Order Match Allocation Priorities for Fairer Market Liquidity
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Solution Overview
Problem
Current financial instrument trading systems face challenges in maintaining market integrity, transparency, and predictability due to the influence of human interaction and speed-based advantages, leading to inefficiencies and reduced market liquidity.
Innovation Solution
Implementing a hybrid matching algorithm that gradually shifts from time-based priority to proportional allocation, grouping orders by temporal proximity, and equalizing order processing rates to incentivize market making behavior and reduce speed-based penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time-based priority matching is used, then market transparency and predictability are improved, but speed-based advantages create unfair benefits and reduce market liquidity
Solution Approach 1:
The system dynamically adjusts order matching priorities based on order age and market conditions. Instead of static time-based priority, the match engine transitions from time-based to proportional allocation as orders age, creating a dynamic system that adapts to market conditions while maintaining fairness and liquidity
Solution Approach 2:
The system changes the matching parameter from time-based priority to proportional allocation based on order characteristics and age. This parameter change allows the system to balance between fairness (time-based) and liquidity encouragement (proportional), resolving the contradiction between market integrity and liquidity
2Productivity
If speed-based order processing is used, then trading efficiency is improved, but excessive speed investments are required and market fairness deteriorates
Solution Approach 1:
The system performs preliminary actions by aging orders and evaluating their characteristics before matching. This allows the match engine to apply appropriate allocation methods (time-based for new orders, proportional for aged orders) without requiring excessive processing speed during critical matching moments, reducing the arms race for speed
Solution Approach 2:
The system introduces an intermediary evaluation layer that assesses order characteristics and determines appropriate matching priorities. This intermediary process decouples the matching decision from pure speed-based processing, allowing efficient yet fair allocation without requiring participants to invest excessively in speed
3Productivity
If proportional allocation is used, then market liquidity is improved, but predictability and transparency are reduced
Solution Approach 1:
The system applies different allocation qualities to different orders based on their characteristics and age. New orders receive time-based priority treatment for predictability, while aged orders receive proportional allocation for liquidity. This local differentiation resolves the contradiction by applying the right allocation method in the right context
Data Source
AI summary
The disclosed embodiments relate to regulation of a rate of incoming orders by buffering or otherwise batching orders together as they are received and subsequently forwarding batches of orders to a match engine for processing thereby in a manner which may equalize orders from traders having varying abilities to rapid submit orders or otherwise capitalize on market events. The disclosed embodiments further relate to prioritizing the matching of resting orders against an incoming order. In particular, the disclosed embodiments alter the priority of a given resting order to match against an incoming order, relative to other suitably matching resting orders, as a function of how long the orders have been resting on the order book.