Python-first systemic-risk research software for evidence-graded, bitemporal dependency graphs. Import JSON, CSV, GraphML, or NetworkX; run bounded shocks, compare constrained interventions and sensitivity surfaces, and export recomputation-verifiable RiskPacks. Includes 16 scenario-only cases and 11 connector contracts.
Machine learning and optimization models designed to enhance financial system stability through portfolio optimization, risk forecasting, and network-based financial risk analysis.
