Q-Level Support Resistance System for Securities Trading
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Solution Overview
Problem
Current systems for identifying support and resistance levels in financial markets lack reliability and accuracy, with existing methods often relying on arbitrary or unorthodox techniques, resulting in success rates below 50% and failing to consistently provide useful predictions for traders.
Innovation Solution
A system that generates and displays Q-levels, which are derived from a natural fractal pattern based on the value of Q, an extension of the Golden Ratio, to provide support and resistance levels for securities and commodities, using Q-levels and power ratings to aid traders in making buy and sell decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If arbitrary or unorthodox techniques are used to identify support and resistance levels, then the system can be implemented, but the reliability and accuracy of predictions remain below 50%
Solution Approach 1:
The patent applies parameter changes by transitioning from arbitrary pricing methods to a mathematically-defined pricing system based on the Golden Ratio (Q=1.618). This fundamental parameter change in how price levels are calculated transforms the reliability of predictions from below 50% to above 50%, while maintaining implementation feasibility through standardized mathematical formulas.
Solution Approach 2:
The patent replaces mechanical/manual identification of support and resistance levels with an automated computational system that calculates Q-levels using mathematical formulas. This substitution eliminates human subjectivity and arbitrary decision-making, thereby improving prediction reliability while the automation actually simplifies implementation through systematic calculation.
2Reliability
If traditional support and resistance levels are used, then traders can identify basic price floors and ceilings, but the success rate remains around 56.2% and cannot consistently provide useful predictions
Solution Approach 1:
The patent segments the continuous price spectrum into discrete, mathematically-defined Q-levels based on the Golden Ratio. This segmentation creates distinct support and resistance zones (Q0, Q1, Q2, etc.) that are more precisely identified than traditional continuous price levels, thereby improving measurement precision and prediction accuracy.
Solution Approach 2:
The patent introduces a new dimensional approach by applying the Golden Ratio mathematical constant to price level identification, moving beyond traditional time-based or volume-based analysis. This dimensional change in the mathematical foundation of price level calculation enables more accurate identification of support and resistance zones.
3Measurement precision
If a system provides detailed Q-levels and power ratings for every price point, then measurement precision is maximized, but the complexity of the system increases
Solution Approach 1:
The patent applies dynamics by making the display of Q-levels adaptive and selective rather than static and comprehensive. The system dynamically determines which Q-levels to display based on relevance to current price and market conditions, and adjusts the level of detail (including power ratings) based on user needs. This dynamic approach maintains measurement precision while reducing perceived system complexity.
Solution Approach 2:
The patent applies partial action by selectively displaying only the most relevant Q-levels and power ratings rather than providing exhaustive information for every possible price point. This partial disclosure approach maintains measurement precision for critical levels while reducing system complexity and information overload for the user.
Data Source
AI summary
The disclosure is directed generally to a system used to generate and display useful support and resistance levels, as derived from a given price of a security or commodity. The system incorporates a methodology to derive and display key price values, in actual currency units such as dollars and cents, to serve as support and resistance levels for the benefit of a user who in turn can better make buy and sell decisions for targeted securities and/or commodities.