Q-Score Trading Signals Using Wave-Collapse Market Regimes
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Solution Overview
Problem
Conventional financial models fail to adapt to nonlinear dynamics and regime shifts in financial markets, lack transparency, and provide unreliable trading signals across multiple asset classes, particularly in FX markets due to missing volume data and overfitting issues in machine learning models.
Innovation Solution
A Q-Score collapse-based signal generation framework integrating quantum-inspired indicators and genetic algorithms to generate adaptive trading signals, with explainability features and risk management protocols, enabling robustness across asset classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional statistical models are used, then linearity and stationarity assumptions simplify analysis, but they fail to capture nonlinear dynamics and regime shifts in financial markets
Solution Approach 1:
The system dynamically changes parameters by switching between different quantum-inspired indicators (price curvature, phase interference, amplitude interactions, volatility correlations) based on market regime detection. This allows the model to adapt its behavior to different market conditions while maintaining a unified quantum-mechanics-based framework.
Solution Approach 2:
The patent implements dynamic adaptation through genetic algorithms that continuously evolve indicator weights and parameters in response to changing market conditions. The system transitions from static conventional models to dynamic quantum-inspired models that can capture nonlinear dynamics and regime shifts.
2Measurement precision
If machine learning models are used, then historical data fitting improves, but overfitting occurs and interpretability is lost
Solution Approach 1:
The patent introduces quantum-inspired indicators as intermediary constructs that bridge the gap between raw market data and trading signals. These indicators (phase interference, amplitude interactions, volatility correlations) provide physically-inspired intermediate representations that maintain interpretability while capturing complex nonlinear relationships, avoiding the black-box nature of conventional machine learning.
Solution Approach 2:
The system replaces conventional machine learning algorithms with quantum-mechanics-inspired models. By using quantum-inspired indicators derived from wave interference and collapse dynamics, the patent substitutes statistical and neural network approaches with a physics-based framework that provides both accuracy and interpretability through transparent signal decomposition.
3Loss of information
If simple models are used, then transparency is maintained, but reliability under stress and volatility shocks deteriorates
Solution Approach 1:
The patent creates a composite trading system that combines multiple quantum-inspired indicators (price curvature, phase interference, amplitude-amplitude interaction, amplitude-charge interaction, volatility-price correlation) into a unified Q-Score framework. This composite approach maintains transparency through quantum-mechanics-based interpretability while improving reliability under stress by capturing diverse nonlinear market dynamics.
4Adaptability or versatility
If quantum-inspired indicators are integrated, then nonlinear dynamics are captured, but system complexity increases
Solution Approach 1:
The patent segments the complex quantum-inspired analysis into distinct, modular indicators: price curvature (Ψ''p), phase interference indicator (PII), amplitude-amplitude interaction (AAI), amplitude-charge interaction (ACI), and volatility-price correlation (VCI). Each indicator captures a specific aspect of nonlinear dynamics, and their modular structure allows selective application and independent optimization, managing overall system complexity.
Data Source
AI summary
A system and method for trading and risk management are disclosed. The invention introduces a Q-Score framework derived from the Total Wave Modified Schrödinger Equation (TWMSE), using five quantum physics indicators: price curvature, phase interference, amplitude-amplitude interaction, amplitude-charge interaction, and volatility-price correlation. A genetic algorithm optimizer adapts indicator weights by asset and regime, generating buy, sell, or neutral signals. Collapse-based logic enables abstain states, reducing false positives. Integrated risk protocols include adaptive sizing, pyramiding, and turnover controls. Tests on equities and FX in August 2025 confirm robustness, supporting institutional use in adaptive, explainable platforms. This Continuation-in-Part extends prior wave-collapse inventions into the domain of financial markets, providing a physics-inspired, adaptive, and transparent system that bridges theoretical innovation with practical trading execution across diverse asset classes.