Conditional Trading Offers via Participant Profiles
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Solution Overview
Problem
Modern trading systems lack the ability to set prices based on information about transacting parties, leading to inefficient trading as buyers and sellers remain anonymous, resulting in prices that do not reflect the trading history or behavior of participants.
Innovation Solution
The system associates trading entities with identifiers, generates profiles based on their trading history, and uses these profiles to create conditional offers that are tailored to individual traders, allowing providers to offer different prices to different takers based on their trading behavior, thereby increasing transparency and informed trading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If trading systems use anonymous participants, then trading speed and automation are improved, but price accuracy and information quality deteriorate
Solution Approach 1:
The system segments participant information by creating discrete profiles associated with identifiers. Each profile contains specific trading history data that can be independently analyzed and used for pricing, while maintaining separation between different participants' information.
Solution Approach 2:
The system performs preliminary actions by pre-collecting and storing trading history information for participants before trading occurs. Profiles are built in advance based on past trading behavior, enabling informed pricing decisions without requiring real-time information exchange during transactions.
2Ease of operation
If trading systems use anonymous participants, then ease of operation is improved, but pricing accuracy deteriorates
Solution Approach 1:
The system introduces an intermediary mechanism in the form of profile-based pricing. Instead of direct participant interaction or complete anonymity, the profile acts as an intermediary that carries information about trading history and behavior, enabling accurate pricing while maintaining operational convenience through automated profile matching.
Solution Approach 2:
The system implements feedback by using historical trading data stored in profiles to inform future pricing decisions. The pricing mechanism receives feedback from accumulated trading history, allowing prices to reflect actual participant behavior patterns while maintaining ease of operation through automated feedback processing.
3Productivity
If conditional offers are made to identified participants, then trade volume and transparency are improved, but device complexity increases
Solution Approach 1:
The system uses copying by creating profile representations of participants based on their trading history. These profile copies contain essential information needed for conditional pricing without requiring direct access to or complexity of the actual participant identities, enabling simplified yet effective personalized offers.
Solution Approach 2:
The system applies parameter changes by modifying pricing parameters based on profile characteristics. Instead of changing the fundamental trading system structure, the invention adjusts pricing parameters dynamically according to stored profile data, increasing trade volume through personalized offers while maintaining relatively simple system architecture.
Data Source
AI summary
According to one embodiment of the present invention, a method for generating conditional offers for semi-anonymous trading participants is provided. According to one embodiment of the present invention, a method comprises associating a trading entity with an identifier; acquiring trade history information including a history of trading transactions associated with said identifier; and receiving an offer from a Liquidity Provider based on said trade history information, said offer being only made to the trading entity associated with one of said identifiers.