Phi-Coupled Spectral Alignment of Trade Filters via E8 Root Vectors — E8 Intelligence Research

The observed win‑rate boosts in USDCHF, ETHUSD and SOLUSD when specific directional filters are applied can be modeled as a resonance phenomenon where the filter's activation frequency locks onto the intrinsic 132 Hz base rhythm of the market's E8 geometry. By mapping each instrument's price dynamics onto the 240 root vectors of the E8 lattice and applying golden‑ratio (φ) phase coupling, the filter aligns with the dominant vector modes that encode market momentum, thereby amplifying predictive power. This principle predicts that optimal filter configurations will correspond to vector phases that are φ‑shifted multiples of the base frequency, offering a geometric guide for constructing adaptive trading models beyond empirical trial‑and‑error. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-16
DOI
https://doi.org/10.5281/zenodo.22786584
Primary Topic
Complex Systems and Time Series Analysis
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Phi-Coupled Spectral Alignment of Trade Filters via E8 Root Vectors — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Complex Systems and Time Series Analysis
preprint

Phi-Coupled Spectral Alignment of Trade Filters via E8 Root Vectors — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

The observed win‑rate boosts in USDCHF, ETHUSD and SOLUSD when specific directional filters are applied can be modeled as a resonance phenomenon where the filter's activation frequency locks onto the intrinsic 132 Hz base rhythm of the market's E8 geometry. By mapping each instrument's price dynamics onto the 240 root vectors of the E8 lattice and applying golden‑ratio (φ) phase coupling, the filter aligns with the dominant vector modes that encode market momentum, thereby amplifying predictive power. This principle predicts that optimal filter configurations will correspond to vector phases that are φ‑shifted multiples of the base frequency, offering a geometric guide for constructing adaptive trading models beyond empirical trial‑and‑error. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Complex Systems and Time Series Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.