Business Case
The Argentine FX market had persistent pricing inefficiencies due to rapid regulatory changes. Traders were manually executing spread arbitrage but couldn’t keep up with daily policy shifts. I built a multi-threaded Python system that automated the entire process.
Impact
- Automated the entire trading desk workflow for FX operations
- Captured ~20% market share through automated pricing reactions
- Managed institutional capital for the company’s own portfolio
- Cut churn by 32% using ML-driven user segmentation
- Reduced client onboarding from 72h to 24h via process automation
- Led to my promotion from trader to trading desk leadership, then to Head of BI
- Team: Led a team of 4 at IOL
Technical Details
- Multi-threaded execution using Python multiprocessing (Pools, Queues, Locks)
- Real-time price monitoring via WebSocket feeds and broker APIs
- Automated round-trip trade execution when spreads exceeded thresholds
- Tkinter GUI for manual overrides by the trading desk
- Mobile push notifications for critical alerts