AI Trade Review Simulator

How AI Trade Review Analyzes Trade Quality

Tragene Journal uses artificial intelligence to evaluate every logged trade against objective risk management rules. Rather than scoring trades solely on profit or loss, the AI engine evaluates risk discipline, strategy compliance, and risk-to-reward execution.

This simulator demonstrates how automated AI scoring rates trade execution. A disciplined losing trade that respects stop loss rules scores higher than a lucky winning trade executed with impulse over-leveraging.

AI Execution Scoring Criteria

Trade score (out of 100) is calculated based on three core performance pillars:

• Risk Control (40 Points): Risk percentage kept within safe limits (under 2% to 3% of account equity).
• Strategy Adherence (30 Points): Trade executed in alignment with documented rules rather than FOMO or revenge trading.
• R:R Quality (30 Points): Realized payout matches or exceeds planned risk-to-reward ratio.

Step-by-Step Worked Example

Assume you log a trade on a $10,000 account risking $200 (2% risk) with a planned R:R of 2.0, achieving +$400 realized profit while following your trading plan.

Total Execution Score: 100/100 (Grade A+ Execution). The AI flags this trade as high quality.

Common Mistakes Traders Make

• Judging Execution Quality solely by PnL: Lucky winning trades with bad risk habits create false confidence, leading to severe future losses.
• Ignoring Risk Percentage Limits: Risking 10% on a single trade incurs severe AI penalty deductions regardless of trade outcome.
• Failing to Log Trade Context: Omission of trade notes prevents AI pattern detection across strategy setups.

Frequently Asked Questions

Can a losing trade receive a high AI score?
Yes. A trade that follows your rules, maintains 1% risk, and closes at technical stop loss receives a high score (85-95) because execution was disciplined.

How does full Tragene Journal AI review work?
In the full application, OpenAI analyzes your trade screenshots, entry notes, asset market conditions, and execution history to provide personalized coaching feedback.