A trading bot can evaluate a setup without emitting a signal, emit a signal without submitting an order, or submit an order that never fills. The way to find where a trade went missing is to log each decision and order transition as a separate, timestamped event. The title’s “41 days” and “saved me” describe an author’s personal account; they are not independently verified here or evidence of a typical failure rate.
Why a signal is not the same as a trade
“My bot ignored a signal” can describe several different failures. The market event may not have reached the system; the strategy may not have evaluated it; a candidate may have been filtered out; an order may not have been submitted; or a submitted order may have been rejected, canceled, expired, or left unfilled. A signal is not an order, and an order request is not proof of execution.
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Framework documentation such as the AKQuant Strategy Guide separates strategy callbacks from order, trade, and rejection callbacks. That separation is useful operationally: it lets you identify the last confirmed stage instead of treating “no trade” as one undifferentiated problem.
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Start with a specific expected opportunity: its symbol, approximate event time, and expected direction or rule. Search the records in chronological order, following a correlation identifier if one exists. Find the last confirmed event, then identify the first missing or failed transition.
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- Market event: Was the relevant data received, and was it fresh enough for the strategy to use?
- Strategy evaluation: Did the strategy evaluate the relevant condition? Record the strategy identifier, symbol, event time, and rule or version where useful.
- Candidate signal: Was a candidate emitted? Log its direction and the inputs needed to explain the decision.
- Filter or risk decision: Did a position, session, cooldown, duplicate, quality, or risk rule allow it? If not, record the gate and a specific reason. These are examples, not assumptions that every bot has each gate.
- Order intent and submission: Was an order created and sent? Preserve the client-order identifier and broker order identifier as they become available.
- Broker outcome: Was the order acknowledged, filled, rejected, canceled, or expired? Keep broker status and error details; submission alone does not mean execution.
- Post-broker handling: Did a callback, queue consumer, or reporting stage fail after the broker event?
AKQuant documents lifecycle callbacks including on_order, on_trade, and on_reject, along with context such as phase, strategy, symbol, event time, and order identifiers. Use the equivalent boundaries available in your own framework rather than assuming its callback names or lifecycle match another system.
Log suppressed candidates as decisions
A missing order record cannot distinguish an intentional rejection from a silent failure that occurred before submission. Whenever the system discards a candidate, log the decision at that boundary: which gate acted, whether it allowed or denied the candidate, and why. For example, record “denied” with a reason such as “position limit reached,” if that is the actual rule and reason in your implementation. Do not infer a reason later from the absence of an order.
Keep condition evaluation, candidate generation, filtering, order submission, and broker lifecycle events distinct. This gives operators a way to tell “the setup was never evaluated” from “the setup was evaluated and deliberately blocked.”
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Use structured, correlated event records
Machine-readable logs are easier to search and compare across asynchronous components than free-form messages. A practical starting schema—not a universal standard—could include:
event_time_utcand, separately, receipt or processing time when diagnosing delaysevent_type,phase, anddecisionstrategy_id,symbol, andsignal_idreasonfor a filter, rejection, or failureclient_order_id,order_id, and broker status or error when applicable
Use consistent identifiers across records where possible. Timestamps should retain their timezone or use UTC consistently; keep event time distinct from the time a downstream service received or processed the event. This helps expose delays without confusing them with the time the underlying market event occurred.
Persist the order trail and configure live logging
For a post-mortem, logs must survive the process that produced them. AKQuant documents a JSON order-audit stream that can preserve submit, update, fill, cancel, and reject events. Its live operations guide describes configuring logging and combining strategy-side order and trade records with gateway and execution warnings. It recommends a separately persisted INFO-level order-audit stream while keeping console output less noisy.
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Check the logging configuration and access controls in your actual deployment. AKQuant documents sensitive-field masking as enabled by default in that framework, but that setting is not a guarantee about other systems or every deployment configuration.
Check upstream delivery when signals arrive by webhook
If an external alert or webhook feeds the bot, begin at the receiver rather than at order placement. A missing receipt record points toward the source alert, endpoint, or transport path; a received event with a processing failure points farther downstream.
Amabit’s Monitoring & Logs documentation describes statuses such as Received, Processing, Executed, Failed, Deduplicated, and Rate Limited. It lists a wrong webhook URL, disabled webhook, expired TradingView alert, and payload larger than 10 KB as possible reasons for no receipt entry. Those limits, labels, and causes are specific to Amabit’s documented service, not universal webhook behavior.
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The same guide lists provider-specific processing examples including insufficient balance, invalid symbol, position-mode mismatch, minimum order size, and reduce-only rejection. If a webhook was received, use the provider’s own event history and error details to determine whether processing stopped before order submission or later in the lifecycle.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make data-pipeline health visible
A strategy cannot act on market data it never receives or accepts. Track feed health at the upstream boundary as well as strategy and order events. OpenFNO’s data-module documentation, updated 2026-10-01, lists metrics for queue depth and ticks dropped, not stored, or rejected. These are examples of useful observability; they do not establish that any particular bot is losing data.
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A process heartbeat can show that a service is alive, but it cannot by itself show that market data reached the strategy, an opportunity was evaluated, or a submitted order reached a terminal broker state. Monitor those transitions independently and alert on the specific missing or failed transition that matters to your system.
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Turn the first missing transition into a focused alert
Once the event trail shows where a particular opportunity stopped, add an alert for that failure mode rather than relying only on a generic “bot is running” check. Depending on what your implementation actually exposes, that might mean alerting on rejected feed events, repeated strategy errors, denied candidates at an unexpected rate, broker rejections, or orders that remain unresolved beyond an operational threshold you define. The threshold should reflect your market, venue, and execution design; there is no universal timeout established by the cited documentation.
Keep the alert tied to records that an operator can inspect: the strategy, symbol, event time, decision reason, and order identifiers when available. That turns an alert into a starting point for reconstructing the event chain, rather than another symptom without context.
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