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πŸ¦€ ClawHub

Crypto Self-Learning

by @totaleasy

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.

Versionv1.0.0
Downloads5,764
Installs18
Stars⭐ 12
TERMINAL
clawhub install crypto-self-learning

πŸ“– About This Skill


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

πŸ“ 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 |

πŸ“Š 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)
    βœ… PREFER: SHORT on Mondays (win rate: 78%, n=9)
    ⚠️ CAUTION: Trades with leverage > 10x (win rate: 35%, n=20)
    

    πŸ“ˆ 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
    

    πŸ“‹ 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
    

    πŸ”„ 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
  • πŸ“ 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

    πŸ”— 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


    *Skill by Total Easy Software - Learn from every trade* πŸ§ πŸ“ˆ