Log the events that explain what the bot did and why: order submissions and outcomes, fills, risk-control decisions, exchange errors, latency, reconnects, and rate-limit signals. Keep consequential events intact; sample or aggregate repetitive health data only under a documented policy. Keep credentials and unnecessary personal or account data out of event payloads before they leave the application.
What should my trading bot log?
Build telemetry around operational questions: Did the bot submit the intended order? Did the venue accept, reject, or fill it? Did a safety rule intervene? Was the bot delayed, disconnected, or asked to slow down? Structured events tied to those questions are generally more useful than indiscriminate high-volume traces.
Keep consequential events identifiable
- Order lifecycle: submission attempt, acknowledgement, rejection, cancellation, partial or complete fill, and relevant timing. Use an internal order or correlation ID rather than credentials or signed request content.
- Risk controls: trigger, action taken, and outcome—for example, that a configured limit prevented an order. Record the decision and relevant non-sensitive values needed to investigate it.
- Exchange and connectivity: response status, timeouts, reconnects, retry and backoff events, and rate-limit indicators.
- Performance and health: request or processing latency, queue delay, and service health signals that help distinguish a strategy decision from an operational fault.
Use consistent timestamps and event names, and include enough non-sensitive context to correlate related events. Do not put API keys, secrets, authorization headers, signed request material, or unnecessary personal or account identifiers into those records.
How often should I sample bot telemetry?
These sources establish no universal sampling percentage or trading-bot sampling benchmark. Choose sampling per stream and per diagnostic need rather than applying one percentage to everything.
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Retain complete records for order outcomes, safety actions, and errors that affect bot behavior. Sampling away a rare rejection or risk intervention can make an incident impossible to reconstruct.
Reduce repetitive health data deliberately
For high-frequency, repetitive signals, consider aggregation or sampling if the remaining resolution still lets you detect and diagnose the failures you care about. Document which streams are sampled or aggregated, the policy version, and the relevant time window. Dashboards and incident reviews should make that reduction visible; sampled counts are not necessarily complete totals.
This is an operational design approach, not a measured claim that a particular rate performs best. Revisit it when event volume, diagnostic needs, or costs change.
Why are my logs so expensive?
For Amazon CloudWatch Logs, AWS treats collection and ingestion, storage, and analysis as distinct cost dimensions. High-volume verbose events can raise ingestion, while long retention increases stored data; query activity can add analysis charges. AWS recommends logging events that provide value, using efficient event syntax, setting retention, and considering the Infrequent Access log class when its feature set fits the workload. See AWS’s CloudWatch cost guidance.
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CloudWatch Standard and Infrequent Access differ in ingestion costs; AWS says storage and Logs Insights charges are the same, while some features are unavailable in Infrequent Access. Check the current regional pricing and feature list before choosing a class: no current price comparison or market-wide cheapest option is established here. AWS also describes Intelligent Tiering as moving data based on access patterns: data not accessed for 30 days moves to Infrequent Access, and data not accessed for 90 days moves to Archive Instant Access. These are AWS product behaviors, not general retention recommendations; AWS reports StoredBytes once daily, so it may not reflect transitions in real time. See the AWS log classes documentation.
Review cost drivers before reducing diagnostic value
- Identify chatty events that answer no operational question; reduce their frequency, fields, or verbosity.
- Set retention to match the investigation window and your own operational or policy requirements. These sources do not establish a legally required retention period.
- Account for query and analysis activity as well as ingestion and storage when reviewing a bill.
- Compare lower-cost storage options against the investigative features you would lose, and verify current regional prices and service terms.
Should API keys ever appear in logs?
No. Keep API keys, secrets, authorization headers, signed request material, and unnecessary personal or account identifiers out of telemetry. The safest first control is to avoid capturing them in application events; use collector-side filtering as an additional safeguard, not as the only one.
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Redact before export when the raw value must not leave the process
Filtering after data has been exported cannot undo its first transmission. AWS’s CloudWatch Omni documentation distinguishes capture-time filtering, collector processors, ingestion-time masking, read-time access controls, and encryption. Capture-time removal means the raw value is unavailable for debugging, so choose controls to fit the threat model and data policy. AWS says CloudWatch Omni does not automatically detect or redact personally identifiable information; filtering must be configured. See AWS guidance on protecting sensitive data.
Also check free-form names and tags. AWS warns against putting confidential or sensitive information in tags or fields such as a Name field because these may appear in billing or diagnostic logs. CloudWatch Logs encrypts log data at rest by default, but encryption does not make unnecessary collection appropriate. See AWS’s CloudWatch Logs data-protection documentation.
Does a self-hosted bot send telemetry?
It can. Self-hosting does not itself prove that no data leaves the machine; behavior depends on the project and its settings. Hummingbot Condor’s privacy documentation describes anonymous usage reporting, install counts, notification and opt-out controls, and content telemetry handled through a distinct endpoint. That is a project-specific example, not a claim about every bot. Review the actual software’s privacy documentation, configuration, and outbound connections if you need to establish what it sends. See Hummingbot Condor’s privacy documentation.
Which exchange signals should I monitor?
Monitor venue responses and the signals that determine whether a client should continue, retry, or back off. Rate-limit rules differ by exchange, so use the venue’s own API documentation rather than assuming one platform’s behavior applies everywhere.
Binance Spot REST example
Binance Spot REST documentation describes IP-based limits rather than API-key-based limits, request-weight headers, and HTTP 429 responses when limits are exceeded. It says repeated violations or failure to back off can lead to HTTP 418 IP bans whose durations scale for repeat offenders; a Retry-After header indicates the required wait. Binance states: “When a 429 is received, it’s your obligation as an API to back off and not spam the API.” For a Binance Spot integration, useful telemetry includes response status, used-weight headers, endpoint weight, order-count headers, Retry-After values, and retry/backoff events. These are Binance-specific details; consult the relevant documentation for other venues. See Binance General REST API Information.
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