Risk Operating
System v3.0
Production-grade commodity risk platform for oil & gas trading firms. Built on VectorLedgerDB — the open-source analytical engine that outperforms Snowflake and Databricks for financial risk workloads.
Nine integrated layers.
One risk operating system.
VectorLedgerDB
Ontology-based no-join columnar datalake. Cumulative-at-load metrics. MVCC late-data correction.
Risk Dashboard
Live VaR, ES, rolling windows, backtesting, desk decomposition — all in one interactive view.
Matrix Risk Engine
48× faster than Snowflake SQL. NumPy SIMD matrix operations for 5-year historical simulation.
Stress Testing
COVID-19, Russia-Ukraine, OPEC cuts, Hormuz Strait — 7 historical and hypothetical scenarios.
Data Lineage
Full provenance trace from dashboard number back to individual ENDUR trade rows.
Architecture
Modular RiskOS layers: ENDUR ingestion → VectorLedgerDB → Risk Engine → Visualization.
Snowflake Layer
Regulatory compliance data store. Snowpipe real-time ingestion, row-level security, dynamic data masking.
dbt Pipeline
13 models across 4 layers: staging → intermediate → compliance → marts. 80 tests. 5-min cadence.
Compliance Data
EMIR Refit, MiFID II, Basel III FRTB capital, and counterparty credit exposure — all in Snowflake.
Beyond Snowflake.
Beyond Databricks.
Purpose-built for financial risk data patterns. No joins, no rewrites, no proprietary lock-in.
| FEATURE | SNOWFLAKE | DATABRICKS | VECTORLEDGERDB |
|---|---|---|---|
| Storage Engine | Micro-partitions (proprietary) | Delta Lake (Parquet) | Chunked Parquet + Zone Maps |
| Join Strategy | SQL JOIN (full scan) | Spark JOIN (shuffle) | No joins — cumulative-at-load |
| Late Data | MERGE INTO (rewrite) | Delta MERGE (rewrite) | MVCC append-only (zero rewrite) |
| Schema Model | Fixed DDL | Schema-on-write | Semantic ontology URIs |
| VaR Compute | SQL window functions | Spark DataFrame | NumPy SIMD matrix (48× faster) |
| Audit Trail | Time travel (file-level) | Delta log | Immutable MVCC correction log |
| Cost | $$$$ | $$$$ | Open source (free) |
Five VaR methods.
One matrix engine.
Historical Simulation
403msFull revaluation over 1-year lookback window
Parametric Normal
18msDelta-normal with 21-day EWMA volatility
Student-t
22msFat-tailed parametric (ν=5 degrees of freedom)
Monte Carlo
92ms50,000 correlated scenarios via Cholesky decomp.
GARCH(1,1)
84msVolatility-adjusted historical simulation
Explore the Risk Engine
Interactive VaR surface, backtesting results, and Monte Carlo convergence.