Failed Trade Recovery Engine
Reduced failed trade recovery from ~10% to near 0 using automation.
# Description
Built an intelligent system to automatically detect and recover failed trades at Finosauras, reducing recovery failures from ~10% to near 0 through automation and AI-assisted analysis.
# Tech Stack
- Node.js event-driven architecture for real-time monitoring
- MongoDB for tracking trade status and failure patterns
- LLM integration for intelligent failure diagnosis
- Automated retry mechanisms with exponential backoff
- Alert system for cases requiring human intervention
# Problem
Failed trades required manual intervention to identify root causes and initiate recovery. The manual process was error-prone, time-consuming, and often missed critical recovery windows, resulting in a ~10% permanent failure rate.
# Solution
Developed an automated recovery engine that monitors trade execution, detects failures in real-time, diagnoses root causes using pattern matching and LLM analysis, and automatically triggers appropriate recovery workflows. Implemented smart retry logic and escalation paths for complex cases.
# Results
Reduced permanent trade failures from ~10% to under 0.5%. Decreased average recovery time from 2+ hours to under 15 minutes. Saved the operations team 10+ hours per week and prevented significant financial losses.