Market data and derived signals define whether momentum or weakness is building.
Decision Logic
How Bot Decisions Work
AetherPro uses policy-driven decision flow with defined inputs, risk checks, and action states. The goal is visibility and control, not black-box output.
Decision flow overview
Each cycle follows a consistent pattern: inputs are collected, strategy logic evaluates signal quality, risk rules check whether action is allowed, and the system chooses the next state for each asset.
InputsSignal evaluationRisk checksAction selectionExecution or hold state
Inputs the bot uses
Existing open positions, ladder state, and lifecycle context affect whether the system holds or exits.
Allocations, leverage constraints, and exit behavior shape the decision envelope.
Actions the bot can take
Start a new position when signal and risk criteria align.
Maintain the existing state because no exit or new-entry condition overrides it.
Exit because strategy or risk logic indicates the position should no longer remain open.
Take no position because data, signal quality, or safety rules do not support action.
Close an existing side and immediately move to the opposite direction when configured to do so.
Why a bot holds instead of closing
Hold is a valid outcome. If exit conditions have not triggered and no stronger state change is required, the system should continue holding. This is why understanding the configured exit model matters before interpreting behavior.
Why a bot skips instead of opening
Skip can result from weak signal quality, missing data, volatility conditions, or safety rules that block new entries even when directional bias exists.
Policy-driven, not opaque
The system should be described as deterministic automation with configurable rules and inspectable outcomes. Users should be able to see why it acted and why it did not act.
Related Pages
Next step
Validate decisions in test mode
The best way to build trust is to inspect the same decision path in dry run before enabling live execution.