Case study
Macro Event Risk Filter
A risk-control concept for filtering trading activity around scheduled macro events and other high-volatility windows.
Overview
A trading risk filter that models scheduled macro events as operational constraints for strategy evaluation or live decision support.
Problem
High-impact economic releases can change liquidity, volatility, and execution risk. Strategies need explicit rules for how those windows are handled.
What I Built
- An event-risk architecture for ingesting scheduled events and exposing risk windows to strategy logic.
- A decision model for allow, warn, reduce, or block states around event timing.
- A Foundry-style operational interface for reviewing strategies, policies, and event coverage.
Technologies Used
- Python
- Market calendars
- Risk controls
- Automation
- Palantir Foundry workflows
Key Technical Challenges
- Representing event risk without overstating predictive power.
- Making filter behavior deterministic and easy to audit.
- Coordinating calendar data with strategy execution windows.
What It Demonstrates
- Risk-control thinking
- Operational modeling
- Quant workflow design
- Calendar-aware automation
Current Status
Presented with a working interface screenshot and design notes; source details are kept private where they include operational trading workflow context.