crypto-self-learning – OpenClaw Skill

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name: crypto-self-learning description: Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy. metadata: {"openclaw":{"emoji":"🧠","requires":{"bins":["jq","python3"]}}}

Crypto Self-Learning 🧠

AI-powered self-improvement system for crypto trading. Learn from every trade to increase accuracy over time.

🎯 Core Concept

Every trade is a lesson. This skill:

1. Logs every trade with full context
2. Analyzes patterns in wins vs losses
3. Generates rules from real data
4. Updates memory automatically

📝 Log a Trade

After EVERY trade (win or loss), log it:

python3 {baseDir}/scripts/log_trade.py \
  --symbol BTCUSDT \
  --direction LONG \
  --entry 78000 \
  --exit 79500 \
  --pnl_percent 1.92 \
  --leverage 5 \
  --reason "RSI oversold + support bounce" \
  --indicators '{"rsi": 28, "macd": "bullish_cross", "ma_position": "above_50"}' \
  --market_context '{"btc_trend": "up", "dxy": 104.5, "russell": "up", "day": "tuesday", "hour": 14}' \
  --result WIN \
  --notes "Clean setup, followed the plan"

Required Fields:

Field	Description	Example
--symbol	Trading pair	BTCUSDT
--direction	LONG or SHORT	LONG
--entry	Entry price	78000
--exit	Exit price	79500
--pnl_percent	Profit/Loss %	1.92 or -2.5
--result	WIN or LOSS	WIN

Optional but Recommended:

Field	Description
--leverage	Leverage used
--reason	Why you entered
--indicators	JSON with indicators at entry
--market_context	JSON with macro conditions
--notes	Post-trade observations

📊 Analyze Performance

Run analysis to discover patterns:

python3 {baseDir}/scripts/analyze.py

Outputs:

• Win rate by direction (LONG vs SHORT)
• Win rate by day of week
• Win rate by RSI ranges
• Win rate by leverage
• Best/worst setups identified
• Suggested rules

Analyze Specific Filters:

python3 {baseDir}/scripts/analyze.py --symbol BTCUSDT
python3 {baseDir}/scripts/analyze.py --direction LONG
python3 {baseDir}/scripts/analyze.py --min-trades 10

🧠 Generate Rules

Extract actionable rules from your trade history:

python3 {baseDir}/scripts/generate_rules.py

This analyzes patterns and outputs rules like:

🚫 AVOID: LONG when RSI > 70 (win rate: 23%, n=13)
✅ PREFER: SHORT on Mondays (win rate: 78%, n=9)
⚠️ CAUTION: Trades with leverage > 10x (win rate: 35%, n=20)

📈 Auto-Update Memory

Apply learned rules to agent memory:

python3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md

This appends a "## 🧠 Learned Rules" section with data-driven insights.

Dry Run (preview changes):

python3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md --dry-run

📋 View Trade History

python3 {baseDir}/scripts/log_trade.py --list
python3 {baseDir}/scripts/log_trade.py --list --last 10
python3 {baseDir}/scripts/log_trade.py --stats

🔄 Weekly Review

Run weekly to see progress:

python3 {baseDir}/scripts/weekly_review.py

Generates:

• This week's performance vs last week
• New patterns discovered
• Rules that worked/failed
• Recommendations for next week

📁 Data Storage

Trades are stored in {baseDir}/data/trades.json:

{
  "trades": [
    {
      "id": "uuid",
      "timestamp": "2026-02-02T13:00:00Z",
      "symbol": "BTCUSDT",
      "direction": "LONG",
      "entry": 78000,
      "exit": 79500,
      "pnl_percent": 1.92,
      "result": "WIN",
      "indicators": {...},
      "market_context": {...}
    }
  ]
}

🎯 Best Practices

1. Log EVERY trade - Wins AND losses
2. Be honest - Don't skip bad trades
3. Add context - More data = better patterns
4. Review weekly - Patterns emerge over time
5. Trust the data - If data says avoid something, AVOID IT

🔗 Integration with tess-cripto

Add to tess-cripto's workflow:

1. Before trade: Check rules in MEMORY.md
2. After trade: Log with full context
3. Weekly: Run analysis and update memory

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Skill by Total Easy Software - Learn from every trade 🧠📈