that came up again and again in my analyses was binary signals to identify turning points in asset classes, stocks or economic data series. The idea is simple. First, you create a binary indicator which takes the value of 1, if a certain threshold in the data is breached to the upside or downside, and zero otherwise. Secondly, you investigate what happens after such a signal has gone off, either in the original data set or mapped to a separate data set. You can combine signals across datasets to get a rolling series of signals, which can be compared to asset prices or economic data. You can see an example of such an analysis with the Nasdaq here.
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