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: 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: 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 🧠📈

Crypto Self-Learning