Balanced Instrument Groups for Interval Midpoint Price Updates
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Solution Overview
Problem
Existing computer systems face challenges in performing high-speed, accurate, and efficient midpoint price computations for large volumes of financial data, particularly in the context of exchange-traded funds (ETFs), where trade data is sparse and relying on trade prices can lead to stale prices that do not reflect true market conditions.
Innovation Solution
Divide a set of instruments into balanced groups based on activity levels and compute midpoint prices at intervals, using order data rather than trade data, to reduce computational and network load while ensuring accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If midpoint prices are computed for all instruments continuously using trade data, then price accuracy is maintained, but computational load and network demand increase significantly
Solution Approach 1:
The patent segments the set of instruments into multiple groups and processes each group at different intervals rather than continuously processing all instruments. This segmentation allows the system to maintain price accuracy for active instruments while reducing computational load for less active ones, directly resolving the contradiction between continuous computation and computational efficiency.
Solution Approach 2:
The patent implements periodic computation of midpoint prices at specified intervals rather than continuous computation. By computing prices periodically and using order data to supplement trade data, the system maintains accuracy while significantly reducing the computational burden, thus resolving the contradiction between continuous processing and computational efficiency.
2Reliability
If midpoint prices are computed using trade data only, then price reflects actual trades, but prices become stale when trades are sparse
Solution Approach 1:
The patent collects order data in advance and uses it to compute midpoint prices when trade data is unavailable or sparse. By preparing order data beforehand and using it as a supplement to trade data, the system prevents price staleness while maintaining reliability, effectively resolving the contradiction between trade-based reliability and timeliness.
3Loss of time
If update intervals are shortened to improve price timeliness, then price freshness increases, but computational and network resource consumption increases
Solution Approach 1:
The patent dynamically adjusts the computation strategy by using order data for instruments with shorter update intervals and trade data for instruments with longer intervals. This dynamic approach allows the system to maintain price freshness for active instruments while conserving resources for less active ones, resolving the contradiction between timeliness and resource consumption.
Data Source
AI summary
A set of instruments is divided into groups of instruments. Order data is received for the set of instruments. When an update interval elapses, a group is selected for update of a midpoint price. For each instrument in the group, a current midpoint price is computed based on order data received for the instrument. If the current midpoint price differs from a previous midpoint price, the current midpoint price is published.