Trading journal: your edge, on paper

What to record on every trade

The journal's job is to make your trading auditable. Record the objective facts — date, instrument, direction, size, entry, exit, stop — and two subjective fields that matter more than people expect: the setup (which rule triggered the trade) and an emotion score (1-5, how calm versus impulsive you felt).

R multiple is the single most useful derived number: (exit − entry) ÷ (entry − stop). It standardizes every trade into units of risk, which lets you compare setups, weeks and strategies fairly.

The weekly review routine

• Compute win rate, average R, and expectancy = (win rate × avg win R) − (loss rate × avg loss R).
• Group trades by setup and by time of day; compute expectancy per group.
• Find the leak: which group loses money consistently? Cut it, even if you love it.
• Answer the three questions: what did I follow the plan, what did I break, what does the data say.
• Write next week's one-line focus — a single behavior to change, not a manifesto.

The metrics that expose hidden problems

Win rate alone is meaningless: 60% wins with a −1.5R average loss is a losing system. What matters is expectancy per trade and the consistency of R across trades. A journal also exposes behavioral leaks — revenge trades after losses, oversized entries after wins, morning sloppiness — that no backtest can show you.

For systematic traders, the journal validates the human half of the loop: did you actually follow the validated rules? If your manual trades diverge from the plan, the strategy was never the problem.

How platforms automate the journal

Execution data — fills, times, sizes, R multiples — is best logged automatically. Platforms record backtest and paper trades for you, which removes the temptation to forget the losses. What automation cannot capture is your setup rationale and emotion score, so keep those two fields manual. The combined record gives you a complete, honest ledger of your process.