Corvil Electronic Trading Data Warehouse

Precision visibility into trade execution performance to help maximize alpha.

Underpinned by Corvil streaming data, Corvil Electronic Trading Data Warehouse provides transparency into transaction execution quality to correlate client trading behavior with execution path and counterparty performance.

The platform delivers precision intraday visibility from individual trades to aggregate transaction outcomes, providing a lens into how trading is impacted by technology stack performance.

Correlate Trading Outcomes to IT Execution Path and Counterparty Performance

Aggregate transaction monitoring allows flow isolation for specific clients, strategies, order types, and counterparties to reveal how the execution path impacts trading success from client to market.

  • Associate path execution with trade performance

  • Detect intermittent problems

  • Monitor transaction failures

  • Reconcile market, application and client order flow

Trace Transaction Execution Through IT Infrastructure

Full trade traceability from issuer to market, across every hop, translation and parent/child order relationship, to isolate degraded decision and transmission latencies.

  • Search for any trade ID

  • Trace path

  • Find slow hops

  • Locate lost messages

Detect Problems as they Happen to Mitigate Business Impact

Machine learning and analytics provide real-time monitoring and alerting on any trade and performance metric.

Detect anomalies across:

  • Client behavior

  • IT & application performance

  • Counterparty performance

  • Trading & business metrics

Price Impact Analysis at Nanosecond Granularity

Monitor every aspect of Algo/SOR performance and how strategy execution is impacted by price quality, decision latency and execution path to market.

Monitor Algo/SOR execution performance:

  • Quote to order latency

  • Decision latency

  • Arrival / decision price

  • Slippage

  • Shortfall

  • Price impact

Deliver High-Quality Execution Reports

Brochure quality reporting provides brand-aligned execution transparency to clients and internal stakeholders.

  • Corporate branded

  • Report templating

  • Data-driven content generation

Business Challenges

Sell-Side

The buy-side increasingly uses transaction cost analysis tools and algo wheels to scrutinize their broker trade performance, making data-driven decisions when weighing up execution options. The impact for the sell-side is increasing flow reallocation and client churn.

Corvil Electronic Trading Data Warehouse allows the sell-side to measure trade plant performance using the same metrics and criteria that the buy-side makes execution routing decisions upon. It provides the sell-side with real-time insight into the impact of plant performance and venue execution on their client's trading outcomes. Machine learning and analytics allow the business to move a proactive footing, so service impacting problems and changes in client behavior can be detected as they happen.

Market Makers

Regulators require exchanges to monitor the volume of unexecuted orders to prevent disorderly trading conditions (e.g., MiFID II RTS-9). Exceeding exchange order-to-trade ratios (OTR) limits can incur fines or suspensions.

Corvil Electronic Trading Data Warehouse allows market makers to walk an optimal line between high-frequency order replacement to deliver best bid-offer spreads while being compliant with regulatory requirements that limit the volume of unexecuted orders placed on the exchange by members.

Buy-Side

Traditionally the buy-side has had to opt between distinct tools to provide insight on infrastructure performance, service performance, business intelligence and trade execution analytics. The separation of tools makes it difficult to directly relate market data performance, plant performance, and counterparty execution with trading outcomes.

Corvil Electronic Trading Data Warehouse enables proactive operations and business management without waiting for the trading desk to complain, or worse, lose their edge. Advanced machine learning baselines key execution quality performance metrics like tick-to-order latency, and alerts on anomalies in trading behavior and execution quality, as it happens, in real-time.