dvcrn 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:
- Logs every trade with full context
- Analyzes patterns in wins vs losses
- Generates rules from real data
- 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
- Log EVERY trade - Wins AND losses
- Be honest - Don't skip bad trades
- Add context - More data = better patterns
- Review weekly - Patterns emerge over time
- Trust the data - If data says avoid something, AVOID IT
🔗 Integration with tess-cripto
Add to tess-cripto's workflow:
- Before trade: Check rules in MEMORY.md
- After trade: Log with full context
- Weekly: Run analysis and update memory
Skill by Total Easy Software - Learn from every trade 🧠📈