Pricing Graph Engine for Real-Time Investment Data
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Solution Overview
Problem
Generating pricing information for investment instruments is complex and resource-intensive due to dynamic and time-sensitive nature of broker quotes, requiring frequent and real-time calculations across multiple instruments.
Innovation Solution
A pricing graph system with inter-dependent nodes and a pricing engine that executes calculations in real-time, facilitating the generation and management of pricing information through input and output slots, enabling efficient processing and storage of market data and investment prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent real-time calculations are performed to generate pricing information for multiple investment instruments, then the timeliness and accuracy of pricing information is improved, but the computational resources and cost increase significantly
Solution Approach 1:
The pricing system is divided into multiple independent pricing graphs, each handling specific investment instruments or pricing calculations. This segmentation allows the system to process only relevant calculations rather than performing full recalculations for all instruments, reducing overall computational resource consumption while maintaining pricing accuracy for each segment.
Solution Approach 2:
The system pre-calculates and stores pricing information in advance where possible, and maintains pricing graphs in a ready-to-execute state. When pricing information is needed, the system can quickly retrieve or minimally update pre-computed values rather than performing complete real-time calculations, thus reducing computational resources while ensuring timely accurate pricing.
2Loss of time
If pricing information is generated in substantially real-time using a pricing graph with inter-dependent nodes, then the timeliness of pricing information is improved, but the system complexity increases
Solution Approach 1:
The pricing system uses a graph structure that segments pricing calculations into independent nodes representing specific instruments, data sources, or calculation steps. This modular node-based architecture reduces overall system complexity by allowing independent processing and easier maintenance of individual pricing components while enabling parallel real-time execution.
Solution Approach 2:
The pricing graph introduces intermediate nodes that act as mediators between raw market data and final pricing outputs. These intermediate nodes process and transform data in discrete steps, simplifying the overall pricing logic while maintaining real-time capability. The graph structure itself serves as an intermediary framework that organizes complex inter-dependent calculations into manageable sequences.
Data Source
AI summary
Systems and methods are provided to generate and exchange pricing information by a pricing engine. According to one embodiment, a pricing graph having a plurality of inter-dependent nodes is constructed. The pricing graph includes at least one input pricing information node and output pricing information node. In addition, at least one node provides information that effects information received by that node. A frame is established with at least one input pricing information slot and output pricing information slot. The pricing graph is then executed via a pricing engine in substantially real time. As a result, a value in the input pricing information slot is provided to the input pricing information node and a value for the output pricing information slot is received from the output pricing information node.