Hierarchical Object Model for Dynamic Market Data Analysis
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Solution Overview
Problem
Current data analysis methods for market instruments are inadequate in handling large, dynamically changing datasets, as they often rely on oversimplified and static approaches that fail to provide reliable insights into future market performance and do not support flexible modeling or timely hypothesis testing.
Innovation Solution
A programmatic object model that facilitates financial analysis by using zero-order and higher-order objects, including time series, metrics, instruments, and auxiliary entities, allowing for the decomposition and composition of these objects to analyze and model market data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data analysis approaches (spreadsheets, empirical knowledge) are used, then ease of operation is maintained, but reliability of insights and ability to handle large dynamic datasets deteriorates
Solution Approach 1:
The system segments the complex data analysis task into hierarchical levels: zero-order objects (raw data), first-order objects (derived data), and higher-order objects (analytical models). This segmentation allows the system to handle large datasets by processing them in manageable layers, improving reliability while maintaining operational clarity through structured decomposition.
Solution Approach 2:
The patent introduces a dimensional hierarchy to data organization, moving from flat spreadsheets to multi-level object models. This dimensional transformation enables the system to manage complexity by organizing data across multiple abstraction levels, thereby improving reliability without overwhelming the user with complexity.
2Adaptability or versatility
If static modeling approaches are used, then device complexity is reduced, but adaptability to new variables and trends deteriorates
Solution Approach 1:
The system implements dynamic adaptability through its hierarchical object model, where higher-order objects can be dynamically created and modified based on new market variables and trends. The decomposition into zero-order and higher-order objects allows the model to evolve flexibly without requiring complete system redesign, thus improving adaptability while managing complexity through structured organization.
Solution Approach 2:
The object model provides universal functionality by allowing the same hierarchical structure to handle diverse data types and analytical purposes. The decomposition framework can accommodate various variables and trends within a unified model, improving versatility without proportionally increasing complexity.
3Reliability
If comprehensive data analysis is performed, then reliability of insights is improved, but loss of time for analysis and response deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data into hierarchical levels, where zero-order objects are prepared and structured in advance. This preliminary organization enables faster analysis and hypothesis testing, as the foundational data structure is already in place, reducing the time required for comprehensive analysis while maintaining reliability.
Solution Approach 2:
By segmenting data analysis into hierarchical levels, the system can perform comprehensive analysis efficiently. Lower-level processing is automated and pre-computed, allowing higher-level analytical models to focus on interpretation and insight generation, thereby reducing overall analysis time while maintaining thoroughness and reliability.
Data Source
AI summary
Techniques are described for facilitating performing computer-implemented financial analysis. A metric that transforms one or more time series into an output object is identified. The one or more time series are determined based on one or more input objects. The metric is applied using the one or more time series, thereby generating a particular value for the output object. One of the metric and the particular value for the output object is stored in a physical storage device.