The Continuous Trading Loop

A trading workflow should continue beyond the first useful answer. A thesis becomes valuable when it can move through research, testing, monitoring, approval, execution, and review without losing the context that explains each decision.

Define the goal

The loop begins with a specific objective. The user may want to investigate a market condition, test a rule, monitor an existing position, or prepare an action for approval. The objective should state what success means and what the workflow must not do. A clear goal narrows the work before tools or models are involved.

Constraints belong at this first step as well. Instruments, time horizon, data sources, risk limits, infrastructure, and required approvals shape every stage that follows. When those constraints remain attached to the strategy, later steps do not need to guess the operating boundary.

Research and test

Research gathers the evidence needed to form or challenge a hypothesis. The agent can organize observations, compare sources, and identify assumptions that require testing. The output is not an instruction to trade. It is a structured thesis with questions that a test can examine.

Testing then translates the thesis into explicit rules. Entries, exits, sizing, timing, and failure conditions should be visible rather than implied. Backtesting and paper trading serve different purposes, but both produce evidence that should be written into the same strategy context: configuration, result, limitation, and the reason for the next change.

The workflow should not end when the first AI response is complete.

Monitor without starting over

Monitoring connects a tested idea to changing market conditions. Instead of rerunning the entire research process whenever new data arrives, the workflow can evaluate the current condition against the existing rules and assumptions. The agent knows what it is watching for because the strategy state is already present.

A monitoring event may confirm a condition, invalidate an assumption, or reveal that no action is needed. Each outcome belongs in the record. Silence can be meaningful when a strategy’s rules have not been met, and an alert should explain which condition changed rather than merely announce that something moved.

Approval before execution

An agent can prepare an action without having permission to complete it. The approval step makes that boundary explicit. The proposed order, current context, relevant limits, and reason for acting can be presented together so the user evaluates a complete decision rather than a detached instruction.

Approval should also be specific. Permission for one action does not imply ongoing permission for different instruments, sizes, or conditions. When protected actions require review, the loop pauses at the boundary and resumes only with the authority the user has granted.

Write the result back

Execution is not the end of the loop. Fills, partial fills, rejections, cancellations, and other outcomes must return to the strategy record. The agent should be able to distinguish what was intended from what occurred and carry that difference into monitoring and evaluation.

The same applies when no live action occurs. A paper result, expired condition, or rejected approval still changes the strategy’s history. Recording those outcomes prevents the next iteration from repeating work or assuming that an unexecuted plan became a position.

Begin the next iteration

The next run begins with the current state, not a blank conversation. The strategy can be revised, a test can be narrowed, monitoring can continue, or a position can be reviewed using the evidence already attached. History remains open to challenge, but it does not disappear.

Continuity also makes comparisons more disciplined. A revised rule can be measured against the configuration it replaced, and a monitoring change can be traced to the event that justified it. The user does not have to accept the agent’s summary on trust; the underlying steps remain available for inspection. That makes iteration faster without turning it into an unreviewed chain of automatic decisions.

A continuous loop therefore has checkpoints as well as momentum. Each stage can advance the strategy, pause it for review, or send it back to testing when the available evidence is incomplete.

Mattheus frames agentic trading as this continuous loop. Goals define the work, research and tests build evidence, monitoring checks conditions, approvals protect authority, execution returns outcomes, and the next iteration starts with the accumulated context available.