The real risk isn’t “the model” — it’s ungoverned change
In production, the biggest failures rarely come from one big mistake. They come from small changes that weren’t treated like changes:
- a configuration tweak,
- a dependency upgrade,
- a minor data schema update,
- a “temporary” override that stayed forever.
In an autonomous trading system, that’s dangerous because the system acts continuously. If a change creates a subtle bug, you don’t just get an error—you can get behavior.
Change control is the discipline of making sure:
- every change is intentional,
- evaluated against risk,
- test-gated,
- deployed safely,
- and reversible.
Define “change” broadly
A useful definition of “change” includes:
- code changes (obvious)
- model changes (weights, prompts, features)
- configuration changes (thresholds, limits, schedules)
- data changes (schemas, vendors, mappings)
- infrastructure changes (timeouts, queues, autoscaling)
- permissions and access changes
- operational procedures (runbooks, escalation rules)
If you only control “deploys,” you miss most of the risk.
A simple risk classification that actually works
Classify changes into 3 buckets:
Class 1: Low-risk (routine)
- logging improvements
- dashboards
- non-functional refactors (with tests unchanged)
- documentation/runbook updates
Class 2: Medium-risk (behavior-adjacent)
- execution routing logic
- data normalization changes
- feature flags that affect decisions
- dependency upgrades that touch runtime behavior
Class 3: High-risk (behavior-defining)
- model updates
- decision policy changes
- risk limit changes
- anything that changes what gets traded, when, or how
Rule: if you’re unsure, treat it as higher risk.
The change request: what must be written down
Before touching production, write:
- What is changing?
- Why now? (triggering evidence)
- Expected effect (including “no behavior change intended”)
- Risks and failure modes
- Test plan (what proves it’s safe?)
- Rollback plan (how do we revert fast?)
- Metrics to watch during rollout
This doesn’t need to be a bureaucracy. It needs to exist.
Test gates: you need more than backtests
For production trading systems, “it backtests” is not a safety guarantee.
Useful gates include:
- Unit tests for deterministic logic
- Simulation tests for pipeline integrity (can it run end-to-end?)
- Shadow mode (compute decisions without executing)
- Replay tests (run on historical feeds as if live)
- Canary environments (small-scale exposure, monitored closely)
- Regression dashboards that compare key metrics pre/post
The goal isn’t to prove it will “make money.”
The goal is to prove it won’t misbehave operationally.
Rollouts: stage, don’t jump
For Class 2–3 changes, prefer staged rollout patterns:
- Feature flags (default off; enable gradually)
- Canary release (small subset of operation first)
- Time-boxed trial with explicit stop criteria
- Automatic rollback if guardrails trigger
Every rollout should have:
- a “green path” (what success looks like)
- and a “red line” (what forces disable/rollback)
Guardrails and stop conditions
Define stop conditions that are operational, not emotional:
- repeated data quality failures
- unexpected decision distribution shifts
- execution anomaly rate
- latency spikes beyond thresholds
- risk constraints breached
- missing heartbeats from critical components
If you can’t stop the system quickly and safely, you don’t have autonomy—you have a liability.
Decision logs: make the system auditable
For each production decision cycle (or batch), log:
- input versions (data source + schema version)
- model/policy version
- configuration version
- decision output
- execution outcome (if executed)
- correlation IDs to trace across services
This is how you debug the “weird stuff” later.
Post-deploy reviews: close the loop
After rollout:
- confirm expected metrics moved (or didn’t)
- review any alerts/incidents during the window
- record what you learned
- and remove “temporary” flags/overrides
Most systems rot from unclosed loops.
