Compliance in automated trading is not only about regulation. It is about proving that strategies are reviewed, changes are controlled, risks are monitored, and decisions can be reconstructed when needed.
Create approval paths for strategy changes
Live strategy changes should require review, testing, and documentation. Even small parameter changes can alter risk. A governance process helps prevent accidental exposure changes and creates accountability for production decisions.
Preserve audit trails
Systems should record who changed what, when it changed, which version ran, what orders were generated, and how risk checks responded. Audit trails are valuable for compliance, investor reporting, debugging, and internal discipline.
Treat models as controlled assets
Models should have owners, validation history, monitoring plans, and retirement criteria. This is especially important for machine learning strategies where behavior can drift over time. Governance makes model risk visible.
Strategic takeaway
Good governance does not slow serious trading teams down. It creates the trust required to scale automated systems responsibly.
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