Define “Broker API” before verifying details
A Broker API is a technical interface that lets a client system send requests to a brokerage (or brokerage technology layer) and receive responses, such as confirmations, order status updates, and account-related information. In this context, “verifying information” means confirming that a description of the API’s behavior matches what the API actually does under stated conditions.
Verification is easier when you separate stable mechanics from variable provider conditions. Stable mechanics are behaviors that should not change with market randomness—for example, how request formats are validated, how authentication is performed, and what fields appear in responses. Variable conditions are things that can differ across environments or times, such as execution outcomes, service load, costs, and network performance.
Use a source hierarchy you can test
Use a hierarchy of evidence from most authoritative to most empirical:
- Official API documentation: Look for request/response schemas, error codes, authentication method descriptions, and documented constraints.
- Broker legal or technical reference documents: These can clarify what the API is intended to do, how it handles data, and what limitations apply.
- Platform or protocol specifications (when applicable): If the API uses standardized protocols or message formats, the underlying specification helps validate semantics.
- Your own controlled tests: Empirical verification is essential for anything not fully specified, or when documentation is ambiguous.
This approach avoids treating marketing-level descriptions as technical truth. It also keeps verification reproducible: the same test inputs should lead to the same “type” of outputs, even if real-world outcomes differ.
Verification steps that are reproducible
Follow a step-by-step checklist that records assumptions and produces evidence you can compare later.
Step 1: List claims and classify them
Create a table with three columns: Claim, What would prove it, and Stability class (stable mechanic vs variable condition).
- Example stable mechanic claim: “If a request body is missing a required field, the API returns a structured error response.”
- Example variable condition claim: “This order will fill immediately.” That one is not a verifiable API property in isolation.
Step 2: Map each claim to a specific documented artifact
For each stable mechanic claim, identify the relevant documentation section: schema fields, validation rules, response structure, or error handling guidance. If no section exists, flag it as documentation gap and plan an empirical test.
Step 3: Define assumptions for any example or calculation
Even for simple examples, state assumptions:
- What environment you are using (sandbox vs production).
- Which identifiers you will use (e.g., a test account, fixed instrument codes).
- Whether you expect the request to be accepted or rejected (because the inputs you chose matter).
For timestamps and ordering, record the timezone and include a consistent ordering method. For payloads, store the exact JSON (or equivalent) you sent.
Step 4: Run controlled tests with deterministic inputs
Use tests that focus on structure and validation rather than predicting market outcomes.
- Send well-formed requests that should be accepted.
- Send intentionally malformed requests that should be rejected.
- Vary one input at a time (e.g., missing field, invalid format, wrong type) while keeping everything else constant.
Capture the full response: status code (if applicable), error code, message body, and any correlation identifiers.
Step 5: Perform an “afrondingscontrole” on your evidence
Before you conclude, verify that you actually observed the behavior you tested:
- Did you collect the response for every request you sent?
- Did you record the exact payload and timing?
- Did the client-side networking layer (timeouts/retries) interfere with what you think happened?
If evidence is missing or inconsistent, repeat the test with clearer instrumentation.
Step 6: Interpret results within the limitations
Do not treat a single test run as universal truth. An API might behave correctly for one request shape but fail under load or when a rate limit is exceeded. Compare observed behavior across multiple runs, especially for edge cases.
Material limitations and failure modes to verify against
When verifying Broker API information, expect uncertainty and test for failure modes.
Common material limitations:
- Rate limits and throttling: Requests may be rejected or delayed when you exceed documented limits. - Request validation failures: Missing or invalid fields can cause structured errors; verify how those errors are represented.