Measure API request latency and strategy or order cycle time as separate durations. Use a monotonic clock for elapsed time, record each measurement in a histogram, and define precisely which events begin and end a cycle. This gives you useful tail-latency and outcome data without mistaking a quick HTTP response for a completed trading workflow.
Decide what each duration means
A request metric measures the time spent in one client operation—for example, from just before awaiting a market-data call until that call returns or raises an exception. A cycle metric measures a larger unit of work that you define, such as one strategy evaluation or an order workflow.
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Keep those boundaries explicit in metric descriptions and dashboards. A strategy cycle might start when a scheduled evaluation begins and end when it finishes submitting its intended actions. An end-to-end order measure might instead begin when the bot decides to submit an order and end at a specified event, such as acknowledgment or fill confirmation. Those are different measurements; choose the one that answers the operational question you have.
A local request duration is not automatically “exchange latency.” It can include local scheduling, network transit, server processing, and client-side work. Without defined endpoints and suitable clock synchronization, the measurement does not isolate time spent by the exchange.
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Use a monotonic clock for elapsed time
For durations measured within one process, use a monotonic timer such as Python’s time.perf_counter(). Its reference point is not meaningful as a timestamp; subtract two readings to obtain an elapsed duration. Unlike wall-clock time, a monotonic clock is not affected by system-clock adjustments.
Keep wall-clock timestamps separately if you need to correlate events across hosts or with external logs. Do not subtract wall-clock timestamps as a substitute for an in-process duration unless you have accounted for clock synchronization and adjustments.
Instrument the awaited request and classify its outcome
Create the metric instrument once during application startup, then record one observation per completed request attempt. In asyncio code, the timing boundary must include the await; timing only the creation of a coroutine measures almost none of the operation.
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started = time.perf_counter()
outcome = "error" # safe default if an unexpected exception occurs
try:
response = await exchange.fetch_ticker(symbol)
outcome = "success"
return response
except TimeoutError:
outcome = "timeout"
raise
except Exception:
raise
finally:
elapsed_seconds = time.perf_counter() - started
request_duration.labels(
venue=venue_name,
operation="fetch_ticker",
outcome=outcome,
).observe(elapsed_seconds)This is an illustrative pattern, not tested drop-in code. Adapt exception classes and metric APIs to the client and library you use. Set the outcome before returning or re-raising so the finally block records it. Keep cancellation behavior in mind: if your application treats task cancellation as a distinct outcome, handle and label it deliberately rather than allowing it to be silently categorized as success. Do not swallow exceptions merely to make metrics easier to record.
Decide whether retries are recorded as separate request attempts, as one logical operation, or both. Per-attempt request observations make timeouts and retry behavior visible; a separate higher-level measurement can capture the total time for a logical operation that includes retries. Name and describe each metric so its scope is clear.
Measure the larger cycle at business boundaries
Start a cycle timer at the event your team means by “cycle begins,” and stop it when the declared completion condition occurs. For example, a strategy-evaluation metric can cover input preparation, decision logic, and submission work if all of that is part of the defined cycle. An order-workflow metric can run from local submission through the chosen acknowledgment or fill event.
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Do not assume every trading API follows the same order lifecycle or that every client exposes the same events. Select boundaries from the bot’s actual workflow, document what they include, and keep separate histograms if you need to distinguish evaluation time from order completion time.
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Choose a histogram and useful buckets
Latency varies, so a histogram is more informative than an average alone. It retains a distribution that can reveal slow-tail behavior; dashboards can use it to examine quantiles such as p50, p95, and p99. These describe the observations collected for the selected population and time window. They do not guarantee what future requests will take.
Set bucket boundaries for the durations that matter to your bot, including its slow tail. The Prometheus Python client’s example shows configurable buckets and timed observations, but example defaults target ordinary web or RPC request durations and may not suit a trading workload. Choose boundaries based on your own expected request and cycle ranges rather than treating published example values as trading benchmarks.
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Use seconds consistently in Prometheus metric names, such as trading_api_request_duration_seconds and trading_cycle_duration_seconds. A duration metric should represent one quantity in one base unit. For Prometheus Python client usage, select a label set with .labels(...) and record seconds with .observe(elapsed_seconds); a timed context manager is another option where it fits the boundary.
Keep labels bounded and outcomes visible
A practical starting set of labels is venue, operation, and outcome. Use stable categories such as success, timeout, and error where they match your handling. Include retries as an outcome or operation dimension only if it helps answer a specific question and the possible values remain controlled.
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Do not attach unique order IDs, client order IDs, symbols drawn from an unbounded or rapidly changing set, or exception messages as metric labels. Each distinct label combination creates additional time series, which can grow storage and operational costs sharply. Put per-order diagnostic details in logs or traces instead, and use metrics for bounded aggregates.
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Choose the instrumentation and export path
| Approach | Fits when | Implementation considerations |
|---|---|---|
| Prometheus Python client histogram | Your application already exposes metrics for Prometheus to scrape. | Configure buckets and labels, create instruments once, then observe durations in the request and cycle paths. Avoid assuming the library’s example buckets match your workload. |
| OpenTelemetry histogram | Your deployment uses an OpenTelemetry collection pipeline or wants metrics alongside other telemetry. | Create a histogram with an appropriate unit and description, initialize the MeterProvider and reader/exporter for the deployment, and record durations in application code. |
Both approaches support histogram-style duration measurements; neither is a universal winner. Prefer the path that matches the bot’s existing metrics and export setup. If tracing context is important, consider how the metrics pipeline fits with the tracing instrumentation already used by the application.
In either case, initialize instruments at startup rather than creating them for every request. Keep metric names, descriptions, units, labels, and cycle definitions consistent across deployments so dashboards compare like with like.
Use asyncio auto-instrumentation selectively
OpenTelemetry Python Contrib provides asyncio instrumentation that can wrap supported coroutine and future paths and record processing-time metrics, with optional span information. Confirm that the installed instrumentation version supports the methods used by the bot and that it covers the calls you care about.
Automatic instrumentation can help measure supported asynchronous operations, but it may not match a business-level cycle boundary such as “strategy evaluation began” through “order fill confirmed.” Add explicit application-level measurements for those boundaries. Check for overlap so the same request is not unintentionally recorded twice under metrics that appear to mean the same thing.
Check the measurements before relying on them
- Confirm a completed request produces one observation with the expected operation and outcome.
- Exercise success, timeout, and ordinary error paths; verify that failures still record elapsed time and are re-raised or handled according to the bot’s existing policy.
- Verify that the timer spans the awaited operation, not just coroutine creation or request setup.
- Check that a cycle observation starts and ends at the documented business events, including the intended treatment of retries and asynchronous completion.
- Inspect exported metric names, units, bucket boundaries, and label combinations; make sure no per-request identifiers become labels.
Python’s clock guidance referenced here is for Python 3.14.8 documentation. Prometheus Python client and OpenTelemetry documentation can change; check the API signatures and supported asyncio methods for the versions pinned in your bot before adopting code examples.
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