An AI trading system should not go live just because a notebook produced a strong equity curve. Before production, the system needs checks across data, modeling, execution, risk, and operations.
Start with data. Are the data sources licensed and reliable? Are timestamps correct? Are corporate actions handled? Are missing values treated consistently? Is the backtest using point-in-time data where needed? Data errors can create fake alpha faster than any model can detect.
Review validation. Was there a clean separation between training and testing? Was walk-forward validation used for time series? Were transaction costs included? Did the strategy survive different market regimes? Does performance depend on one parameter setting or one lucky period?
Inspect model behavior. What features matter most? Are predictions stable? Does the model behave sensibly when inputs are missing or extreme? Is there a fallback if the model fails? Complex models should be monitored for drift and unexpected output distributions.
Examine execution. Can the orders be filled at realistic prices? What is the expected turnover? What happens during gaps, halts, API outages, or low liquidity? Are order sizes capped? Is there a kill switch?
Define risk limits. Maximum position size, sector exposure, leverage, drawdown limits, and daily loss limits should be clear before capital is deployed. Risk rules should not be invented during a crisis.
Set monitoring. A live system needs logs, alerts, performance attribution, cost tracking, and reconciliation. If the live strategy differs from the backtest, the team needs to know quickly.
Finally, document responsibility. Who can change the model? Who approves deployments? How are incidents reviewed? AI does not remove accountability. It increases the need for a careful operating process.
Research Question
What should be reviewed before an AI-assisted trading system is considered ready for live operation?
Why This Matters
A live system fails in more ways than a notebook. Data feeds, model drift, execution, risk limits, monitoring, permissions, and incident response all matter. A checklist turns those risks into reviewable items.
Practical Example
Before launch, a team might simulate a missing data feed, an extreme model prediction, an API outage, and a daily loss limit breach. The goal is not to make the system complex; it is to learn whether the system has safe defaults when the normal path breaks.
Evidence Checklist
- Verify data licensing, timestamps, missing values, and point-in-time handling.
- Review validation, cost assumptions, and regime performance.
- Test order limits, kill switches, and failure modes.
- Define who can approve model, code, and parameter changes.
Known Limitations
- A checklist cannot replace judgment during novel market events.
- Operational risks change when brokers, exchanges, or infrastructure changes.
- Monitoring can fail if alerts are noisy or ignored.
- Governance documents are only useful if teams follow them.
Reader Actions
- Run a pre-launch tabletop review.
- Assign an owner to every alert and control.
- Document rollback steps before deployment.
- Review live performance against the backtest weekly at first.