Case study
Macro Event Risk Filter
A risk-control concept for filtering trading activity around scheduled macro events and other high-volatility windows.
- Macro events
- Risk filter
- Trading operations
Screenshot / demo TODO
Add project screenshots, architecture diagrams, backtest reports, or repository previews here.
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
- A placeholder 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.
- TODO: add exact event source, screenshots, and validation notes.
Technologies Used
- Python
- Market calendars
- Risk controls
- Automation
- TODO: add exact data source
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
TODO
- Add screenshots or diagrams.
- Add repository, demo, or write-up links when they are public.
- Add measured results only when they can be accurately sourced.