What a Trading Journal Is (and Isn’t)
A trade log is history. A journal is a feedback loop.
A trade log captures what happened: entries, exits, and PnL. A journal captures what you can change next week: decision quality, risk consistency, and the conditions that trigger mistakes.
**The job of a journal** - Preserve intent: why you took the trade. - Preserve context: what the market looked like when you decided. - Preserve process: what rules you followed or broke. - Make review fast: so you actually do it weekly.
**The two failure modes** - Too little structure: everything becomes a diary and nothing is measurable. - Too much structure: logging becomes work and you stop doing it.
The right journal is “minimal, consistent, reviewable.”
Key Points
- •A journal exists to change next week’s decisions, not to archive screenshots.
- •Your schema must be small enough to sustain, even in busy weeks.
- •Review cadence matters more than perfect detail.
The Minimal Schema (What to Track Every Time)
Start with fields you can sort and compare.
Core trade data: - Venue (exchange, account/subaccount) - Instrument (symbol), direction, size, leverage (if used) - Entry, exit, fees, funding (if applicable) - Planned stop and actual stop behavior (held, moved, removed)
Decision tags (one layer deep): - Setup type (your label) - Entry quality (A/B/C or “in plan” vs “rule drift”) - State tag (tilt, FOMO, fatigue, calm)
Context tags (small and consistent): - Session window (your block), volatility regime (simple: low/normal/high) - Reason for exit (target, stop, time stop, discretionary)
If you track these consistently, you can answer the only questions that matter: “What prints expectancy?” and “What drains it?”
Key Points
- •Track costs (fees/funding) and stop behavior, not just entry and exit.
- •Use 3-5 tags you can keep forever, not 30 tags you will abandon.
- •A simple A/B/C quality tag beats long notes you never review.
A Weekly Review Loop You Can Keep
**Weekly beats perfect.** A journal that gets reviewed monthly is mostly a scrapbook.
Run this loop once per week: - Sort by state tag (tilt/FOMO/fatigue) and identify your highest-cost condition. - Sort by setup label and identify one edge worth repeating. - Pick one leak to cut with a constraint (not a promise).
Constraint examples: - “After any rule drift, 20-minute cooldown.” - “After trade 4, stop.” - “No re-entry without a new level or a new condition.”
Output of a good review is short: - Keep one edge. - Cut one leak. - Commit one change for next week.
Key Points
- •A review loop produces one constraint, not 15 insights.
- •Sort by behavior and condition, not by coin.
- •Make next week’s change enforceable.
Tooling: Spreadsheet vs Dedicated App vs AI
Pick tools based on volume and review habit.
Spreadsheets: - Great if you trade infrequently and like full control. - Risk: manual logging gets skipped when you are busy or emotional.
Dedicated journaling apps: - Great if you want structured imports and standard metrics. - Risk: dashboards can become entertainment instead of change.
AI-assisted review: - Great when you generate enough trades that patterns hide in noise. - Risk: do not outsource judgment; use AI to surface candidates, then verify with your own review.
Whatever you choose, the goal is the same: make logging low-friction and reviewing unavoidable.
Key Points
- •Choose the simplest tool that you will review weekly.
- •If you do not review, better tools do not help.
- •Automation is valuable when it protects the habit, not when it adds complexity.