Auction Engine Matching Financially Settled Contracts
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Solution Overview
Problem
Current systems for trading financially settled contracts face challenges in improving liquidity and volume, particularly in facilitating auctions for complex contracts with non-outright bids that do not have a one-to-one correlation with underlying contracts.
Innovation Solution
A computer system architecture that receives and matches participant bids, including outright and non-outright bids, to maximize economic surplus and transaction volume by optimizing the matching of bids across various dimensions, such as location, time, and commodity types, using an auction engine that can handle complex positions and collateral constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional trading systems are used for financially settled contracts, then system simplicity is maintained, but liquidity and transaction volume are insufficient
Solution Approach 1:
The system segments bids into different types (outright bids, non-outright bids, spread bids, strip bids) and processes them through specialized matching logic. This segmentation allows the system to handle complex trading scenarios while maintaining manageable system architecture by dividing the matching function into discrete, handleable components.
Solution Approach 2:
The system introduces multiple dimensions for bid matching beyond simple price pairing, including contract type dimensions (outright vs. non-outright), time dimensions (different settlement periods), and position dimensions (long/short). This multi-dimensional matching approach increases transaction volume by enabling more flexible and nuanced bid-ask pairing.
2Adaptability or versatility
If the system supports complex non-outright bids, then trading versatility is improved, but matching complexity increases
Solution Approach 1:
The system introduces an intermediary auction engine that acts as a mediator between complex non-outright bids and the matching process. This intermediary component translates diverse contract positions into a standardized matching framework, handling the complexity of spread bids, strip bids, and other non-outright positions without requiring complex custom logic for each bid type.
Solution Approach 2:
The matching system is designed with universal functionality that can handle multiple bid types (outright, non-outright, spread, strip) through a unified matching engine. This multi-functional approach allows the system to accommodate diverse contract positions while maintaining a single, consistent matching process rather than requiring separate specialized systems for each bid type.
3Productivity
If batch bid matching is implemented to maximize economic surplus, then trading efficiency is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and categorizing bids before the main matching computation. Bids are segmented by type, filtered for compatibility, and organized into structured formats ahead of the batch matching process. This preliminary organization reduces the computational burden during the actual economic surplus maximization calculation.
Solution Approach 2:
The system implements partial matching in batch processing, where bids are matched in manageable groups rather than all at once. This allows the computational task to be divided into smaller, more efficient batches that can be processed sequentially, reducing peak computational power requirements while still achieving comprehensive matching across all bids.
Data Source
AI summary
Various embodiments show a system for conducting an auction for a plurality of financially settled contracts: The system may comprise at least one processor. The at least one processor may be programmed to receive a plurality of first participant bids from a first participant and a plurality of second participant bids from a second participant. The at least one processor may also be programmed to match a batch of bids, where the bids may be linear combinations, to create a plurality of awarded bids that may maximize an economic surplus or maximize a volume of awarded bids. Degenerate price solutions may be solved by minimizing variations from historic pricing levels for a contract. The contracts may include, for example, an oil contract, a coal contract, a natural gas contract, an electricity contract, a weather contract, a weather-related events contract, a commodities contract, a location specific service contract (e.g., passenger contract and/or freight contracts), a financial derivative contract or credit default contract on any of an entity's issued securities.