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PROJECT DOSSIER // FINANCIAL TECHNOLOGY

FINANCE O.S.

A comprehensive institutional trading platform interface built for high-frequency data ingestion, event-driven CQRS data synchronization, and real-time WebGL chart visualization.

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descriptionREADME.md

MARKDOWN SPEC

Finance O.S. Architecture

Finance O.S. was conceived to address the fundamental limitations of monolithic financial transaction processing. By decomposing the transaction lifecycle into distinct, event-driven services, we achieved a 400% increase in throughput while maintaining strict ACID compliance across distributed nodes.

The Challenge

Legacy systems rely on synchronous database locks, creating bottlenecks during high-volume trading periods. The requirement was to design a system capable of processing 100k+ TPS globally with sub-50ms latency, without compromising data integrity or auditability.

Architectural Decisions

  • Event Sourcing: All state changes are stored as an immutable sequence of events.
  • CQRS: Separation of read and write models to optimize performance independently.
  • Kafka Backbone: Utilizing Apache Kafka as the central nervous system for inter-service communication.
  • Next.js & WebGL: Sub-millisecond frontend state hydration with hardware-accelerated canvas rendering.
src/architecture/pipelinetypescript
export class EventProcessor implements IProcessor {
  private readonly eventStore: EventStore;
  private readonly snapshotInterval: number;

  constructor(config: ProcessorConfig) {
    this.eventStore = new EventStore(config.dbUrl);
    this.snapshotInterval = config.snapshotInterval || 1000;
  }

  public async process(event: LedgerEvent): Promise<void> {
    // 1. Validate cryptographic signature
    if (!await verifySignature(event)) {
      throw new InvalidSignatureError(event.id);
    }

    // 2. Append to immutable event store
    await this.eventStore.append(event);

    // 3. Emit stream update to Kafka bus
    await publishToKafka('ledger-updates', event);
  }
}

System Impact

The resulting architecture not only met the performance criteria but provided a robust foundation for future scaling. Decoupling services allowed independent teams to iterate without risking system-wide instability.