Editor’s note: This article is a quantitative analysis piece published for research and educational purposes. Vortex Legacy does not provide investment advice, price predictions, or trading recommendations. Nothing below should be interpreted as guidance to buy, sell, or hold any asset.
The capacity to make well-informed judgements in the face of uncertainty is what distinguishes rigorous analysts from intuitive guesswork. Poker players excel at figuring out probabilities, interpreting partial information, and selecting the optimal bet — and the same probabilistic toolkit has long been applied to descriptive studies of price-return sequences.
This article examines a method that applies poker-style decision trees and probabilistic thinking to the analysis of historical AAPL monthly returns. By converting returns into binary outcomes, we measure the historical frequency of recurring 3-month patterns and compare that frequency to what a naive model would predict. The exercise is descriptive statistics, not a prescription.
The Python implementation and full source code are available on GitHub for readers who want to reproduce the analysis or explore the methodology in their own research.
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