Trading Journal

Log every trade with full context (thesis, entry, exit, PnL, emotion, lesson). Generate weekly and monthly performance reports. Identify patterns in wins/los...

Security Scan

The skill is an instruction-only trading journal that writes/reads a local JSON file and references other trading skills; its requirements and behavior are coherent with its stated purpose.

Purpose & Capability

Name/description (trade logging, reports, pattern detection) match the instructions: reading/writing a journal JSON and generating reports. It does not request unrelated credentials, binaries, or system components.

Instruction Scope

Runtime instructions are narrowly scoped to logging trades, updating entries, and generating weekly/monthly reports from ~/.openclaw/workspace/trading/journal.json. It references other skills (skill-crypto-threshold-watcher, skill-catalyst-calendar, binance-pro, etc.) and a 'rulebook' without specifying how to access them; this is expected for integration but means the agent may seek context from those skills if available.

Install Mechanism

No install spec and no code files — instruction-only. Nothing is downloaded or written during install, which minimizes installation risk.

Credentials

The skill declares no environment variables, credentials, or config paths beyond a single local journal path. Requested access appears proportional to its logging and reporting purpose.

Persistence & Privilege

always:false and default model invocation mean it won't be force-included. It writes to a local file in the agent workspace (~/.openclaw/workspace/trading/journal.json), which is reasonable for a journal; it does not request system-wide changes or other skills' configs.

Assessment

This skill appears coherent and only writes/reads a local journal file in ~/.openclaw/workspace/trading/journal.json. Before installing: (1) confirm you are comfortable storing potentially sensitive trade history at that path (consider encrypting or backing up the file), (2) review any other skills it references (skill-crypto-threshold-watcher, binance-pro, etc.) before enabling autonomous invocation, since integrations could cause the agent to pull or push trade signals or confirmations, and (3) note the SKILL.md references a 'rulebook' and other systems without details — if you rely on strict rule enforcement, verify where those rules live and how the agent will access them. No network calls, external downloads, or credential requests are present in the instructions.

Systematic trade logging and performance review. Every trade logged = strategy gets smarter over time.

Trade Log Storage

Stored at: ~/.openclaw/workspace/trading/journal.json

Log a New Trade

Log trade to trading/journal.json:
- Symbol: GRASSUSDT
- Direction: LONG
- Entry: $0.36
- Entry date: 2026-03-13
- Size: $50 USDC
- Thesis: Price broke above $0.30 threshold + GTC keynote catalyst in 3 days + volume rising
- Catalyst: NVIDIA GTC 2026
- Signal source: skill-crypto-threshold-watcher
- Stop loss: $0.28 (22% below entry)
- Take profit: $0.50 (39% above entry)

Log Trade Exit

Update journal entry [trade-id] with exit:
- Exit price: $0.46
- Exit date: 2026-03-17
- Exit reason: Take profit not hit, manual exit (Aladdin decision)
- PnL: +$12.78 (+27.7%)
- Lesson: Entry thesis was correct. Exit was early — could have held to $0.50 TP.

Trade Record Schema

{
  "id": "trade-001",
  "symbol": "GRASSUSDT",
  "direction": "LONG",
  "status": "CLOSED",
  "entry_price": 0.36,
  "entry_date": "2026-03-13",
  "entry_size_usdc": 50,
  "thesis": "Price broke above $0.30 threshold + GTC keynote catalyst",
  "catalyst": "NVIDIA GTC 2026",
  "signal_source": "skill-crypto-threshold-watcher",
  "stop_loss": 0.28,
  "take_profit": 0.50,
  "exit_price": 0.46,
  "exit_date": "2026-03-17",
  "exit_reason": "Manual exit — pre-TP",
  "pnl_usdc": 12.78,
  "pnl_pct": 27.7,
  "lesson": "Exit was early. Hold to TP next time unless thesis breaks.",
  "emotion": "neutral",
  "thesis_correct": true,
  "execution_correct": false
}

Weekly Performance Report

Generate weekly trading report from trading/journal.json for the past 7 days.
Include: total PnL, win rate, avg win vs avg loss, best/worst trade, lessons summary, strategy adjustments.

Sample output:

📊 WEEKLY TRADING REPORT — Mar 10–17, 2026

Trades: 2 | Wins: 2 | Losses: 0 | Win Rate: 100%
Total PnL: +$18.43 (+23.4% on deployed capital)
Avg Win: +$9.21 | Avg Loss: N/A
Best Trade: GRASSUSDT +$12.78 (+27.7%)

LESSONS THIS WEEK:
- Thesis-correct but execution left early on GRASS — hold to TP when thesis intact
- Volume spike threshold ($100M) on FET called the move correctly

STRATEGY ADJUSTMENTS:
- Raise FET volume threshold to $120M (reduce false positives)
- Add trailing stop at +20% to avoid leaving gains on table

NEXT WEEK CATALYSTS:
- [from catalyst-calendar]

Monthly Strategy Review

Generate monthly strategy review from trading/journal.json.
Identify: which signal types worked, which failed, regime conditions, recommended rule updates.

Integration with Trading Pipeline

• Inputs from: skill-crypto-threshold-watcher (signal), skill-catalyst-calendar (context), binance-pro (execution confirmation)
• Outputs to: backtest-expert (validated signals for re-testing), quant-trading-system (parameter updates)
• Review cadence: Weekly debrief every Sunday 20:00 UTC

Emotion Tracking

Log emotion at trade entry: calm | excited | fearful | greedy | neutral

Over time, identify if emotional state correlates with win/loss rate. This is where most traders lose edge.

Rules Enforcement

Before logging a new trade, verify:

• Thesis stated in one sentence
• Stop-loss defined
• Take-profit defined
• Position size within rulebook limits
• Catalyst identified (or "none — pure technical")

If any field missing → trade is NOT valid per rulebook.