The article examines the Kendall methodology as an integrated quantitative WT/PE architecture that translates heuristic market intuition into a formalized language of signals, portfolio rules, and rigid verification procedures on long historical series. The growth of backtest overfitting risks drives the relevance of the study, demands for regulatory and institutional transparency, and the industry’s shift toward integrated AI/ML pipelines, in which the verifiability of trading algorithms becomes as significant as their predictive power. The objective of the work is to articulate, in scientific terms, the internal logic of the WaveTech/PortfolioExpert linkage, the core of proprietary indicators (SMA bands 10/21/41, PPM oscillators, ER metric), and to demonstrate how thematic concentrated Top ER portfolios with a controlled risk profile are formed through signal confluence and ER-based selection. The scientific novelty consists in a systemic formalization of the robustness-by-design principle for a practical trading platform: multi-level timeframe alignment, a discrete signal refresh regime, outlier exclusion, strict in-/out-of-sample and walk-forward discipline, and a substantiated inclusion of ML-enhanced Genetic Evolution Algorithms as a meta-layer for searching interpretable strategies without transitioning to an opaque black box. The main results show that a portfolio constructed exclusively on ER logic and transferred statically from the in-sample period 2015–2020 into the out-of-sample window 2021–2025 preserves positive dynamics under an expected reduction in returns, which is interpreted as empirical evidence of construct transferability and of the limits of its adaptability to changing market regimes. The article will be helpful to researchers and practitioners of quantitative trading, portfolio managers, trading-platform architects, and risk specialists interested in reproducible and auditable algorithmic strategies.

Kendall methodology, WaveTech, PortfolioExpert, SMA bands, Price Pressure Momentum, Effectiveness Rating

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