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Building a time-aware AI trading workflow with n8n and MT5.

A clearer, engineering-first revision of the original AI trading-agent article.

The system problem

Market context arrives from different sources, at different times, and in different shapes. A useful workflow must normalize that context before asking a model to reason about it.

The hourly pipeline

The workflow gathers financial headlines, macro sentiment, economic-calendar events, and open MT5 positions. Calendar events receive time-aware labels such as past, imminent, or just_released.

Structured output before execution

The model is asked for a constrained response, not free-form prose. A deterministic layer then validates allowed symbols, actions, stale timestamps, required risk fields, and position conflicts before anything reaches MQL5.

The model can propose. The system must validate.

What I would add next

  • Human approval for sensitive actions.
  • Risk caps outside the model prompt.
  • Replayable input snapshots.
  • Full decision and execution logs.
  • A kill switch and stale-data protection.