Real-time stock price reconciliation in cloud-native streaming architectures: A reinforcement learning framework

Electronic trading platforms ingest billions of market events daily from distributed exchanges, and transmission latency, schema drift, and feed interruptions routinely produce cross-venue price discrepancies. Existing reconciliation pipelines depend on end-of-day batch validation or reactive stream monitoring, neither of which supports microsecond-level decisions or autonomous recovery. This study proposes a cloud-native reconciliation architecture built on Apache Kafka, Kubernetes, and a closed-loop Auto-Healing Exception Manager (AHEM). Four mechanisms are integrated: a Temporal Consensus Reconciliation Algorithm (TCRA) fusing multi-venue prices through latency- and error-weighted consensus; an Adaptive Confidence Scoring Engine (ACSE) continuously estimating feed trustworthiness; Predictive Exception Forecasting (PEF), a streaming machine-learning layer anticipating failures ahead of occurrence; and a reinforcement-learning healing agent selecting corrective actions without operator involvement. The framework was validated against 90 trading days of simulated NASDAQ, NYSE, ARCA, and CBOE feeds totaling 10.2 billion events. Results show reconciliation accuracy of 99.997 percent, a thirty-fold reduction in exception resolution time relative to rule-based automation, and a 74 percent decline in data-quality incidents, while sustaining 10.3 million events per second at sub-millisecond latency. These outcomes indicate that consensus-based, self-healing pipelines materially improve data integrity and resilience for cloud-native trading infrastructure.