Use this workflow to move from an AI agent idea to a reviewable paper-trading system: write the rules, turn them into instructions, collect simulated decisions, check risk, review the sample, and version only one change at a time.
Trading Boy does not execute live trades, hold funds, or provide financial advice. This AI paper trading agent workflow is a way to organize simulated decisions and human review. It should not be described as live bot deployment, copy trading, signal delivery, or a promise of future performance.
A paper agent becomes easier to evaluate when every stage has one owner artifact. If a stage is missing, pause before collecting another sample because the next review will be incomplete.
| Stage | Owner artifact | Review question | Next link |
|---|---|---|---|
| 1. Define the agent | Persona, watchlist, setup, invalidation, and skip rules. | Can a reviewer tell what the agent is allowed to do? | AI trading agent rules |
| 2. Write the prompt | Paper-first prompt with risk limits and output requirements. | Does the prompt block live-execution language and require evidence? | Prompt template |
| 3. Standardize output | Structured fields for entry, skip, exit, risk, and next action. | Can entries and skips be compared without guessing? | Output format |
| 4. Run paper decisions | Journal rows for simulated entries, skips, and exclusions. | Is the record complete, including decisions to do nothing? | AI trading journal |
| 5. Review risk | Size, exposure, drawdown, frequency, and pause checks. | Did the agent respect the same limits on winners and losers? | Risk workflow |
| 6. Review decisions | Human notes for rationale, rule fit, mistakes, and next action. | Is the next action supported by repeated evidence? | Decision review |
| 7. Version changes | A change log that names one prompt or rule adjustment. | Will the next sample be comparable to the prior sample? | Prompt versioning |
Setup: A trader wants an AI paper agent to review crypto breakouts. Before any sample starts, the trader writes the agent rules, chooses a small watchlist, defines the invalidation condition, and sets a simulated size cap.
Prompt: The prompt tells the agent to produce an entry, skip, or watch decision. Every output must include setup name, evidence, invalidation, paper risk, blocked condition when skipped, and next review question. The prompt also states that the agent does not execute live trades.
Sample: Over four weeks, the agent records paper entries, skips, and exits. Several skips are important because they show the agent avoiding unclear data and correlated exposure. Those skipped trades are kept in the journal, not deleted because they are not exciting.
Review: The team uses the evaluation checklist and human review checklist. The sample shows good setup discipline but weak exit notes. The next version changes only the exit-output field. The watchlist, risk cap, and setup rule stay fixed so the next paper sample remains comparable.
The workflow needs a defined market frame, a paper-trading account setup, a prompt version, an output format, and a review cadence. If the agent is still broad, start with the AI paper trading agent pillar and the paper trading account setup process.
For crypto-specific work, use crypto paper trading watchlist rules and market data review before treating a paper decision as useful evidence.
The workflow should produce a version label, a decision journal, a risk review, a human review note, and one next action. Good outputs are conservative: keep collecting paper evidence, tighten one field, reduce simulated size, or retire a weak workflow.
Weak outputs are usually too broad. Avoid claims that an agent is approved, ready, safe, autonomous, or profitable. Paper trading can improve review discipline, but it cannot prove live fills, slippage, liquidity, or future returns.
Choose a cadence before the sample begins. A weekly review can work when the agent produces many simulated decisions. A slower swing workflow may need a fixed number of entries and skips before the review is meaningful. The important rule is to avoid changing the workflow because the first few results feel good or bad. The sample should be closed by the plan, not by emotion.
Sample hygiene matters as much as prompt quality. Keep excluded records visible. If market data was stale, if the setup was unclear, or if the output missed a required field, keep the row and mark why it was excluded. Deleted rows make the agent look cleaner than it was and weaken the next review. The AI trading agent human review checklist can help a reviewer spot those gaps before changing the prompt.
Stop the workflow when the agent repeatedly breaks a hard risk limit, cannot produce structured output, depends on hindsight explanations, or turns every decision into an entry. Stopping does not have to mean deleting the idea. It may mean returning to the rules page, narrowing the market universe, requiring a stronger skip reason, or lowering simulated frequency.
Also stop if the content or product framing starts drifting toward live execution. A paper agent workflow should keep the boundary visible: simulated entries, human review, versioned changes, no custody, no live order routing, and no financial advice. That boundary is both a product trust requirement and an SEO quality requirement for finance-adjacent pages.
It is a simulated workflow that defines an AI trading agent, runs decisions in paper mode, records structured evidence, reviews risk, and changes one versioned rule at a time.
Define the persona, market universe, setup, invalidation rule, paper size cap, output format, review cadence, and stop conditions before collecting a sample.
No. Trading Boy does not execute live trades, hold funds, or provide financial advice. The workflow is for simulated practice and review.