Rule-Constrained Statistical Pattern Recognition for Price Formation
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Solution Overview
Problem
Existing methods for recognizing price formations in financial data are prone to errors due to reliance on human pattern recognition, which can be subjective and biased, and are often inefficient, especially when dealing with complex or unseen data patterns.
Innovation Solution
A hybrid formation recognition method that combines rule-constrained statistical pattern recognition, using a minimalist rule-based model to narrow the search space and generate relevant features for a statistical model, allowing for the identification of candidate formations and their validation by expert opinions to refine the recognition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human pattern recognition is used to identify price formations, then the method is intuitive and easy to understand, but it is subjective, biased, and prone to errors
Solution Approach 1:
The patent replaces human visual pattern recognition with an automated computational system that uses statistical models and algorithms to identify price formations. This substitution eliminates human subjectivity and bias while maintaining the ability to detect patterns in financial data, thereby improving reliability without sacrificing the core functionality of pattern recognition.
Solution Approach 2:
The system enables automated self-service pattern recognition by using algorithms to independently identify and classify price formations without requiring human intervention. The computational model processes financial data autonomously, reducing reliance on human analysts while improving consistency and accuracy in formation detection.
2Reliability
If complex statistical models are used to recognize price formations, then recognition accuracy may improve, but the complexity of the model increases making it harder to manage
Solution Approach 1:
The patent segments the pattern recognition process into distinct stages: data preprocessing, feature extraction, statistical modeling, and formation classification. By dividing the complex task into manageable components, the system achieves high recognition accuracy while keeping each individual module relatively simple and easier to manage和维护.
Solution Approach 2:
The system optimizes model complexity by carefully selecting and tuning key parameters in the statistical models. Rather than using overly complex models, the patent identifies and focuses on the most influential parameters that drive accurate formation recognition, thereby achieving high reliability with manageable model complexity.
3Ease of operation
If rule-based recognition methods are used, then the model is deterministic and easy to interpret, but it cannot encompass uncertainty or model expert disagreement
Solution Approach 1:
The patent creates a hybrid recognition system that combines rule-based methods with statistical modeling. The rule-based component provides deterministic, interpretable logic for clear-cut cases, while the statistical component handles uncertainty and ambiguity. This composite approach allows the system to maintain interpretability where rules apply while adapting to uncertain situations through statistical inference.
Solution Approach 2:
The system dynamically switches between rule-based and statistical approaches depending on the situation. When data clearly satisfies predefined rules, the deterministic rules are applied for interpretability. When uncertainty or ambiguity is detected, the system transitions to statistical modeling that can accommodate expert disagreement and probabilistic outcomes.
Data Source
AI summary
A method of developing a rule-constrained statistical pattern recognizer applicable to price formation recognition includes assembling input data containing examples of patterns to be recognized and establishing mandatory recognition rules. The recognition rules are programmed to construct an underspecified or underconstrained recognition model which is applied to the assembled data to produce candidate patterns. The candidate patterns are reviewed and identified as valid or invalid and for each pattern type a residual statistical model is produced based on the candidate patterns identified as valid. A filter is used to ensure that custom conditions such as duration relationships, height relationships and volume requirements are met.