# Drift Monitoring and Post-Trade Review

## Objective
Live systems fail slowly before they fail obviously. Drift monitoring detects when the strategy, parser, data, or execution quality no longer matches the assumptions validated in research and paper trading.

## Drift Domains
1. Data drift
2. Parser drift
3. Feature drift
4. Candidate drift
5. Execution drift
6. Performance drift

## Data Drift
Monitor for:
- missing mandatory fields by source
- source latency changes
- unexpected filing/exhibit parse failures
- symbol mapping failures
- bar completeness issues

Suggested metrics:
- null rate by field
- source freshness SLA miss rate
- adapter error rate
- duplicate raw payload rate
- daily event count vs rolling baseline

## Parser Drift
Monitor for:
- confidence score distribution shift
- event_type mix change
- oneoff_flag rate change
- guidance_direction distribution shift
- increase in manual review rate

Use a gold set and periodic re-score process. Any meaningful degradation must freeze capital ramp.

## Feature Drift
Monitor for:
- z-score distribution change by feature
- feature null rate
- clipping rate
- value range violations
- population stability index or equivalent simple distribution checks

## Candidate Drift
Monitor for:
- candidate count per day
- pass rate by rule gate
- score distribution
- rank order concentration
- sector concentration

## Execution Drift
Monitor for:
- live slippage vs modeled slippage
- open-to-fill latency
- partial fill rate
- cancel/replace rate
- rejected order rate
- discrepancy between planned and realized size

## Performance Drift
Monitor for:
- rolling expectancy
- rolling hit rate
- rolling average R multiple
- MFE/MAE relationship
- realized vs backtest tolerance band

## Alerting Rules
- Warning alert: threshold crossed once or mildly.
- Action alert: threshold crossed repeatedly or materially.
- Freeze alert: capital ramp freeze condition met.
- Kill alert: operational integrity risk.

## Post-Trade Review Process
Every closed trade must receive a structured review.
Fields should include:
- thesis type
- event category
- approval path
- whether data was clean
- whether parser output looked reasonable
- whether execution matched plan
- exit reason
- what worked
- what failed
- preventable vs non-preventable
- follow-up action

## Weekly Review Questions
- Are losses coming from market regime or operational issues?
- Are parser changes affecting candidate quality?
- Is attention overlay helping or just adding noise?
- Are live fills materially worse than expected?
- Are more trades being blocked by guards than research expected?
