Extensible Software Architecture for Level 2 Financial Data Processing
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Solution Overview
Problem
Current systems for processing Level 2 financial data are limited by their inability to accommodate diverse data feeds and formats, leading to inefficiencies in information processing and presentation, with existing solutions being ad hoc, non-extensible, and performance-draining.
Innovation Solution
The Order Book Engine API provides a modular and extensible architecture that generates dynamic keys for sorting and processing Level 2 financial data, allowing for flexible organization and translation into various formats compatible with client equipment, using a network of managers and publishers for scalable data dissemination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ad hoc processing methods are used for Level 2 financial data, then flexibility in handling diverse data feeds is improved, but system complexity and performance degradation worsen
Solution Approach 1:
The system segments the processing architecture into distinct modular components: data feed adapters for different feed formats, a normalization layer that converts all feeds to a common internal representation, and processing engines that operate on the normalized data. This segmentation allows each component to be independently developed, tested, and configured, providing flexibility for handling diverse data feeds without increasing overall system complexity.
Solution Approach 2:
The patent implements a universal normalized data model that serves as a common intermediate representation for all Level 2 financial data feeds regardless of their source or format. This universal model enables a single processing engine to handle multiple feed types (NYSE, NASDAQ, regional exchanges, alternative data providers) without requiring separate processing logic for each, thereby improving adaptability while maintaining manageable system complexity.
2Manufacturing precision
If custom processing architectures are created for each data feed, then data processing accuracy is improved, but development time and resource requirements worsen
Solution Approach 1:
The system performs preliminary normalization of all incoming Level 2 data feeds into a standardized internal representation before processing. This preliminary action converts diverse feed formats (different delimiters, field orders, data types) into a unified structure, ensuring consistent and accurate processing downstream. The feed adapters pre-process and validate data according to feed-specific characteristics, while the normalization layer applies universal transformation rules, thereby ensuring data processing accuracy without requiring custom processing logic for each feed.
Solution Approach 2:
The patent introduces a normalized data model as an intermediary layer between the diverse data feeds and the processing engines. This intermediary representation acts as a universal interface that preserves the semantic meaning and structural relationships of the original data while eliminating format-specific variations. By mediating between feed diversity and processing requirements, this intermediary layer ensures accurate processing across all feed types without requiring custom processing architectures for each source.
3Loss of information
If multiple processing criteria are applied to Level 2 data, then information completeness is improved, but processing overhead and computational resources worsen
Solution Approach 1:
The system implements dynamic processing that adjusts the depth and type of processing applied to data based on client-specific requirements and market conditions. Processing engines can selectively apply different levels of analysis (basic normalization, advanced pattern recognition, predictive modeling) depending on the client's subscription tier and real-time needs. This dynamic approach ensures complete information is captured in the normalized representation while allowing computational resources to be allocated efficiently based on actual processing requirements rather than applying maximum processing to all data uniformly.
Data Source
AI summary
The present invention processes and distributes Level 2 financial data. This invention comprises a constituent component that identifies various pieces of information that are contained in stock feeds. These pieces of information are identified and keys are generated based on the various pieces of information and combinations of pieces of information. The information in the incoming stock feeds can be sorted and processed based on a particular key or keys depending on the desires of a particular client. In addition, new keys can be generated based on the preference of a particular client. This flexibility to create the various keys to be used to process feed information is different from conventional methods that use only a standard set of sorting and processing criteria for all feeds and for all clients.