The engine decides the intended side, size posture, and whether the action is an open, close, or flip.
Technical Docs
Execution and Reconciliation
AetherPro treats execution as a lifecycle, not a single API call. A chosen action has to move through order routing, fill reality, state refresh, and reconciliation before the platform can claim an accurate outcome.
Lifecycle overview
The important distinction is between a decision and a confirmed platform state. Execution bridges that gap, and reconciliation is what prevents the platform record from drifting away from live exchange reality.
What execution has to account for
Liquidity, slippage, and exchange response shape what actually gets filled and at what price.
The live position after execution is the truth source for exposure, TP/SL posture, and follow-on actions.
Why reconciliation matters
Reconciliation is how the platform checks that internal state still matches the exchange. It is essential when fills are partial, positions are reduced externally, or native TP/SL orders close exposure outside the exact path the bot originally requested.
- Detect whether open positions still exist on the exchange.
- Refresh entry, size, and lifecycle data from live state.
- Mark positions closed when live exposure has already ended.
- Prevent the bot from acting on stale assumptions during the next cycle.
Operational edge cases
- A signal chooses an action, but the exchange rejects or only partially fills the order.
- A native TP or SL closes the position before the next engine cycle runs.
- Live margin availability changes between the bot decision and order submission.
- An operator or external system changes the position state directly on the exchange.
Execution quality is part of trust
A transparent automation platform has to explain more than the decision model. It also has to explain how live outcomes are confirmed, refreshed, and corrected when real execution differs from requested intent.
Related Pages
Execution reality
Validate logic safely before adding exchange complexity
Test mode helps isolate decision quality first. Live execution adds a second layer of complexity that should be understood deliberately.