Rules first
Define the agent persona, watchlist, holding horizon, maximum paper position size, trade frequency, and invalidation rules before judging any result.
Use case
AI trading risk management
Use Trading Boy to keep AI paper-trading agents inside explicit risk limits, then review every simulated decision through sizing, drawdown, frequency, journal evidence, and rationale.
What this workflow is for
AI-assisted trading tools can create a false sense of precision. The useful question is not whether an agent sounds confident. The useful question is whether the agent is operating inside a written paper-trading system that can be reviewed. Trading Boy is designed around that paper-first review loop: define the rules, record the simulated decision, inspect the risk, and change only what the evidence supports.
Risk evidence
Review simulated size, maximum drawdown, repeated entries, correlated exposure, and whether alerts matched the intended process.
One change at a time
A good review produces one rule change, one sizing change, or a decision to collect more paper-trading samples before changing anything.
AI risk review workflow
| Risk area | What to review | Related Trading Boy page |
|---|---|---|
| Paper position size | Does the simulated position fit the risk budget, stop distance, and trade frequency? | Position size calculator |
| Trade entry quality | Was thesis, invalidation, time horizon, and rule fit written before the simulated entry? | Trade entry checklist |
| Drawdown | Is drawdown explainable from the strategy, or is the agent drifting into trades outside its frame? | Maximum drawdown calculator |
| Feedback loop | Did the review produce a concrete process update, or only a reaction to the latest result? | Trading feedback loop |
Example review
Scenario: An AI paper-trading agent records three simulated long entries in the same crypto sector within one session.
Risk question: Is this intended sector exposure, or did the agent violate its frequency and correlation rules?
Useful output: Reduce max simultaneous correlated paper exposure, tighten the watchlist, or require a stronger pre-trade note before the next alert can be counted as valid practice data.
Common mistakes
- Treating a confident AI explanation as risk control.
- Changing agent rules after one lucky or unlucky paper trade.
- Reviewing profit and loss without checking thesis quality.
- Ignoring trade frequency when drawdown still looks acceptable.
Best fit
This page is for traders who want AI assistance without outsourcing risk decisions. Trading Boy helps record and inspect a simulated process; the human operator still owns the rules, review standards, and live-capital boundary.
Related workflows
How to use it in Trading Boy
- Create or choose an agent persona with a clear market scope.
- Set a maximum paper position size and frequency rule before the agent records decisions.
- Use the journal to compare each simulated decision with the written risk frame.
- Review drawdown and correlated exposure before expanding the watchlist.
- Change one rule only when repeated evidence supports the change.
What this should not become
This workflow should not become a way to rationalize every AI-generated trade idea. If the agent cannot explain the setup inside the written risk frame, the decision should be treated as weak paper evidence even when the simulated outcome is positive.
It should also not become a live-capital approval process. Live trading requires execution, custody, venue, compliance, tax, and personal risk controls that paper trading does not prove.
Signals to monitor
Risk review should look for patterns that are easy to miss when each simulated trade is reviewed alone. A frequency spike can mean the agent is reacting to noise. Repeated correlated entries can mean the watchlist is too broad. A string of technically valid entries with weak thesis notes can mean the rule is easy to satisfy but hard to learn from.
Track these signals before they become rule changes. The safest paper-mode response is often to pause expansion, reduce simulated size, or require more complete pre-trade notes until the journal becomes clearer.
Review acceptance criteria
- The paper trade has a written thesis and invalidation.
- The simulated size matches the rule before the result is known.
- The agent decision fits the persona, market frame, and cadence.
- Drawdown and correlated exposure are reviewed together.
- The next action is specific: keep collecting, tighten one rule, reduce size, or pause the setup.
FAQ
Can AI trading risk management remove trading risk?
No. It can make a paper-trading process easier to review, but it cannot eliminate market risk, execution risk, model error, or human behavior risk.
What should I review before changing an agent rule?
Review the written thesis, invalidation, sample size, trade frequency, drawdown, and whether the decision matched the agent persona. If the evidence is thin, collect more paper data before changing the rule.
How many paper trades should be reviewed before changing risk rules?
Review enough comparable paper trades to separate one-off noise from repeated behavior. If the sample is small or mixed, keep collecting evidence instead of changing multiple rules at once.
Risk boundary
Trading Boy is paper-trading software. Risk pages are educational workflow tools, not investment advice, execution instructions, or portfolio recommendations.