This repository documents the results of a closed-source algorithm designed to detect statistical patterns in binary sequences, derived from real-world event data.
While the algorithm itself remains confidential, the structure, evaluation, and observed outcomes are fully presented for review.
- 52 independent blocks of binary evaluations
- +392 cumulative gain
- Strong positive skew over majority of blocks
- Focused on pattern detection and deviation from randomness
| File | Purpose |
|---|---|
RESULTS.md |
Table of outcomes across 52 blocks |
GRAPH.md |
ASCII-based visual of cumulative progress |
METHODOLOGY.md |
Conceptual description of the method (no code) |
DISCLAIMER.md |
Legal notice and usage restrictions |
- Pattern emergence in binary event streams
- Deviation from statistical randomness
- Aggregated block analysis instead of per-trade logic
- Possible applications: signal detection, behavioral entropy, statistical noise reduction
This repository does not contain the underlying algorithm.
It is not intended for use in betting, trading, or prediction systems.
See DISCLAIMER.md for full notice.
To contact the author or cite this experiment, please use the GitHub repository URL.
See TODO.md for upcoming milestones and research directions.