Match Engine Modeling Language for Trading Systems
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Solution Overview
Problem
The complexity of commodities markets, characterized by a large number of potential order combinations and exponential growth in possible trades, poses challenges for high-speed and reliable electronic trading systems, particularly in terms of development, testing, and maintenance, due to the need for rapid implementation and high reliability in a competitive and scrutinized environment.
Innovation Solution
A symbolic modeling language, such as the Match Engine Modeling Language (MEML), is introduced to describe match engine operations in a form understandable by business analysts and easily translatable into program code, facilitating development, testing, and maintenance by using Domain Specific Visual Modeling Language elements like concrete syntax, abstract syntax, syntactic mapping, semantic domain, and semantic mapping, along with an automated test tool to expedite match engine development and ensure reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional programming approaches are used for match engine development, then implementation speed is fast, but development time and testing complexity increase due to market complexity and exponential order combinations
Solution Approach 1:
The patent creates a virtual model of the match engine using a symbolic language that replicates the engine's behavior. This virtual model can be simulated and tested independently of the actual engine, allowing thorough testing without delaying implementation. The model copies the engine's logic and can be used to generate test cases and verify functionality before deployment.
Solution Approach 2:
The symbolic language allows developers to specify the match engine's behavior in advance and automatically generate test cases before the actual engine is built or before new features are implemented. This preliminary specification and testing approach ensures that complex scenarios are covered without requiring time-consuming manual testing after implementation.
2Reliability
If comprehensive testing is performed to ensure reliability, then system reliability improves, but testing time and resource requirements increase significantly
Solution Approach 1:
The virtual model serves as a copy of the match engine that can be extensively tested without affecting the actual system. This allows comprehensive testing of edge cases, error conditions, and complex order combinations in isolation, ensuring reliability while avoiding the time cost of testing the live system repeatedly.
Solution Approach 2:
The system uses its own symbolic representation to automatically generate test cases and perform self-verification. The match engine can be described in the symbolic language, and the system automatically derives test scenarios from this description, reducing the need for manual test case creation and execution.
3Adaptability or versatility
If the match engine handles complex implied markets with multiple commodities, then market coverage increases, but computational complexity and processing requirements grow exponentially
Solution Approach 1:
The patent segments the complex market modeling into discrete components using the symbolic language. Each market relationship, order type, and trading rule can be represented as separate symbolic elements that can be combined to model complex scenarios. This segmentation allows the system to handle multiple commodities and implied markets without overwhelming computational complexity, as each component can be processed independently.
Data Source
AI summary
A symbolic modeling language for trade matching providers techniques to describe the specialized operations of a match engine in a form that can be understood by business analysts and readily translated into program code and test cases by developers and testers. Associated techniques for calculating implied markets and testing can expedite match engine development, testing and maintenance.