Agent setup
Use the agent-rule pages when the task is to define how a simulated trading agent should behave before any result exists.
Workflow directory
Trading workflows for paper-mode review
Use these workflows to turn simulated trading into a repeatable operating loop: write the agent rules, check the planned entry, review paper risk, record the exit, and feed one clear improvement back into the next sample.
Workflow sequence
| Step | Owner page | Review output |
|---|---|---|
| 1. Define the agent | AI trading agent rules | Persona, market scope, cadence, setup criteria, skip rules, and review standard. |
| 2. Check market data | Paper trading market data review process | Source, freshness, gaps, timeframe, liquidity assumption, and sample status. |
| 3. Prepare the decision | Pre-trade review | Thesis, invalidation, planned paper size, risk-reward, and reason to skip if unclear. |
| 4. Check the entry | Trade entry checklist | A consistent record before the outcome is known. |
| 5. Review risk | Risk review | Paper exposure, drawdown, related positions, frequency, and rule-fit notes. |
| 6. Close the loop | Post-trade review | Outcome classification, behavior tag, and one next process action. |
| 7. Improve carefully | Trading feedback loop | A narrow change, more sample collection, or a decision to retire the setup. |
Decision capture
Use pre-entry workflows when the trade needs clean market data, a written thesis, invalidation, risk plan, and skip condition before the outcome can bias the review.
Review and feedback
Use review workflows after entries and exits to separate clean losses from broken process, and to avoid rewriting rules from one small sample.
AI paper-agent workflow path
Use this path when a simulated AI trading agent needs a full setup, risk review, decision review, and human signoff loop. Each page owns a different part of the paper evidence so reviewers do not mix setup, output quality, risk behavior, and prompt changes.
| Sequence | Owner page | What it clarifies |
|---|---|---|
| 1. Build the loop | AI paper trading agent workflow | Connects rules, prompt, output format, paper decisions, risk review, decision review, and versioning. |
| 2. Review paper risk | Paper trading agent risk control workflow | Checks size, exposure, drawdown, skip discipline, and pause rules before outcome bias. |
| 3. Inspect decisions | AI trading agent decision review workflow | Reviews entries, exits, skips, blocked conditions, risk notes, and next actions row by row. |
| 4. Run the sample review | Simulated AI trading agent review process | Freezes the version, reviews the sample, and produces one conservative next action. |
| 5. Sign off the next action | AI trading agent human review checklist | Requires human evidence review before changing a prompt, risk rule, or sample plan. |
| 6. Journal workflow sequence | AI Trading Journal Workflow | Use it when AI output, paper trades, and human notes need to become one reviewable journal process. |
| 7. Daily review workflow | Daily Trading Journal Review | Use it at the end of each paper session to classify decisions while the context is still fresh. |
| 8. Weekly review workflow | Weekly Trading Journal Review | Use it after several paper sessions when there is enough evidence to decide whether to collect, tighten, pause, or revert. |
Example workflow pass
Before entry: The agent identifies a simulated crypto setup. The trader opens the pre-trade review, records the thesis, checks paper size with the position size calculator, and confirms the invalidation.
During review: The risk workflow checks related exposure and max drawdown before the simulated entry remains eligible for the sample.
After exit: The post-trade review tags rule fit separately from PnL, then the feedback loop chooses one action: keep collecting samples, tighten one rule, or reduce simulated size.
Why workflow hubs matter
Many SEO pages answer a definition and stop. Trading Boy needs workflows because the product value is the sequence: paper decision, risk context, journal record, review, and improvement. This directory gives crawlers and users a clear map of that sequence.
It also prevents search-intent overlap. Risk review owns exposure and drawdown questions. Pre-trade review owns setup readiness. Post-trade review owns outcome classification. The feedback loop owns rule changes.
Paper-first boundary
These workflows are for simulated practice and review. They do not execute live trades, provide financial advice, or turn alerts into buy or sell signals.
FAQ
What workflow should I use first?
Use AI trading agent rules first when you are setting up a system. Use pre-trade review first when the agent is already configured and a simulated entry is being considered.
Why separate pre-trade and post-trade pages?
The pre-trade page records the plan before outcome bias. The post-trade page reviews behavior after the outcome is known.
Are these workflows trading signals?
No. They are review structures for paper trading and should not be interpreted as live trading instructions.