Skill: totaleasy/crypto-self-learning (version 1.0.0)

--- 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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): ```bash python3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md --dry-run ``` ## 📋 View Trade History ```bash 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: ```bash 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`: ```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 --- *Skill by Total Easy Software - Learn from every trade* 🧠📈


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)