Correlation Analysis System for Financial Chart Matching
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Solution Overview
Problem
Investors face difficulties in identifying correlations between traded items and finding items with similar price histories due to overwhelming market information and the dominance of well-known correlations by institutional traders, making it hard for amateur investors to realize profits.
Innovation Solution
A system that automatically processes pricing data from multiple markets to identify correlated traded items and those with similar price histories by performing mathematical operations, such as normalization and similarity determination, and provides users with listings of closely correlated items or items matching predefined curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If investors manually review thousands of market listings to find correlations, then they can identify potential investment opportunities, but the process becomes overwhelming and time-consuming
Solution Approach 1:
The patent replaces manual mechanical review of market listings with an automated computer system that uses mathematical algorithms to calculate correlations. The system automatically processes pricing data, computes correlation coefficients, and identifies similar charts without human intervention, thereby maintaining high measurement precision while eliminating time loss.
Solution Approach 2:
The system performs self-service by automatically analyzing market data, calculating correlations, and generating investment insights without requiring manual human analysis. The computer system independently processes the overwhelming amount of market information and provides structured correlation results, freeing investors from manual data review.
2Reliability
If investors rely on well-known correlations, then they can use established investment strategies, but profits become difficult to realize due to institutional trader dominance
Solution Approach 1:
The patent segments the market analysis process by categorizing correlations into well-known and emerging patterns. The system separately identifies and presents established correlations alongside newly discovered correlations, allowing investors to access both reliable traditional strategies and untapped profit opportunities through automated detection of lesser-known relationships.
Solution Approach 2:
The system performs preliminary action by proactively discovering and presenting emerging correlations before they become widely known. The automated analysis continuously scans market data to identify new correlation patterns, giving investors early access to potentially profitable relationships that institutional traders have not yet exploited.
3Productivity
If the system processes vast amounts of market data automatically, then correlation identification becomes efficient, but the complexity of the analysis system increases
Solution Approach 1:
The patent applies parameter changes by transforming complex market data into standardized correlation coefficients and similarity metrics. The system converts diverse pricing information into comparable mathematical parameters, simplifying the analysis process while maintaining high processing efficiency. This parameter transformation reduces perceived system complexity despite the volume of data processed.
Data Source
AI summary
Embodiments of the invention concern finding correlations between related traded items, such as securities, commodities, currencies, or contracts. The pricing information of multiple traded items from one or more markets is analyzed and a user is provided with a listing of pairs or larger groups of traded items which are most closely correlated. Another embodiment of the invention is a system and method for finding the most related traded item to a particular predefined item. Another embodiment of the invention is a system and method for finding the traded item whose trading history is closest to a predefined curve.