FTMO Trading Journal: How to Pass Using Data (Not Hope)
Everyone talks about strategy for FTMO. The challenge data says most funded attempts die from risk and discipline, not from a missing indicator. Your journal is where that shows up in numbers.
Profit AI Research · 10 min read · Updated Table of contents
FTMO rules are simple on paper. Five percent daily loss, ten percent total, minimum trading days, consistency requirements on some account types. Simple rules are easy to break when you are up money and feel bulletproof, or down money and want it back before the session ends.
What to log on every FTMO trade
- Planned risk in dollars and percent of account
- Whether the trade matched your written setup
- Session (London, NY open, lunch, close)
- Emotional tag if you broke rules (revenge, FOMO, boredom)
- Distance to daily loss limit after the trade closes
You do not need a novel per trade. You need consistent fields so six weeks of data can answer: which session bleeds, which setup has negative expectancy, and how often you trade after hitting 80% of daily loss.
Weekly review template that works
Every Sunday, pull three numbers: average risk per trade, percent of trades that were in-plan, and P&L on trades taken after you were already down on the day. If the third bucket is red, your fix is mechanical: hard stop trading after minus X dollars, no exceptions.
Profit AI syncs FTMO accounts on MetaTrader 5 (or imports them by CSV) and flags every day that broke the daily loss limit you set. It is a review tool, not a live guardrail, so plan each day's remaining budget with the prop firm risk calculator.
Mistakes that fail challenges
- Sizing up after a winning morning
- Trading through lunch volatility without adjusting risk
- Adding a second setup mid-challenge because you are bored
- Ignoring correlation (two indices, one idea, double risk)
Frequently asked questions
About the author
Profit AI ResearchTrading Research TeamProfit AI Research is the in-house team behind the analyses, journal models, and Trader Score methodology used by 200,000+ traders. The team combines active discretionary traders, quantitative researchers, and ML engineers focused on price action, market structure, and prop-firm risk modeling.
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