K-line Form Identification Using Segmented Key Line Extraction
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Solution Overview
Problem
Existing K-line charts in financial software are simplistic, providing limited visual information and poor user experience due to their inability to effectively display and analyze large numbers of stock price data over time.
Innovation Solution
A method and system that allows electronic devices to interact with servers to analyze K-line data, identify target forms, and display these forms on the charts, enabling users to intuitively understand K-line forms and trends, with features like adjustable time windows and detailed information pop-ups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional K-line charts simply display stock price data at various time points, then the implementation is simple and basic visual information is provided, but the reference value is limited and user experience is poor when facing large numbers of K-line charts
Solution Approach 1:
The patent segments the complex task of K-line form identification into multiple hierarchical levels: (1) extracting key K-lines from the full dataset, (2) identifying local forms between adjacent key K-lines, (3) synthesizing complete forms by combining local forms. This segmentation reduces computational complexity while maintaining comprehensive information coverage, directly resolving the contradiction between information completeness and system complexity.
2Productivity
If hierarchical processing is implemented for K-line form identification, then computational complexity is reduced and processing efficiency is improved, but the implementation becomes more complex
Solution Approach 1:
The algorithm divides the K-line data processing into hierarchical segments: first identifying key K-lines as anchor points, then analyzing local forms between these keys, and finally synthesizing complete forms. This segmentation transforms a single complex O(N²) problem into multiple simpler O(N) subtasks, improving processing efficiency while managing algorithmic complexity through structured decomposition.
Solution Approach 2:
The patent extracts key K-lines from the complete dataset as representative samples that capture the essential form characteristics. By taking out these key points and analyzing local forms around them, the system achieves efficient form identification without processing every single K-line, thereby improving productivity while keeping the extraction criteria relatively simple.
3Loss of information
If only connecting lines between two points are supported, then the implementation is simple, but the reference value is limited and user experience is poor
Solution Approach 1:
The patent implements dynamic form identification that adapts to different K-line patterns (rising forms, falling forms, oscillating forms, etc.). Instead of using a static connecting line approach, the system dynamically determines the appropriate form type based on the relative positions and characteristics of key K-lines, then draws the corresponding form representation. This dynamic approach enriches form information while managing drawing complexity through standardized form templates.
Data Source
AI summary
The present disclosure relates to computer technology, and provides a method for identifying a K-line form and an electronic device. The method includes: obtaining, by a terminal device, data of N1 K-lines of a first stock in a first time window, where N1 is an integer greater than 1; obtaining, by the terminal device, a first target form corresponding to the data of the N1 K-lines and x key K-lines in, the data of the N1 K-lines, the first target form indicating a K-line form of the data of the N1 K-lines, where x≤N1; displaying, by the terminal device, the N1 K-lines corresponding to the data of the N1 K-lines and drawing, by the terminal device, a first target form line on the N1 K-lines based on the first target form and the x key K-lines.