Give the model a bounded role
Begin with tasks such as summarizing a verified dataset or proposing a candidate for review. Require structured outputs that resolve to approved contract identities and documented quantities. A fluent explanation should never compensate for missing metadata or give the model authority to widen its own permissions.
Put validation outside the model
Instrument allowlists, data freshness, quantity rules and stop procedures should be enforced independently of model output. Treat retrieved documents and commentary as data, not instructions to bypass policy. Retain rejected proposals and their reasons so evaluation measures more than the proposals that happened to pass.
Evaluate in paper before discussing execution
Separate research observations from assumed fills, include costs and no-fill behavior, and prevent later information from entering an earlier decision. Model version, policy version and simulation settings should travel with every report. No simulation guarantees future returns, and this site does not offer an autonomous trading bot or connect to a real account.
Build for failures and review
FINRA’s algorithmic-control guidance discusses development, validation, monitoring and disabling algorithms. Use it as a control reference, not a claim that a checklist establishes compliance. Our article develops a practical evaluation architecture with explicit ownership, observable states and a tested recovery path.
