Six areas of work, each scoped to your timeline and budget — from a single break-resolution overhaul to a ground-up reporting stack.
Automated daily P&L, VaR, greeks, and exposure reports. Power BI and Excel-native delivery tailored to your workflow.
Multi-counterparty position reconciliation across prime brokers, custodians, and fund administrators. Break resolution at scale, with identifier-agnostic matching and per-field tolerances.
Production-grade BLPAPI pipelines for pricing, reference data, and security master maintenance. Static data quality control built in.
End-to-end Python data infrastructure: ingestion, QC, normalization, and historical snapshots. Designed for auditability and ops resilience.
Factor decomposition, sector attribution, and performance contribution analytics across equities, credit, and derivatives books.
Automated exception flagging, tolerance-based validation, and regulatory-ready documentation. Reduce ops overhead, increase confidence.
Getting from a raw figure to something your desk can act on has traditionally meant two jobs on two timelines. The point of this work is to collapse the second into something instant and self-service.
Your vendor, clearinghouse, or internal model runs the actual calculation — on the source you already trust, connected however it lives: file, database, or API.
The reporting, break analysis, and reconciliation that a quant used to do by hand — automated, documented, and reproducible instead of a day of manual turnaround.