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What OpenTelemetry does—and what remains custom
OpenTelemetry separates instrumentation APIs from SDK implementations: application code can use the APIs, while the application owner configures an SDK to collect and process telemetry. Semantic conventions provide shared names and values for common concepts across codebases and platforms. The project describes conventions across traces, metrics, logs, profiles, and resources; its page was last modified September 28, 2026 (OpenTelemetry Semantic Conventions).
Those conventions can make common infrastructure signals easier to interpret consistently, but they do not supply the trading vocabulary your bot needs. Define the meaning of signals such as a risk rejection, partial fill, or strategy decision yourself, then emit them through OpenTelemetry APIs where that fits. This gives bot-specific telemetry a shared collection and export path without implying that the framework standardizes those trading concepts. For implementation, instrumentation authors are directed to depend on the API rather than an SDK package (OpenTelemetry overview).
How the approaches compare
| Decision | OpenTelemetry-led | Custom-only | Practical choice |
|---|---|---|---|
| Consistency across services and libraries | Shared APIs, conventions, and context can align telemetry. | Your team defines and maintains names and representations. | Use existing conventions when they fit; document custom signal names and meanings. |
| Trading-domain meaning | Does not prescribe trading-specific signals in the reviewed documentation. | Can represent domain states and business events directly. | Design explicit bot signals and emit them through OpenTelemetry APIs where useful. |
| Collection and export | SDKs, Collector, and integrations provide configurable collection and export options. | Your team chooses and maintains those paths. | Use shared plumbing if it meets deployment needs; check support for your language and components. |
| Data control | SDK and Collector configuration provide processing points for filtering, sampling, and transformation. | Your team owns all collection behavior. | Set deliberate rules for sensitive data, sampling, and retention. |
| Performance evidence | No trading-bot-specific benchmark is established by the reviewed official sources. | No head-to-head benchmark is established either. | Measure your bot and deployment rather than relying on a generalized overhead figure. |
| Long-term maintenance | Shared conventions and APIs may reduce bespoke exporters and schema drift. | Internal schemas and tools can match local needs, but remain your team’s responsibility. | Account for schema ownership, upgrades, exporter upkeep, and operational burden. |
These are architectural trade-offs, not results of an empirical comparison between trading bots. OpenTelemetry describes its Collector as “a vendor-agnostic implementation on how to receive, process, and export telemetry data” (OpenTelemetry glossary).
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Which bot signals belong in metrics, traces, or logs?
Metrics: bounded measurements over time
Use metrics to answer recurring operational questions and observe changes over time. Suitable candidates include market-data message rate, processing-queue depth, risk-check counts, order submission or acknowledgement latency distributions, rejection counts, and connection health. The metrics API supports instruments such as counters, gauges, and histograms; views can configure aggregation, transformation, and filtering (OpenTelemetry overview).
Keep metric attributes bounded: environment, venue class, strategy family, or outcome category can be useful when each has a controlled set of values. Avoid unique order IDs and other unbounded values as metric dimensions. OpenTelemetry defines high cardinality as many unique attribute values or combinations and notes that it can increase backend storage and performance demands as well as metrics-SDK memory use (OpenTelemetry glossary).
Traces: follow a representative workflow
A trace can connect work across application components and process or network boundaries. For a bot, a useful trace might follow a representative order workflow from market input through strategy evaluation, risk checks, order submission, and exchange acknowledgement. Context propagation helps related telemetry share context across that distributed operation (OpenTelemetry overview).
Do not assume every high-frequency event should become a full trace. Choose sampling based on the diagnostic question and workload; the Collector can sample telemetry, but the policy must fit the deployment. Keep high-detail per-order context in selected traces when it is useful, and govern its access and retention.
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Logs and events: preserve selected detail
Use structured logs or events for detailed exceptional transitions and diagnostic context that would be too granular for bounded metrics. Correlate them with traces where useful. Do not place credentials, secrets, account identifiers, or sensitive strategy details in telemetry without a reviewed data policy. Collector processing can scrub personal information, but that capability does not replace the system owner’s security review (OpenTelemetry overview).
How to design a hybrid instrumentation plan
- Start with operational questions. Decide what the team needs to detect or diagnose: stalled market-data processing, rising risk rejections, slow acknowledgements, or unexpected execution outcomes.
- Adopt common conventions where they fit. Use OpenTelemetry APIs and applicable semantic conventions for shared runtime and dependency telemetry; write down custom trading signal names and definitions.
- Choose a signal type for each question. Use metrics for bounded counts, levels, and distributions; traces for selected end-to-end workflows; logs or events for detailed transitions and context.
- Control dimensions and sensitive data. Keep metric attributes low-cardinality, and define what detailed data may be captured, who can access it, and how long it is retained.
- Configure collection and export deliberately. The Collector can receive, aggregate or sample, enrich or transform, scrub personal information, and export telemetry to one or more destinations. The Collector may be deployed as an agent or gateway (OpenTelemetry glossary).
- Benchmark the production-shaped workload. Measure representative load, tail latency, CPU and memory use, dropped telemetry, and behavior when an exporter or backend is unavailable. Keep telemetry from adding an unmeasured synchronous network dependency to the order path; this is an engineering safeguard, not a performance guarantee from OpenTelemetry documentation.
Choose a backend separately
OpenTelemetry is not itself the system that stores and queries telemetry. The project glossary defines a backend as the component responsible for receiving, processing, storing, and querying telemetry. Decide separately whether a compatible service or self-managed system meets your requirements, and validate the full collection and export path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What performance claims can you rely on?
The reviewed official OpenTelemetry material describes general telemetry capabilities, not trading-bot performance. It does not establish a universal instrumentation-latency figure or a trading-specific comparison of OpenTelemetry with custom-only instrumentation. That absence does not mean instrumentation has zero overhead. Test the actual language SDK, Collector configuration, backend path, and bot workload before relying on a latency or resource-use claim. The specification page identifies version 1.61.0 in the reviewed material; verify the specific language support and versions selected for deployment because APIs and conventions can change (OpenTelemetry Specification).
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