Pivot Point Formation Recognition in Financial Data Analysis
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Solution Overview
Problem
Manual technical analysis in finance is prone to errors due to human subjectivity and reliance on pattern recognition, and existing automated methods like neural networks are cumbersome and data-dependent, making it difficult to accurately identify predictive formations in financial data.
Innovation Solution
A method using pivot points in reverse chronological order to systematically recognize formations like continuation triangles and reversal diamonds by categorizing and analyzing alternating high and low extreme points, eliminating the need for human judgment and allowing for early pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual charting and pattern recognition is used, then formations can be visually identified, but the process is tedious, error-prone, and slow
Solution Approach 1:
The patent replaces manual visual analysis with an automated computer-based system that uses algorithms to identify pivot points and recognize formations. The system automatically processes price data, identifies extreme points, and detects formations without human intervention, thereby eliminating the trade-off between accuracy and speed.
Solution Approach 2:
The system performs self-analysis by automatically identifying pivot points, categorizing them, and recognizing formations without requiring manual charting or human pattern recognition. The automated process serves itself by systematically analyzing data according to predefined criteria, eliminating human error and subjectivity.
2Extent of automation
If neural networks are used for pattern recognition, then automation is achieved, but the method is cumbersome and highly dependent on data quality
Solution Approach 1:
The patent segments the complex pattern recognition task into distinct, manageable steps: identifying pivot points, categorizing them by type (high/low, left/right), and then recognizing formations based on sequences of these categorized points. This segmentation simplifies the automation process compared to training entire neural networks, reducing both complexity and data requirements.
Solution Approach 2:
Instead of training a system to recognize complete formations directly from raw data (as neural networks attempt), the patent inverts the approach by first identifying and categorizing fundamental building blocks (pivot points), then assembling these into formations. This bottom-up approach reduces complexity and improves automation reliability.
3Reliability
If forward-facing pattern recognition is used, then systematic analysis is performed, but time delay occurs in recognizing formations
Solution Approach 1:
The patent performs preliminary identification and categorization of pivot points as they occur, maintaining a structured record of these points. When sufficient categorized pivot points are available, formation recognition can immediately begin without waiting for additional data, thereby reducing time delay while maintaining systematic analysis.
Solution Approach 2:
The system dynamically adapts its analysis based on the availability of categorized pivot points. As new pivot points are identified and categorized in real-time, the system continuously evaluates whether sufficient data exists to recognize formations, enabling timely recognition without rigid forward-facing constraints.
Data Source
AI summary
A method of formation recognition in technical analysis relies on the use of pivot points. A formation of interest is defined in terms of extreme points. The extreme points can be characterized in relation to one another as a series of local or global extrema in corresponding intervals. The method numbers the pivot points in reverse chronological order and attempts to match pivot points with the extreme points of the formation of interest. The first pivot point is assigned to the first extreme point of the formation. A second pivot point is selected from the interval defined by the first pivot point and the highest numbered pivot point that is a high or a low, as required by the formation. Subsequent pivot points are selected from intervals determined based on the formation and previously determined intervals. A formation is recognized if corresponding pivot points are identified for all extreme points in the formation.