An AI-generated cBot change is still your code to understand and test. Before you run it on an account, compare the patch with a written requirement, inspect every changed line, and decide what evidence would show the behavior is wrong.
The risky moment is not when an assistant produces a neat-looking method. It is when plausible code slips into a strategy without anyone checking what it changes. Microsoft’s guidance says to read and understand each generated change and test it at least as thoroughly as handwritten code (Microsoft Learn). NIST’s Secure Software Development Framework likewise frames security practices as part of the development lifecycle, rather than a final inspection (NIST SP 800-218).
Start with a requirement, not a prompt
A prompt is not a test specification. Write down the intended behavior in terms you can observe: what event triggers the change, what must remain unchanged, and what should happen at the boundary cases.
For example, a hypothetical request to add a session filter might specify the session boundary, the time zone source, and how an already-open position is handled. Those choices are strategy requirements, not facts an assistant can safely infer from a vague request.
Keep that requirement beside the code review. It gives you a control question: does the diff implement this behavior, and only this behavior?
Read the diff as a change to risk
Do not review only the new method. Check its callers and the values it can alter: entry conditions, order volume, stop handling, position selection, timing, and any state shared across runs. A small edit can change more than its filename suggests.
Use a compact review record:
| Check | What to write down |
|---|---|
| Purpose | The requirement this patch is meant to satisfy |
| Scope | Files and behavior that changed, plus behavior that must not change |
| Assumptions | Time zone, symbol, account state, and missing-data behavior |
| Failure case | A concrete input or sequence that should be rejected or handled safely |
| Evidence | What compile, test, or demo observation you will inspect |
This is a workflow, not a guarantee that a review will find every defect. NIST describes its framework as a set of practices that can be integrated into different development lifecycles; it does not certify a particular cBot or trading outcome (NIST SSDF).
Test the behavior in layers
A successful compile answers a narrow question: the code can be built in that environment. It does not establish that the strategy still behaves as intended. Microsoft specifically warns that generated code can look plausible while being subtly wrong, and calls for testing it at least as thoroughly as handwritten code (Microsoft's AI code guidance).
Use a staged check, keeping the original version available for comparison:
- Build: confirm the edited project compiles; resolve warnings you do not understand rather than hiding them.
- Focused cases: exercise the changed rule at its boundary, with missing or unusual inputs, and when an existing position is present.
- Historical run: compare the edited version with the unchanged control over the same data and settings. Inspect trades around the changed condition, not only the summary result.
- Demo observation: if the code is intended to run live, observe its logs and actions in a demo environment before considering any broader use.
These are review steps, not proof of future performance. A historical run or a short demo observation cannot establish that a strategy is profitable or safe in every market condition.
Keep secrets and authority out of the review loop
Review the assistant’s input boundary too. Microsoft advises against putting credentials, personal data, or proprietary logic into prompts unless the relevant policy permits it, and recommends validating inputs rather than trusting them as-is (Microsoft Learn). For a trading-code task, use synthetic examples and remove account identifiers, tokens, and broker-specific private details from any context you share.
The same discipline applies to execution authority. A code suggestion should not silently gain permission to place or manage orders merely because the task was described as “make the bot work.” Our guide to separating trading-AI permissions in cTrader covers that distinct boundary; a cBot decision log helps reconstruct what the running system did.
When should an AI-written patch be rejected?
Reject or revise a patch when you cannot explain its changed behavior, reproduce its expected result, or identify how it fails safely. Ask for a smaller change and a test case, then review the new diff from the beginning. Do not treat an assistant’s explanation as evidence that the code matches it.
A useful review ends with a traceable chain: requirement, diff, test case, observed result, and a decision to keep or revert. That process can make a change easier to inspect; it does not make the strategy’s edge more real.
The practical rule is plain: if you cannot explain what the patch changes and show how you checked it, it is not ready for an account.
GitHub also notes that generated unit tests may leave scenarios uncovered, while NIST’s DevSecOps example uses repeatable control gates and feedback before changes proceed (GitHub Copilot responsible-use guidance; NIST DevSecOps reference model).