There is no universal millisecond threshold that makes sense for every trading bot. Set one from the delay your strategy can tolerate and the healthy latency distribution of your own deployment. First define exactly what you measure—such as time from dispatch of an order request to the exchange response—then alert on sustained tail-latency breaches and watch errors, timeouts, traffic, and rate limits alongside it.
Define which latency the alert measures
“Request latency” is only useful when its start and end points are explicit. For an exchange API request, a practical measurement is elapsed time from when the bot dispatches a request until it receives the exchange response. Keep that separate from time spent waiting in the bot’s internal queue and from the time until an order is filled or fill confirmation arrives. Those are different delays with different causes and objectives.
Do not combine order submissions, market-data polling, account reads, and fill confirmation into one series if they have different service objectives. Record useful bounded dimensions such as venue, operation, endpoint class, deployment or region, and result class. Avoid labels unique to each order; they can create unbounded time series.
For proxied traffic, distinguish proxy overhead from the upstream target’s latency. Google Cloud’s Apigee guidance treats target latency as excluding proxy overhead, and says the alert threshold depends on the installation’s SLO: Apigee cluster monitoring guidelines.
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Choose a target from the strategy and baseline
Start with the strategy’s maximum tolerable delay: how long can a request take before the intended execution is stale, missed, or otherwise materially affected? Then compare that objective with the healthy latency distribution measured in the actual deployment. A threshold should be useful against both the strategy’s deadline and normal operating conditions; a value copied from another service does not establish either.
Select a tail percentile that expresses how much of the traffic must meet the objective. A p95 threshold describes the 95th-percentile latency; p99 focuses further into the slow tail. For a critical execution deadline, it can also be useful to define a separate severe threshold for delays that threaten that deadline. The available sources do not establish a correct target for any particular trading strategy, venue, or deployment.
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Measure the distribution, not just the average
A mean can hide the slow requests most likely to matter to execution. Instrument a histogram or equivalent distribution metric so you can examine percentiles and the proportion of requests that meet a defined latency objective. Prometheus client documentation describes histograms with bucket counts, sum, and total count, and shows configurable buckets for request latency: Prometheus Python client: Histograms.
Set bucket boundaries with the alert objective in mind. If the relevant threshold is 200 ms but the histogram has no useful resolution around 200 ms, it cannot answer the question precisely. If the SLO is framed as a fraction of requests completing within an objective, calculate that good-request fraction from the bucket at the objective boundary rather than treating a percentile estimate as the entire SLO. Check your monitoring backend’s histogram and query semantics before relying on that calculation.
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Set breach duration, severity, and recovery
Specify how often the metric is evaluated, how long a breach must persist before notification, and what condition clears the alert. Persistence helps distinguish a sustained problem from an isolated sample or scrape fluctuation; it should not delay action longer than the bot can tolerate degraded execution.
Published cloud alarms illustrate mechanics, not recommended trading-bot settings:
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- Amazon CloudWatch’s API Gateway example uses p90 latency above 2500 ms, a 60-second evaluation interval, a 300-second pending period, and a 300-second recovery period. These are AWS values for API Gateway stages, not a bot latency benchmark: AWS recommended PromQL alarms.
- Google Cloud’s Apigee hybrid v1.16 production example alerts on p99 proxy latency at five seconds for five minutes. The same guide says the threshold depends on the installation SLO: Apigee cluster monitoring guidelines.
A two-level design can separate a persistent breach of the ordinary tail objective from severe latency that threatens the execution deadline, especially when errors or timeouts are also rising. Decide whether each alert should open a dashboard, create a ticket, page someone, or trigger a safety action. Any automated action that pauses or changes trading should be tested as a strategy and operational policy; latency alone does not establish that a specific response is safe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Pair latency with traffic, errors, and rate limits
Latency by itself rarely identifies the cause. Keep request volume, timeouts, error ratio, connection and retry behavior, and venue rate-limit responses visible alongside it. If traffic is absent or too low, a percentile may be unavailable or misleading; handle that case explicitly and use a separate liveness or expected-traffic signal where appropriate.
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For Binance Spot, endpoint weights vary, and the /api/v3/exchangeInfo response exposes RAW_REQUESTS, REQUEST_WEIGHT, and ORDERS limits. A 429 response means a rate limit was exceeded; clients should back off, and the Retry-After header gives a wait duration. Repeated violations may lead to an IP ban. Binance.US also documents IP-based limits and automated HTTP 418 bans for repeated violations or failure to back off. These rules are venue-specific and can change, so verify the current documentation for the exchange you use: Binance Spot REST API information and Binance.US API documentation.
Do not treat published examples as a universal target
A third-party Solana bot article reports a p95 fill-latency alert at 1.5 seconds sustained for two minutes: TierZero.dev’s monitoring example. That is an example concerning fill latency, not an independently validated target for exchange request latency. The cloud figures above measure service-specific API or proxy latency. They cannot be fairly compared with one another—or transferred to a bot—without matching the measured span, statistic, threshold basis, traffic context, persistence, and alert action.
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