Mapping Ocean Tide and NASDAQ Data with Category Theory Functors

Summary

The article introduces functors as mappings between categories that preserve objects, relationships, composition, and identity. It applies this framing to daily tide observations from Monterey, California, and NASDAQ volatility data, proposing that one ordered time series could be mapped to the other with a lag. Tide observations are organized as daily domains, while market volatility is represented through sequential price relationships. The implementation constructs these categories from tide data and market highs and lows, then uses a mapping across recent observations as a forecasting feature.

The example is exploratory rather than an established trading method. The article suggests possible applications such as data fusion, correlation studies, causal analysis, and machine learning, and mentions use of a separate Awesome Oscillator entry signal and fixed-margin money management in the assembled EA. The supplied text does not report a test design, measured relationship, forecast accuracy, or trading performance demonstrating that tides predict NASDAQ volatility. Data alignment, normalization, lag choice, and the distinction between association and causation remain important limitations.

Key ideas

• A functor maps objects and morphisms between categories while preserving structural relationships.
• The example models daily Monterey tide observations and NASDAQ volatility as ordered data categories.
• A lagged mapping is proposed as a possible way to investigate whether tide data offers forecasting information for market volatility.
• The article presents code-oriented construction details but supplies no quantitative evidence of predictive or trading performance.
• The proposed interdisciplinary analysis requires careful treatment of timing, scaling, and causal claims.

Tags

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