How to Show an AI Trading Agent’s Activity Timeline in a Trading App
Learn how to design a clear activity timeline for AI trading agents, showing intent, orders, execution, limits and error handling for better auditability.
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- 01The timeline separates intent, order submission and execution results for each action.
- 02It records timestamps, data source IDs and freshness warnings for every market datum.
- 03It displays owner‑authorized limits and scope restrictions that applied to each trade.
- 04It shows explicit error states and reconciliation notes when an order times out or fails.
- 05It highlights emergency‑stop events and the need for owner review of any open positions.
A trading app should present an AI agent’s activity timeline as a chronological list that clearly distinguishes the agent’s intent, the order it generated, and the final execution result. Each entry must include a reliable timestamp, the originating market data source, and any limits that governed the action. This structure lets users quickly audit decisions and understand where uncertainty or failure occurred. For related context, see How a Trading Agent Should Respond When Execution Is Unavailable.
How does core Elements of a Timeline Entry work?
- Timestamp and source of the market data that triggered the decision.
- Agent‑scoped key identifier and the specific limits (size, notional, loss) that applied.
- Intent description, such as “place limit buy 10 BTC at $30,000”.
- Order submission status (accepted, rejected, pending).
- Execution outcome (filled, partially filled, cancelled) with price and quantity.
- Error codes or reconciliation notes if the order did not complete.
How does communicating Data Quality and Freshness work?
Market data entries must expose their source, timestamp and any freshness warnings. If a quote is older than the configured freshness window, the timeline should flag it and optionally block order generation. This avoids silent treatment of missing or stale money and helps users see why an agent may have acted conservatively.
What happens when a quote is stale?
The entry is highlighted with a warning icon and a note such as “stale quote - 12 seconds old”. The UI can suggest adjusting the freshness parameter or pausing the agent until fresh data arrives.
Showing Limits and Scopes
Owner‑signed limits are the primary safety net for an autonomous agent. Displaying the exact limit that applied to each action lets users verify that the agent stayed within policy and understand when a limit caused a trade to be rejected. It also highlights the uncertainty that arises when limits are close to being breached.
Why are scope identifiers important?
The scope identifier tells the user which key performed the action and what permissions it had, making it easy to trace any unexpected behavior back to a specific authorization.
Error Handling and Timeouts
A timeout does not prove an order failed; the timeline must record the timeout event, the last known order state, and a prompt for manual reconciliation. Explicit error states such as “insufficient liquidity” or “venue unavailable” should be shown with guidance on next steps, rather than disappearing silently.
Emergency Stop Visibility
When an emergency stop is triggered, the timeline should mark the stop event, list the keys that were revoked, and note any positions that remain open. Closing positions or revoking token allowances requires separate owner review, so the timeline must make that distinction clear.
Auditing and Exporting the Timeline
Providing filters for date range, event type and data source helps users focus on relevant sections. Exporting the timeline in a structured format supports deeper analysis and compliance reviews. For more guidance on auditing agent decisions, see How to Audit an AI Trading Agent’s Decisions. For related context, see Why an AI Trading Agent Should Never Hold the Owner’s Withdrawal Key.
Visual Cues for Readability
- Color‑code rows by event category (intent, order, execution, error).
- Use icons to indicate data freshness warnings or limit breaches.
- Show collapsible details for each entry so the list stays concise.
A clear timeline turns raw event logs into an actionable audit trail, reducing the cognitive load on traders and compliance teams.
Frequently asked questions
Refresh at a rate that balances real‑time visibility with performance, typically every few seconds, while ensuring each new entry includes a verified timestamp.
Backtests are read‑only and do not generate live orders, so they should be displayed in a separate view that does not mix with live activity.
Investigate the data source, confirm the latest market price, and consider adjusting the agent’s freshness parameters before allowing further trades.
The timeline will list each revoked key and its scope; any remaining keys require manual owner review to confirm they are no longer active.
Sources and verification
Product claims in this article were checked against these first-party references. Runtime status remains authoritative for current availability.
- Felix documentationfirst party
- Felix machine referencefirst party
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