Crypto Self-Learning

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* 🧠📈