Trading Journals

A structured journal with AI psychology analysis that goes beyond entries, exits, and P&L to identify behavioral and emotional patterns over time. Every entry captures pre-trade conviction, emotional state, and rule adherence alongside standard study data. The monthly psychology report organizes observations for reflection; it does not prescribe a trade or guarantee an improvement.

What this page covers

• How to set up and structure your trade logging workflow
• What the AI psychology analysis looks for in your entries
• How the weekly review system summarizes behavioral patterns for reflection
• How journals feed into your AI Coach and Trading Brain

Why Journaling Matters More Than Signals

Many traders focus on finding more signals. Journaling instead helps you examine how you make and review decisions. The gap between knowing a concept and applying it consistently can be examined through a written record.

Hanzo AI Journals are not a spreadsheet. Each entry goes through AI analysis that detects behavioral patterns — revenge trading, FOMO, overconfidence, fear-based exits — that don't show up in your P&L numbers but directly explain them.

Logging a Trade

A journal entry takes 2–4 minutes to complete. Every field is optional except pair and direction, but the more you fill in, the more the AI analysis can detect.

1. Basic trade data

   Pair, direction (long/short), entry price, exit price or current status (open), position size (lots or % of account), and result (win/loss/breakeven). This is the quantitative foundation of the entry.

2. Pre-trade reasoning

   A free-text field for why you took the trade. Write what you were thinking at the moment of entry — not a post-hoc rationalization. 'Looked like a good setup' is less useful than 'Price swept the Asian session high and reversed, I entered on the 1H engulfing close at the 0.618 retracement.'

3. Emotional state

   Select one: Calm & Focused / Slightly Anxious / FOMO / Revenge / Overconfident / Bored. This gives the AI context for reflection; it does not predict a trade outcome.

4. Rule adherence

   Did you follow your trading rules? Yes / Partially / No. If partially or no, a follow-up text field asks which rule was broken and why.

5. Post-trade notes

   Optional — what happened, what you noticed after closing, what you'd do differently. These notes are read by the AI in the context of the full trade timeline.

AI Psychology Analysis

Every time you submit a journal entry, the AI analyzes it against your Trading Brain profile and all previous entries. The analysis looks for:

• Revenge Trading

  Loss → rapid re-entry within the same session, often in the same pair, with equal or larger size. Detected by time-between-trade analysis and size comparison.

• FOMO Entries

  Entry after a significant move has already occurred, often with 'I missed the initial move but chased' language in reasoning text. Higher loss rate in data.

• Overconfidence

  Position size significantly above your stated risk-per-trade rule, typically following a winning streak. Detected by comparing size to your historical average after wins.

• Fear-Based Exits

  Closing profitable trades before TP level, often with 'took partial profits to be safe' or 'didn't trust the move' reasoning. Identified by exit-vs-TP distance analysis.

• Rule Violations

  Cross-references your 'Rule adherence' field against your stated trading rules from onboarding. Tracks which specific rule you break most frequently over time.

• Session Patterns

  Compares recorded entries across session labels when enough data is available. Differences are descriptive, sample-dependent observations and do not establish that a session is better.

The Weekly Review

Every Sunday at 08:00 UTC, Hanzo AI generates your weekly review. This is a synthesized analysis of all entries from the past 7 days. The review includes:

• Best trade of the week — what you did right, and why it worked
• A difficult entry or exit from the week — the assumption and behavioral pattern behind it
• Outcome distribution compared with prior entries — for reflection, not forecasting
• Most frequently broken rule this week
• Emotional state distribution — how often each state appeared alongside different recorded outcomes
• One reflection prompt for the following week (e.g. 'Review why your recorded GBPUSD outcomes differed between the New York and London samples')

The Journal–Coach Feedback Loop

Journals and the AI Coach are not independent systems — they form a closed feedback loop. Understanding the loop helps you optimize both:

Exporting and Reviewing Performance Data

Beyond the in-app views, your full trade journal can be exported for external analysis or sharing with a trading mentor. Export options are in Journal → Settings → Export:

• CSV export — all trades with all fields, compatible with Excel, Google Sheets, or Notion databases
• PDF weekly report — formatted summary of the AI weekly review for printing or sharing
• JSON full export — complete structured data dump including behavioral flags and AI analysis notes

One possible external use of exported data is a personal statistics dashboard in Google Sheets or Notion, where you can compare Hanzo AI records with broker data.

Strategy Journals

Separate from trade logs, Strategy Journals let you document entire trading strategies as living documents:

• Strategy name, description, and edge thesis
• Entry rules, exit rules, and invalidation conditions
• Risk parameters — position sizing formula, max daily loss, max concurrent trades
• Outcome tracking — recorded results, risk ratios, and peak-to-trough changes over time
• Weekly performance notes — what's working, what needs adjustment
• Version history — track how your rules evolve and whether changes improved results