Algorithmic Strategy Lab
Research into automated decisions and execution checks
Market snapshots, a decision module and execution checks. A study in accountable automation, not promised returns.
Work verified against its source.Data can age, positions can change and API responses can disappear while a decision is being prepared. Automation needs a clear boundary between a signal and an allowed action.
A Python/API system ties decisions to a snapshot and expiry, rechecks input conditions and stores intent before calling an external API.
- Market snapshots
- Decision schema and expiry checks
- Pre-action conditions
- Intent log and reconciliation
Input and expiry checks
The system validates the response schema, snapshot identity and validity period. Stale decisions are rejected, and research evaluation cannot create an executable order.
Pre-action checks
Before acting, the system checks data freshness, position, spread, price movement and instrument constraints. Mode, pause state and unresolved operations are checked at the execution boundary.
API-outcome tracking
Intent is saved in advance and reconciled with the response. Uncertain results have their own state. Acceptance is never presented as execution, and financial outcomes are not used in this case.
Context and limits +
No profitability, returns or complete backtesting engine is claimed. Visual data is synthetic. This is research, not trading advice.