Comparing Moving-Average Transforms with Category-Theory Signals

Summary

This article frames raw prices and moving-average series as categories connected by three averaging functors with different periods. It calls the differences between pairs of moving averages natural transformations, tracks two such difference series, and uses their Spearman correlation as a filter. The proposed directional signal comes from the accumulated incremental changes in the moving averages: positive values are treated as bullish and negative values as bearish. Positive correlation is interpreted as trending conditions, while negative correlation suggests whipsaw.

The discussion relates this construction to moving-average crossover ideas and describes a conceptual implementation for time-series forecasting. It argues that the transformations may offer confirmation when used alongside moving averages, but the supplied article does not provide enough detail to assess statistical significance or trading performance. It also notes that a robust trading system requires further work, and that volatility changes may be ignored by the approach. Category-theory terminology provides the organizing framework; the proposed signal still needs independent validation and risk controls.

Key ideas

• Three moving-average periods are represented as mappings from raw prices to separate smoothed series.
• Differences between adjacent moving-average series form two data buffers for comparison.
• The proposal filters a directional signal with the Spearman correlation of those difference buffers.
• The article presents the method conceptually and leaves robust strategy validation and risk controls to further work.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.