Graph-Based Duplicate Trade Detection and Idempotency Framework Implementation in Distributed Electronic Trading Systems

Abstract

To prevent the reprocessing of the same trade message in different distributed financial infrastructures, electronic trading systems must have powerful duplicate trade detection protocols. Redundant messages are a result of network timeouts, TCP retransmission protocols, upstream retry queues, and manual resubmission workflows that are part of heterogeneous trading structures. Idempotency models define message uniqueness by using composite business keys, cryptographic fingerprints using the SHA-256 hashing functions, and deduplication logic on time windows that trades off between accuracy of detection and scalability of computation. Graphed graph frameworks are enhanced with blockchain and deliver distributed data models to specify intricate trade relations in the form of immutable ledger records, smart contract validation logic, and multi-channel designs, which assure information integrity across trading networks. Multi-channel correlation algorithms differentiate between actual trade amendments and replay events based on machine learning classification models and partial fill cases and cross-venue execution strategies. Strategies of implementation are used to optimize parameters of tolerance windows with the use of hierarchical composite key matching, progressive sampled indexing, and container-based pre-fetching strategies. Microsecond-latency duplicate-detection In-memory caching architectures in conjunction with Bloom filter probabilistic structures can achieve duplicate detection at millions of trade messages per day to protect downstream risk management and regulatory reporting systems against position inflation and compliance violations.