How to Design a Supervised Trading Agent With Hard Risk RailsApplied Digital CorporationBATS:APLDquantsignals_alphaMost automated trading systems begin with an entry rule. That is the wrong place to start. The difficult part of a trading agent is not producing a BUY or SELL opinion. It is controlling what happens before the order, after the order, and when the environment stops matching the assumptions. This tutorial presents a seven-layer architecture for a supervised trading agent. It is intentionally platform-neutral and educational. It does not assume that any model can guarantee a return. ## Layer 1 — Market state The agent needs a time-stamped view of: - price and volume; - spread and liquidity; - market session; - volatility regime; - scheduled events; - existing positions and orders; - and data freshness. A useful rule is: **no state, no action**. If the required state is missing or stale, the agent should refuse to enter rather than fill gaps with narrative confidence. ## Layer 2 — Opportunity filter Screening should reduce the universe before deeper analysis begins. Define: - eligible markets and symbols; - minimum liquidity; - allowed session; - maximum spread; - event exclusions; - and setup-specific conditions. The filter should answer “is this candidate worth researching?” It should not silently become an order generator. ## Layer 3 — Evidence and counter-evidence Every trade plan should contain: 1. the primary thesis; 2. the strongest opposing thesis; 3. the evidence supporting each side; 4. the data timestamp; 5. the condition that would invalidate the conclusion. Requiring the counter-thesis reduces one of the most common agent failures: turning a plausible story into false certainty. ## Layer 4 — Risk policy Risk belongs in deterministic policy, not persuasive prose. At minimum, define: - maximum capital deployed; - maximum position size; - maximum loss per trade; - daily loss limit; - maximum number of correlated positions; - allowed order types; - and whether overnight exposure is permitted. A goal must never be allowed to rewrite the risk policy. If a target is behind schedule, the agent should not increase size or lower the evidence threshold. ## Layer 5 — Execution permission Separate research authority from trading authority. A practical permission ladder is: 1. research only; 2. produce a trade preview; 3. submit after explicit approval; 4. manage an approved position; 5. operate automatically inside fixed limits. This lets the operator expand authority gradually instead of choosing between “manual” and “fully autonomous.” ## Layer 6 — Monitoring and failure behavior Before entry, define what the agent will monitor: - thesis validity; - stop or invalidation condition; - spread and liquidity; - time-based exit; - scheduled news; - account-level guardrails. Also define failure behavior: - stale data → pause; - broker unavailable → no new order; - excessive spread → refuse; - risk limit reached → stop adding exposure; - unknown state → return control to the operator. A robust agent is predictable when conditions are bad. ## Layer 7 — Audit and learning Every consequential step should leave a record: - state observed; - evidence used; - plan proposed; - rule that permitted the action; - order preview; - execution result; - monitoring events; - exit reason; - final outcome. Do not evaluate the system only from winning screenshots. Review rejected trades, stopped sessions, slippage, losses, and policy violations. ## A compact pre-trade schema Before an agent can act, require these fields: ```text symbol: market_state_timestamp: thesis: counter_thesis: entry: invalidation: size: maximum_loss: take_profit_or_exit_logic: time_exit: required_approval: failure_action: ``` If one of the required fields is missing, the plan is incomplete. ## The operator model The goal is not to remove the human from responsibility. The goal is to move the human to the correct level. The operator defines: - the mission; - the capital; - the permissions; - the guardrails; - and the kill switch. The agent owns the bounded workflow. That is what “supervised” should mean in trading automation. *Educational material only, not investment advice. Automated trading and human trading both involve substantial risk.*