The David Edward Scherer Formula (DES) Version 3.4 introduces a semi-automated quantitative layer that transforms a traditional scoring-based investment framework into a modular, data-driven research pipeline. This system integrates continuous market data ingestion, feature engineering, factor modeling, backtesting simulation, portfolio construction, and risk-adjusted scoring. DES V3.4 replaces static, point-in-time evaluation with dynamic time-series signal processing, enabling systematic validation of investment signals. The framework incorporates regression-based factor analysis to identify return drivers, walk-forward backtesting to evaluate performance robustness, and adaptive portfolio weighting mechanisms to optimize capital allocation. A Sharpe-style risk adjustment layer further refines outputs by penalizing volatility, drawdowns, and inconsistency, resulting in a final composite score that reflects both expected return and risk profile. This methodology is designed for educational, research, and exploratory quantitative finance applications. It provides a structured foundation for developing semi-systematic investment strategies while maintaining interpretability through its modular architecture.
Scherer et al. (2026) studied this question.