David Edward Scherer Formula — Version 3.5 (DES V3.5): Self-Improving Quant Layer with Reinforcement Learning, Regime Detection, and Bayesian Optimization

The David Edward Scherer Formula — Version 3.5 (DES V3.5) is an adaptive quantitative investment framework designed to dynamically optimize portfolio construction through continuous learning and feedback-driven decision-making. The system integrates reinforcement learning, online parameter adaptation, regime detection, and Bayesian optimization to refine allocation strategies in response to evolving market conditions. DES V3.5 operates as a policy-based system in which allocation decisions are iteratively updated based on observed outcomes. Market data is transformed into structured state representations, enabling a decision policy to determine portfolio actions. These actions are evaluated using risk-adjusted performance metrics, and the resulting feedback is used to continuously improve the system. The framework incorporates a regime detection module that classifies market environments into trend expansion, downtrend, and range-bound conditions. This allows for context-aware allocation adjustments. Parameter tuning is handled using probabilistic optimization, enabling efficient exploration of factor weights, thresholds, and risk parameters while reducing reliance on manual calibration. An online learning layer supports real-time parameter updates and drift correction, ensuring alignment with changing market dynamics. Stability controls, including learning rate management and update gating, are incorporated to mitigate overfitting and noise sensitivity. DES V3.5 is designed as a self-adjusting allocation engine that continuously refines its decision policy, adapts to structural changes, and improves performance through iterative learning and probabilistic optimization.